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From Expert Finding to Entity Search on the Web

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From Expert Finding to Entity Search on the Web

From Expert Finding to Entity Search on the Web
Full-day Tutorial at ECIR 2012
Gianluca Demartini, Peter Mika, Thanh Tran, Arjen P. de Vries

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  • When the competition is copying you, you know that you are doing something right.
  • Facebook invited, but continues to pursue OGP
  • This presentation will focus mainly on extraction information from textual data
  • This presentation will focus mainly on extraction information from textual data
  • I should also say that the state of the art entity resolution approaches use some form of collective resolutionDifferent algorithms (relational learning, jointinferencing, similarityflooding)[adapted from Bhattacharya and Getoor 2007]:Iteratively select entities:Prior pair-wise evaluation of candidate entities;While top available candidate is good enough:Select top candidate from queue;Update evaluations of available candidates;Evaluate candidates by: Similarity of entity description and documentRelatednessto other selected candidates
  • Amajor requirement of these methods is that the schema describing the data at hand as well as the properties of its individual attributes are know a priori. Inevitably, though, this fundamental assumption is broken by the inherent characteristics of heterogeneous informa- tion spaces (i.e., loose schema binding, noise, missing or incon- sistent values, as well as an unprecedented level of heterogeneity), turning them inapplicable.
  • It contains more than 1 million entities and 5 million facts and achieves an ac-curacy of about 95%.Each Wikipedia page title is a candidate to become anentity in YAGO, and the Wikipedia categories of that page become its containing classes. Wikipedia categories are organized in a directed acyclic graph, whichyields a hierarchy of categories.
  • Why did you use the features
  • Explain measures
  • Hybrid data graph with content nodes
  • Content matching: not only one single term but several query terms (predicate)  not only one matching operations but also combining results of matches for parts of the query produced by several operationsInstead of online matching  index is needed for managing last amount of data and fast access to matches Matching can be decomposed into two operations: matching and combine Join : dictionary posting lists  intersect posting lists
  • Assume given structure patterns in the query, i.e. structured queries, e.g. graph patterns (a popular fragment of widely used languages SQL and SPARQL)Blocking: iterator-based approachesNon-blocking: good for streaming, good we cannot wait for some parts of the results to be completely worked-offLink data: cannot wait for sources, (some are slower then other) thus better to push data into query processing as the they come instead of pulling data and wait (busy waiting)This structure matching based on given structure patterns demonstrate the idea behind keyword  however query structure provide guidances as to what structure elements in the data are relevant, given keywords, all possible structured have to be explored, other kinds of join
  • Followed from the excurse what about semantic features? Not directly incorporated into ranking models yet but only to generate candidate matches during the matching stepNot straightforward when using stastitical ranking models
  • Proximity-based ranking employ minimal distance heuristics to maximize structural compactness of results When JRT is more compact, it is assumed to be more meaningful and relevant Intuition: keyword specified by the users are closely related and thus should be connected over relatively short paths I.e. Compactness measured in terms of the length of paths between nodes, i.e. The proximity The larger the length of paths, the less relevant is the overall resultThe proximity of two keywords defined based on proximity of elements matches these keywordsNi and nj are nodes in the graph sim(ni,nj) denotes the compactness between two any nodessim(ki,kj) denotes the compactness between two keywords (taking account the compactness of all pairs of nodes matching the two keywords), i.e. Cki denotes the set of all nodes that match kiOverall score of a JRT is an aggregation on the score of its n is a keyword search result  matching the query keywords

From Expert Finding to Entity Search on the Web From Expert Finding to Entity Search on the Web Presentation Transcript

  • From Expert Finding to Entity Search on the Web Full-day Tutorial at ECIR 2012 1st April 2012 Gianluca Demartini, Peter Mika, Thanh Tran, Arjen P. de Vries http://diuf.unifr.ch/main/xi/EntitySearchTutorial
  • Presenters • Dr. Gianluca Demartini – eXascale Infolab, University of Fribourg, Switzerland – Research Interests: • Entity Search • IR Evaluation • Semantic Web 01 Apr 2012 2 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web
  • Presenters • Dr. Peter Mika – Senior Researcher, Yahoo! Research, Barcelona – Semantic Search group at Yahoo! Barcelona – Semantic Search, Web Object Retrieval, Natural Language Processing 01 Apr 2012 3 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web
  • Presenters • Dr. Thanh Tran – (Institut AIFB, Universität Karlsruhe, Germany) – Semantic Search group at AIFB – Semantic Search, Semantic Data Management, Linked Data Query Processing 01 Apr 2012 4 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web
  • Presenters • Prof.dr.ir. Arjen P. de Vries – Interactive Information Access research group, Centrum Wiskunde & Informatica (CWI); Delft University of Technology; Spinque – Research interest: the intersection between information retrieval and databases 01 Apr 2012 5Van Rijsbergen, 1979 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web
  • Entity • An entity is a “proper noun”, “something that is referred to” Outcome of “definition” discussion reported in SIGIR Workshop Report on The First International Workshop on Entity-Oriented Search (EOS), Krisztian Balog, Arjen P. de Vries, Pavel Serdyukov, Ji-Rong Wen, ACM SIGIR Forum, Vol. 45, No. 2, Dec. 2011, pp 43-50 01 Apr 2012 6 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web
  • Entity Search • All those search tasks that aim at retrieving as answer to a user query an entity instead of a document – People, Countries, Movies, Restaurants, etc. 01 Apr 2012 7 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web
  • Motivation • Information is entity-centric • Search for information is often conducted around entities (Query log analysis) – Many queries (50%) search for specific entities instead of documents [Kumar&Tomkins09] • Traditional search retrieves a list of blue links • Novel web experiences may be designed around entities instead 01 Apr 2012 8 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web
  • Here, forone search query “Nicole Kidman”, variousentities make up the answer: bio photos movies trivia quotes (...) 01 Apr 2012 9
  • Entity-centric Applications • Enterprise applications • News portals • Movie portals • Product reviews • Search Engines 01 Apr 2012 10 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web
  • Entities in SERP 01 Apr 2012 11 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web
  • Entities in SERP 01 Apr 2012 12 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web
  • Entity Search: The Pipeline • Entity Representation (DB/SW) • Entity Extraction (NLP) • Entity Linking and De-duplication (DB/SW) • Entity Storage and Indexing (DB/SW) • Entity Search and Ranking (IR) • Result presentation (HCI) • Evaluation (HCI/IR) 01 Apr 2012 13 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web
  • Outline • FULL DAY Tutorial (sorry, this is not a joke :) • Morning – Data (Peter) – Data Management (Thanh) • Afternoon – Search and Ranking (Gianluca &Thanh) – Evaluation (Arjen) 01 Apr 2012 14 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web
  • Morning • Data – Structured vs. Unstructured: – Entity Profiles: data models, entity identifiers, standards – Datasets (Desktop, Enterprise, Wikipedia, Web, RDF) • Data Management – Entity Extraction – Entity de-duplication / data fusion – Entity storage & indexing 01 Apr 2012 15 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web
  • Afternoon • Search and Ranking – Expert Finding models – Entity Ranking in Wikipedia – Web Entity Retrieval – Entity Search over Structured Data – Relational Entity Search over Structured Data • Evaluation – TREC Enterprise – INEX Entity Ranking – TREC Entity – SemSearch, Ad-hoc Object Retrieval 01 Apr 2012 16 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web
  • Data for Semantic Search
  • Data • Web data – Information Extraction – Semantic Web • Non-web data – Enterprise data – Desktop data – Email – ... 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 18
  • Data on the Web • Most web pages on the Web are generated from structured data – Data is stored in relational databases (typically) – Queried through web forms – Presented as tables or simply as unstructured text • The structure and semantics (meaning) of the data is not directly accessible to search engines • Two solutions – Information Extraction [see Part 2] – Relying on publishers to use Semantic Web formats • Linked Data vs. metadata in HTML 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 19
  • Semantic Web • Sharing structured data across the Web – Standard graph-based data model • RDF – A number of syntaxes (file formats) • RDF/XML, RDFa – Powerful, logic-based schema languages • OWL, RIF – Query languages and protocols • HTTP, SPARQL 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 20
  • Resource Description Framework (RDF) • Each resource (thing, entity) is identified by a URI or otherwise it’s a blank node – URIs are globally unique • Data is broken down into individual facts – Triples of (subject, predicate, object) • A set of triples is published together in an RDF document example:roi “Roi Blanco” name type foaf:Person RDF document 01 Apr 2012 21
  • An RDF graph peter#123 “Peter Mika” name foaf:Person sameAs peter#456 worksWith roi#234 “roi@yahoo-inc.com” email type type 01 Apr 2012 22
  • OWL, the Web Ontology Language • The schema language for the Semantic Web – Classes, properties and restrictions on their usage – Allows validation and inference • Schema is also data – Published just like any other RDF document – Queries can refer to both schema and data • e.g. taxonomy expansion: retrieve instances of a class and instances of all subclasses 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 23
  • Publishing RDF and OWL • Linked Data – Data published as RDF documents linked to other RDF documents • Typically RDF/XML or Turtle • Keep an eye on JSON-LD – Community effort to (re-)publish open datasets • Embedded metadata – RDFa, microdata, microformats annotations inside webpages – Recommended for site owners by Yahoo, Google, Facebook • SPARQL endpoints – Triple stores (RDF databases) that can be queried through the web 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 24
  • Linked Data • Interlinked datasets on the Web – Often data from existing databases or APIs • The four rules of Linked Data: – Use URIs to identify things. – Use HTTP URIs so that these things can be referred to and accessed by people and crawlers. – Use standard formats such as RDF to provide useful information about the thing when its URI is accessed – Include links to other datasets • Most importantly: links to entities in other datasets that describe the same entity 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 25
  • Peter’s homepage Yahoo! Friend-of-a-Friend ontology Linked Data peter#123 “Peter Mika” name foaf:Person sameAs peter#456 worksWith roi#234 “roi@yahoo-inc.com” email type type 01 Apr 2012 26
  • Linked (Open) Data = LOD • Advantages: – No change to the publishing of the HTML documents – Data can be published by third party (e.g. Dbpedia) • Disadvantages: – Web servers need to be configured to properly handle URIs that identify concepts instead of documents – Not favored by search engines • Lack of use cases • Crawling needs to be changed • Authority is difficult to determine • Tools – Triple stores (Virtuoso, Oracle etc.) and front-ends (Pubby) – RDB-to-RDF mappers (e.g. D2RQ, Triplify) – Validators (Vapour) – Linked Data browsers (many) 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 27
  • Linked Data community • Community effort to (re)publish open datasets as Linked Data – In particular, scientific and government data – see linkeddata.org and ckan.org for developer information and datasets
  • Linked Data in practice • Fetching data dumps – See catalogs such as thedatahub.org, linkeddata.org • Crawling Linked Data – Similar to HTML crawling, but the the crawler needs to parse RDF/XML (and others) to extract URIs to be crawled – Semantic Sitemap/VOID descriptions – Existing crawls • Billion Triples Challenge (2009-2011) datasets • LOD cache • Querying SPARQL endpoints – See catalogs such as thedatahub.org, linkeddata.org – Semantic Sitemap/VOID descriptions 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 29
  • Datasets • Broad coverage datasets are linking hubs – Dbpedia – Freebase – Starting in 2012: Wikidata • Domain-specific datasets form clusters – Biology – Government – Library – Entertainment – … 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 30
  • Wikipedia 31
  • Dbpedia Using the Dbpedia ontology Raw data 32
  • Metadata in HTML • 1995: HTML meta tags • 1998: RDF/XML • 2003: Web 2.0 – Tagging – Microformats – Metadata in Wikipedia – Machine tags in Flickr • 2005: eRDF • 2008: RDFa 1.0 • 2011: RDFa 1.1 • 2012: Microdata 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 33
  • HTML meta tags <HTML> <HEAD profile="http://dublincore.org/documents/dcq-html/"> <META name="DC.author" content="Peter Mika"> <LINK rel="DC.rights copyright" href="http://www.example.org/rights.html" /> <LINK rel="meta" type="application/rdf+xml" title="FOAF" href= "http://www.cs.vu.nl/~pmika/foaf.rdf"> </HEAD> … </HTML> 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 34
  • Microformats (μf) • Agreements on the way to encode certain kinds metadata in HTML – Reuse of semantic-bearing HTML elements – Based on existing standards – Minimality • Microformats exist for a limited set of objects – hCard (persons and organizations) – hCalendar (events) – hResume – hProduct – hRecipe • Varying degrees of support and stability – hCard and rel-tag are widely supported • Community centered around microformats.org – Specifications and discussions are hosted there 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 35
  • Microformats: limitations • No shared syntax – Each microformat has a separate syntax tailored to the vocabulary • No formal schemas – Limited reuse, extensibility of schemas – Unclear which combinations are allowed • No datatypes • No namespaces, unique identifiers (URIs) – no interlinking – mapping between instances is required • Always appears in the HTML <body> 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 36
  • Example: the hCard microformat <cite class="vcard"> <a class="fn url" rel="friend colleague met” href="http://meyerweb.com/"> Eric Meyer</a></cite> wrote a post (<cite> <a href="http://meyerweb.com/eric/thoughts/2005/12/16/tax-relief/"> Tax Relief</a></cite>) about an unintentionally humorous letter he received from the <span class="vcard”><a class="fn org url" href="http://irs.gov/"> Internal Revenue Service</a> </span>. <div class="vcard"> <a class="email fn" href="mailto:jfriday@host.com">Joe Friday</a> <div class="tel">+1-919-555-7878</div> <div class="title">Area Administrator, Assistant</div> </div> 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 37
  • RDFa • W3C standard for embedding RDF data in HTML documents – A set of new HTML attributes to be used in head or body – A specification of how to extract the data from these attributes • RDFa is just a syntax, you have to choose a vocabulary separately • RDFa 1.0 is a W3C Recommendation since October, 2008 – RDFa Primer • RDFa 1.1 currently under standardization – RDFa Core&RDFa Lite Working Draft as of January 31, 2012 – Updated version of the RDFa Primer • RDFa API for accessing RDFa data in a webpage in the browser from JavaScript – Currently Working Draft (April 19, 2011) 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 38
  • RDFa 1.1 • Changes – New vocabattribute to define the default namespace for the document or subtree – Syntax changes for ease of use – RDFaLite profile • RDFa 1.1 is backward compatible with RDFa 1.0 – RDFa 1.1 is recommended if you want to use HTML5 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 39
  • Microdata • Currently under standardization at the W3C – Working Draft (May 25, 2011) • Microdata vs. RDFa – Microdata is simpler to author – Lacking some extension features such as co-typing • HTML5 also has a number of “semantic” elements such as <time>, <video>, <article>, <section>… 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 40
  • Microdata example <div itemscope itemid=“http://www.yahoo.com/resource/person”> <p>My name is <span itemprop="name">Neil</span>.</p> <p>My band is called <span itemprop="band">Four Parts Water</span>. I was born on <time itemprop="birthday" datetime="2009-05-10">May 10th 2009</time>. <img itemprop="image" src=”me.png" alt=”me”> </p> </div 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 41
  • Example: Facebook’s Like and the Open Graph Protocol • The ‘Like’ button provides publishers with a way to promote their content on Facebook and build communities – Shows up in profiles and news feed – Site owners can later reach users who have liked an object – Facebook Graph API allows 3rd party developers to access the data • Open Graph Protocol is an RDFa-based format that allows to describe the object that the user ‘Likes’ 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 42
  • Example: Facebook’s Open Graph Protocol • RDF vocabulary to be used in conjunction with RDFa – Simplify the work of developers by restricting the freedom in RDFa • Activities, Businesses, Groups, Organizations, People, Places, Products and Entertainment • Only HTML <head> accepted <html xmlns:og="http://opengraphprotocol.org/schema/"> <head> <title>The Rock (1996)</title> <meta property="og:title" content="The Rock" /> <meta property="og:type" content="movie" /> <meta property="og:url" content="http://www.imdb.com/title/tt0117500/" /> <meta property="og:image" content="http://ia.media- imdb.com/images/rock.jpg" /> … </head> ... 43
  • Fragmentation of web markup • Multiple schemas – Yahoo!’s SearchMonkey – June, 2008 – Google announces Rich Snippets – June, 2009 • Faceted search for recipes – Feb, 2011 – Facebook’s Open Graph Protocol – April, 2010 • ‘Verbs’ added to OGP – September, 2010 – Bing tiles – Feb, 2011 • Different syntax – Microformats, RDFa, microdata 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 44
  • Schema.org • Agreement between Bing, Google, and Yahoo on what markup webmasters should use – Help adoption by reducing fragmentation – Pre-competitive: each party will continue to build competing products independently • Schema.org covers areas of interest to all three parties – Business listings (local), creative works (video), recipes, reviews – Expected to open up also to external contributions for non-core areas 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 45
  • Example: schema.org 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 46
  • Embedded metadata in practice • 5-10% of webpages contain some explicit metadata – Statistics computed from commoncrawl.org give different results • Schema.org helped resolve fragmentation – Except Facebook • RDFa and microdata likely to co-exist 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 47
  • Non-web Data
  • Enterprise Data • Unstructured – Technical reports, Product Specification, etc. • Semi-structured – E-mail, Spreadsheets • Structured – Databases, Repositories 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 49
  • Enterprise Search • Challenges – Deal with data and format diversity – Index/search diverse datasets • Vertical vs Centralized systems – Deal with security and access control – Specific informational needs • Expert Finding • Writing an overview 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 50
  • Desktop Data • Textual – Unstructured • Txt documents – Semi-Structured • E-mails, PDFs, Word files, etc. contain much metadata • Multi-media – Pictures, Videos, Audio – Metadata 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 51
  • Desktop Search • IR techniques over unstructured data • Exploit – the structure and metadata available – user activity logs (browsing history, file access patterns, etc.) • Beagle++ – Hybrid search over inverted index and RDF store – E-mail context and attachments – Folder structure – Browser cache Enrico Minack, RalucaPaiu, StefaniaCostache, Gianluca Demartini, JulienGaugaz, EkateriniIoannou, Paul- AlexandruChirita, Wolfgang Nejdl: Leveraging personal metadata for Desktop search: The Beagle++ system. J. Web Sem. 8(1): 37-54 (2010) 01 Apr 2012
  • Tutorial Outline • Morning – Data (Peter) – Data Management (Thanh) • Afternoon – Search and Ranking (Gianluca &Thanh) – Evaluation (Arjen) 01 Apr 2012 53 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web
  • Data Management
  • Agenda • Knowledge/EntityExtraction • Entity Linking • Entity De-duplication • Entity Storage &Indexing … veryhigh-level overviewofproblemsandsolutions! … seetutorials on thespecificproblems! 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 55
  • Knowledge/Entity Extraction Source: Tadej Steiner from Jozef Stefan Institute, Ljubljana, Slovenia
  • Problem definition • Knowledge extraction: – Extracting information from data and – Adding it to a knowledge base 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 57
  • Problem definition • Information extraction + knowledge acquisition (textual) data extracted infomation knowledge base Information extraction Knowledge acquisition 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 58
  • Information extraction • From the advent of the WWW, there are huge quantities of unstructured textual data, where manual information extraction would be infeasible • How to extract information from text automatically with human-comparable quality 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 59
  • Information extraction: early solutions • Match manually defined patterns against text • Example: – Patterns like “Pay ? from ? in favor of ?” – ATRANS (1986) inter-banking message exchange 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 60
  • Knowledge acquisition • How to transform a world (or domain) model from existing forms into a computer-friendly form – Conceptualknowledge (classes, rules, T-Box) VS. – Instance information (instancedata, resourcedescriptions, datarecords, A-Box) • Use cases for knowledge bases: – Answering complex entitysearchqueries / questions in general: • “which scientists are also politicians?” 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 61
  • Knowledge acquisition • Constructing a knowledge base is expensive – The Cyc KB was mostly manually constructed over the last 20 years • Coupling information extraction and knowledge acquistion lets us construct a knowledge base with no or little human effort 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 62
  • Challenges • Human effort: – Defining (domain-specificand domain- independent) extraction patterns – Especially, in case of bootstrapping approaches: • Specifyingrelations • Construction of training examples – Maintaining knowledge base consistency 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 63
  • Related research areas • Natural language processing • Information extraction • Machine learning • Knowledge management  Knowledge extraction toolscanbecomparedbythese perspectives 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 64
  • General knowledgeextractiontools • WebKB • TextRunner • Cyc • SOFIE withthe corresponding YAGO knowledge base • Read The Web • EntityCube 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 65
  • Natural language processing • Employed by most modern approaches • Part-of-speech tagging • Noun phrase chunking, used for entity extraction • Abstraction of text – From: “Slovenia borders Italy” – To:“noun – verb – noun” 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 66
  • Information extraction: entities • Entity extraction / NamedEntity Recognition – “Slovenia borders Italy” • Entity resolution – “Apple released a new Mac”. – From “Apple”, “Mac” – To Apple_Inc., Macintosh_(computer) • Entity classification – Into a set of predefined categories of interest – Person, location, organization, date/time, e-mail address, phone number, etc. – E.g. <“Slovenia”, type, Country> 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 67
  • Some NER tools • Java – Stanford Named Entity Recognizer • http://nlp.stanford.edu/software/CRF-NER.shtml – GATE • http://gate.ac.uk/ – LingPipe • http://alias-i.com/lingpipe/ • C – SuperSense Tagger • http://sourceforge.net/projects/supersensetag/ • Python – NLTK • http://www.nltk.org 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 68
  • NER – list lookup • Entities stored in lists (gazetteers) – E.g., Countries and cities • Plus: Simple, fast, cross-language • Minus: list update, name variants (UPF, UniversitatPompeuFabra), ambiguity 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 69
  • List lookup – ambiguities • Term level – E.g. capitalized words: [All American Bank] vs. All [State Police] • Structure level – “*Dolce and Gabbana+” vs “*Microsoft+ and *Yahoo!+” • Type level – John Smith (organization vs. person) – May (person vs. date vs. verb) – Washington (person vs. location) – 2015 (date vs. time)  Gazetteers not end solution but sources of background knowledge 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 70
  • NER methods • Rule Based – Regular expressions, e.g. capitalized word + {street, boulevard, avenue} indicates location – Engineered vs. learned rules • NER can be formulated as classification tasks – NE extraction: assign word mentions to tags (B beginning of an entity, I continues the entity, O word outside the entity) – NE classification: assign entity mentions to categories (Person, Organization, etc.) – Use ML methods for classification: Decision trees, SVM, AdaBoost – Standard classification assumes cases are disconnected (i.i.d) • Probabilistic sequence models: HMM, CRF – Each token in a sequence is assigned a label – Labels of tokens are dependent on the labels of other tokens in the sequence particularly their neighbors (not i.i.d). 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 71
  • Naïve Bayes Classification • Determine category of xk by computing for each yi • Priors P(Y=yi) and conditionals P(X=xk | Y=yi) estimated from data (via MLE), – E.g. If ni of the examples in D are in yithen P(Y=yi) = ni/ |D| • When categories are complete and disjoint, P(X=xk): )( )|()( )|( k iki ki xXP yYxXPyYP xXyYP m i k iki m i ki xXP yYxXPyYP xXyYP 11 1 )( )|()( )|( m i ikik yYxXPyYPxXP 1 )|()()( 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 72
  • Classification via Logistic Regression • Insteadof generative models, a descriminativemodelcanbeusedtospecificallyfocuson the conditional distribution P(Y | X) • Assumes a parametric form for directly estimating P(Y | X) • Basically a linear model 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 73 n i ii Xww XYP 10 )exp(1 1 )|1( n i ii n i ii Xww Xww 10 10 )exp(1 )exp( )|1( )|0( 1iff0labelAssign XYP XYP Y n i ii Xww 10 )exp(1 n i ii Xww 100 n i ii Xww 10lyequivalentor )|1(1)|0( XYPXYP
  • Classification Y X1 X2 … Xn Y X1 X2 … Xn Naïve Bayes Logistic Regression Conditional Generative Discriminative 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 74
  • Sequence Labeling Y2 X1 X2 … XT HMM Linear-chain CRF Conditional Generative Discriminative Y1 YT .. Y2 X1 X2 … XT Y1 YT .. 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 75 SunitaSarawagi and William W. Cohen. Semi-Markov Conditional Random Fields for Information Extraction. In NIPS, 2005.
  • NER features • Gazetteers (background knowledge) – location names, first names, surnames, company names • Word – Orthographic • initial-caps, all-caps, all-digits, contains-hyphen, contains- dots, roman-number, punctuation-mark, URL, acronym – Word type • Capitalized, quote, lowercased, capitalized – Part-of-speech tag • NP, noun, nominal, VP, verb, adjective • Context – Text window: words, tags, predictions – Trigger words • Mr, Miss, Dr, PhD for person and city, street for location 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 76
  • Exploiting Query Logs / Click-Through Data • Weakly-supervised entity Extraction from queries / click-through data – A small set of seed instances for each entity type • Learn – Patterns captured by LDA-based topic model: Probabilities of query contexts and click websites of named entities for each class – Template: common query prefix and postfix, e.g. “how did countrygain independence” • Applypatterns / templatestoclick-throughdata/ querylogstominenewnamedentities Marius Pasca: Weakly-supervised discovery of named entities using web search queries. CIKM 2007:683-690 GuXu, Shuang-Hong Yang, Hang Li: Named entity mining from click-through data using weakly supervised latent dirichlet allocation. KDD 2009:1365-1374 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 77
  • Information extraction: relations • Relationextraction – <“Slovenia”, “borders”, “Italy”> • Relationresolution – <“Slovenia”, borders, “Italy”> – <“Slovenia”, next_to, “Italy”> • We distinguish between open and bootstraped approaches 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 78
  • Relation Extraction • Extracting relations – Typical paraphrase problem: identify all the ways a relation may be expressed • Formulated as classification task, e.g. uses SVM – Training data: parse tree, with nodes associate with a type as well as a role (e.g. role=member, role=affiliation to capture a person-affiliation relation) – Tree-based kernel: two trees are similar if roots have same type and role, and each has a subsequence of children (not necessarily consecutive) with the same types and roles – Examples are converted into such parse trees with role labels, and used to train the system – SVM can then classify new examples of possible relations • Formulate as sequence labeling (semantic role labeling) • Joint inference: considers different types of features (syntactic, semantic) and problems (extraction, resolution) 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 79
  • Bootstrapped information extraction • Provide examples for relationships which we want to extract • Compromise: lower coverage, higher quality • Example: Sofie, ReadTheWeb 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 80
  • Open information extraction • We do not want to put constraints on the types of relationships we want to extract • Very interesting for open-domain WWW datasets • Example: TextRunner • Compromise: highercoverage, lowerquality • Hybrid approaches: – EntityCube combines both bootstrapped and open extraction 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 81
  • Knowledge management • Organization • Consistency management • Strictness 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 82
  • Knowledge organization • Lexicon: A set of entities and statements • Ontology: A complex graph of formal concepts – Not only concrete entities, but also abstract classes – Sofie/Yago, WebKB, ReadTheWeb, TextRunner • Full world model: A Context-sensitive complex graph • Cyc 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 83
  • Knowledge consistency • Consistency management – Not all extracted information is accurate – Inaccurate information leads to inconsistencies in the knowledge base – Example: • Having pattern “?x is mayor of ?y” and knowledge that <x,mayorOf,y> requires <x,type,Person> and <y,type,City>, we can filter out inconsistent information 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 84
  • Knowledge consistency • Examples: – SOFIE: • Select the subset of statements which have the maximum satisfiability with regard to constraints – ReadTheWeb: • Learns new constraints via semi-supervised boostrap learning • Accuracy grows with ontology complexity 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 85
  • Knowledge management • Bootstrapping – Using existing manually prepared knowledge to generate new knowledge – While the knowledge base grows, the rules for extraction also change 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 86
  • Knowledge management • Strictness: – When do we consider entity and relationship resolution important? • Depends on use case: – Reasoning and data integration requires well- formed and unambigouous entities and relations – Information retrieval can cope with not-well formed relations and entities 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 87
  • Machine learning • Used in NLP, IE as well as knowledge acquisition • Various approaches – Self-supervised – Semi-supervised – Supervised 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 88
  • Machine learning • Natural language processing – Part-of-speechlearning • Information extraction – Pattern learning • ReadTheWeb, TextRunner, WebKB • Knowledge acquisition – Rule learning (WebKB) – Constraint learning (ReadTheWeb) 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 89
  • Summary • Cyc – Full world model knowledge base • WebKB – First attempt of automatically constructing a knowledge base • TextRunner – Open information extraction 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 90
  • Summary • EntityCube – Hybrid bootstrapped and open IE • SOFIE/YAGO – Tight integration of natural language processing, disambiguation and acquisition • Read The Web – Constraint learning 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 91
  • Entity Linking Source: Tadej Steiner from Jozef Stefan Institute, Ljubljana, Slovenia
  • Basic situation 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 93
  • Pipeline 1. Identify named entity mentions in source text using a named entity recognizer 2. Given the mentions, gather candidate KB entities that have that mention as a label 3. Rank the KB entities 4. Select the best KB entity for each mention 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 94
  • Pipeline 1. Identify named entity mentions in source text using a named entity recognizer 2. Given the mentions, gather candidate KB entities that have that mention as a label 3. Rank the KB entities 4. Select the best KB entity for each mention 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 95
  • Linking approaches - pair-wiselinking • Pair-wise linking: for each in-text entity, choose the candidate entity which is the best w.r.t. description similarity and textual features • Is each disambiguation choice independent? – Pair-wise vs. collective disambiguation 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 96
  • Important ranking features • Mention popularity – P(entity|mention) – P(dbpedia:Kashmir_(song)|”Kashmir”) = 0.54 – P(dbpedia:Kashmir_(region)|”Kashmir”) = 0.91 – Distribution of links and anchors in Wikipedia Context similarity - sim(ctx(mention), ctx(entity)) Context of a mention is the surrounding sentences Context of an entity is the description of the entity (Wiki article) Coherence / Collective Entities that appear together tend to be related to one another Usually solved by a greedy graph pruning algorithm 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 97
  • Collective linking • For each in-text entity, choose the candidate entity which is the most similar to the in-text entity and related to other entities that are already chosen. 01 Apr 2012 98 Tadej Stajner, Dunja Mladenic: Entity Resolution in Texts Using Statistical Learning and Ontologies. ASWC 2009:91-104 Xianpei Han, Le Sun, Jun Zhao: Collective entity linking in web text: a graph-based method. SIGIR 2011:765-774 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web
  • Relatedness • Intuition: entities that co-occur in the same context tend to be more related • How can we express relatedness of two entities in a numerical way? – Statistical co-occurrence – Similarity of entities’ descriptions – Relationships in the ontology 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 99
  • Semantic relatedness • If entities have an explicit assertion connecting them (or have common neighbours), they tend to be related Elvis Memphis Elvis Presley Memphis, Egypt Memphis, TN origin Person Location type type type St. Elvis type 01 Apr 2012 100
  • Co-occurrence as relatedness • If distinct entities occur together more often than by chance, they tend to be related Document FC Barcelona Bayern FC Barcelona Bavaria Bayern München Mutual information Mutual information x y x y x y z 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 101
  • Content similarity as relatedness • If distinct entities have higher similarity of their descriptions, they tend to be related Document a b x y z similarity = 0.35 similarity = 0.25 similarity = 0.35 similarity = 0,7 similarity = 0,1 similarity = 0,2 01 Apr 2012 102
  • Architecture Input text Preprocessing (entity extraction and consolidation) .. with in- text entities Background knowledge (ontology)Match retrieval Entity description vectors Assertion type informativeness Entity co-occurences .. with resolved entities Relatedness Entity linking 103
  • Crowdsourcing for Entity Linking Micro Matching Tasks HTML Pages HTML+ RDFa Pages LOD Open Data Cloud Crowdsourcing Platform Z enCrowd Entity Extractors LOD Index Get Entity Input Output Probabilistic Network Decision Engine Micro- TaskManager Workers Decisions Algorithmic Matchers Gianluca Demartini, DjellelEddineDifallah, and Philippe Cudré-Mauroux. ZenCrowd: Leveraging Probabilistic Reasoning and Crowdsourcing Techniques for Large-Scale Entity Linking. In: 21st International Conference on World Wide Web (WWW 2012), Lyon, France, April 2012. 104
  • Crowdsourcing for Entity Linking • Matching micro-task – Unclear (i.e., low confidence) matches are crowdsourced – Top algorithmic results are presented to the workers – Answers from the crowd are input to a probabilistic network 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 105
  • Crowdsourcing for Entity Linking • Probabilistic Graph – Worker prior probability (from previous tasks) – Link prior probability (from algo matchers) – Link factors connect worker clicks and links – SameAs constraints – Dataset unicitycontstraints w1 w2 l1 l2 pw1( ) pw2( ) lf1( ) lf2( ) pl1( ) pl2( ) l3 lf3( ) pl3( ) c11 c22 c12 c21 c13 c23 u2-3( )sa1-2( ) 01 Apr 2012
  • Entity De-duplication “Entity Consolidation” “Entity Resolution” “Record Linkage” “Instance Matching” Sources: Yongtao Ma from Karlsruhe Institute of Technology, Samur Araujo from The Delft Bioinformatics Lab and Aidan Hogan from Digital Enterprise Research Institute
  • Structure • Motivation • Problem and task overview • Consider only explicit owl:sameAs • Consider some lightweight reasoning • Inductive / instance matching methods – Effectiveness – Efficiency 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 108
  • Motivation 340,000 Results 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 109
  • Motivation • 2% of customer records obsolete in 1 month, due to deaths, name changes • $611B/year loss in US due to poor customer data • An average company has 49 different databases and spends 35% of its IT dollars on integration efforts 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 110
  • Motivation 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 111
  • Hetereogenity in naming… Tim Berners-Lee: URIs … timbl:i dblp:100007 identica:45563 adv:timblfb:en.tim_berners-lee db:Tim-Berners_Lee = owl:sameAs 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 112
  • 11 3 De-duplication for Web data 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 113
  • Entity De-duplication Problem and Task Overview
  • Data integration – big picture • Ontology matching – Widely studied in Semantic Web research, see e.g. list of publications at ontologymatching.org • Entity de-duplication – Logic-based approaches in the Semantic Web – Studied as record linkage in the database literature – Machine learning based approaches, focusing on attributes – Graph-based approaches, see e.g. the work of Lisa Getoor are applicable to RDF data • Improvements over only attribute based matching • Blending / data fusion – Merging objects that represent the same real world entity and reconciling information from multiple sources – Information quality / redundance 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 115
  • De-duplication • The problem of determining if two instances refer to the small real- world entity. owl:sameas Source Instances Target Instances 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 116
  • 1. Find equivalences in the data • Consider only explicit owl:sameAs (baseline) • Consider some lightweight reasoning (extended) • Inductive / instance matching methods 2. Rewrite data according to equivalences (data fusion) De-duplication – task overview 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 117
  • Entity De-duplication Consider only explicit owl:sameAs
  • • Use provided owl:sameAs mappings in the data timbl:i owl:sameas identica:45563 . dbpedia:Berners-Lee owl:sameas identica:45563 . • Store “equivalences” found timbl:i -> identica:45563 -> dbpedia:Berners-Lee -> timbl:i identica:45563 dbpedia:Berners-Lee De-duplication 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 119
  • • For each set of equivalent identifiers, choose a canonical term timbl:i identica:45563 dbpedia:Berners-Lee De-duplication 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 120
  • • Afterwards, rewrite identifiers to their canonical version: De-duplication timbl:i rdf:type foaf:Person . identica:48404 foaf:knows identica:45563 . dbpedia:Berners-Leedpo:birthDate “1955-06-08”^^xsd:date . dbpedia:Berners-Lee rdf:type foaf:Person . identica:48404 foaf:knows dbpedia:Berners-Lee . dbpedia:Berners-Leedpo:birthDate “1955-06-08”^^xsd:date . timbl:i identica:45563 dbpedia:Berners-Lee 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 121
  • Entity De-duplication Consider some lightweight reasoning
  • • Infer owl:sameAs through reasoning (OWL 2 RL/RDF) 1. explicit owl:sameAs (again) 2.owl:InverseFunctionalProperty 3.owl:FunctionalProperty 4.owl:cardinality 1 / owl:maxCardinality 1 foaf:homepage a owl:InverseFunctionalProperty . timbl:ifoaf:homepage w3c:timblhomepage . adv:timblfoaf:homepage w3c:timblhomepage . ⇒ timbl:iowl:sameasadv:timbl . …then apply data fusion as before Extendedde-duplication 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 123
  • Entity De-duplication Inductive / Instance Matching Methods
  • Agenda • Problem overview • Attribute level – (see term matching) • Instance level – Effectiveness: learning – Efficiency: blocking • Dataset level – (see collective entity linking) 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 125
  • Problem overview effectiveness vs. efficiency Instance Matching Effectivity Find correct matches! Efficiency Do it fast! 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 126
  • Efficiency O(NxM) Source Target Not efficient 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 127
  • “Diclofenac” occurrence on DBPEDIA 01 Apr 2012 128
  • Problem overview – attribute level <A1, ‘Dave White’, ‘Intel’, ‘Male’><P1, ‘Database…’, ‘John Black’, ‘Don White’> <A2, ‘Don White’, ‘CMU’, ‘Male’><P2, ‘Multimedia…’, ‘Sue Grey’, ‘D. White’> <A3, ‘Susan Grey’, ‘MIT’, ‘Female’><P3, ‘Title3…’, ‘Dave White’> <A4, ‘John Black’, ‘MIT’, ‘Male’><P4, ‘Title5…, ‘Don White’, ‘Joe Brown’> <A5, ‘Joe Brown’, unknown, ‘Male’><P5, ‘Title6…’, ‘Joe Brown’, ‘Liz Pink’> <A6, ‘Liz Pink’, unknown, ‘Female’><P6, ‘Title7…’, ‘Liz Pink’, ‘D. White’> Attribute level ‘Don White’ , ‘D. White’ ‘Don White’, ‘Dave, White’ • What (values?) • How • Similarity metrics • Similarity threshold • Matching techniques 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 129
  • Problem overview – instance level <A1, ‘Dave White’, ‘Intel’, ‘Male’><P1, ‘Database…’, ‘John Black’, ‘Don White’> <A2, ‘Don White’, ‘CMU’, ‘Male’><P2, ‘Multimedia…’, ‘Sue Grey’, ‘D. White’> <A3, ‘Susan Grey’, ‘MIT’, ‘Female’><P3, ‘Title3…’, ‘Dave White’> <A4, ‘John Black’, ‘MIT’, ‘Male’><P4, ‘Title5…, ‘Don White’, ‘Joe Brown’> <A5, ‘Joe Brown’, unknown, ‘Male’><P5, ‘Title6…’, ‘Joe Brown’, ‘Liz Pink’> <A6, ‘Liz Pink’, unknown, ‘Female’><P6, ‘Title7…’, ‘Liz Pink’, ‘D. White’> Instance level <A1, ‘Dave White’, ‘Intel’, ‘Male’> <A2, ‘Don White’, ‘CMU’, ‘Male’> • What (attributes?) • How • Similarity metrics • Similarity threshold • Matching techniques 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 130
  • • How • Similarity metrics • Similarity threshold • Matching techniques Problem overview – dataset level <A1, ‘Dave White’, ‘Intel’, ‘Male’><P1, ‘Database…’, ‘John Black’, ‘Don White’> <A2, ‘Don White’, ‘CMU’, ‘Male’><P2, ‘Multimedia…’, ‘Sue Grey’, ‘D. White’> <A3, ‘Susan Grey’, ‘MIT’, ‘Female’><P3, ‘Title3…’, ‘Dave White’> <A4, ‘John Black’, ‘MIT’, ‘Male’><P4, ‘Title5…, ‘Don White’, ‘Joe Brown’> <A5, ‘Joe Brown’, unknown, ‘Male’><P5, ‘Title6…’, ‘Joe Brown’, ‘Liz Pink’> <A6, ‘Liz Pink’, unknown, ‘Female’><P6, ‘Title7…’, ‘Liz Pink’, ‘D. White’> Dataset level • What (instances?) 01 Apr 2012 131
  • Agenda • Problem overview • Attribute Level – (see term matching) • Instance Level – Effectiveness: learning – Efficiency: blocking • Dataset Level – (see collective entity linking) 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 132
  • Character-based • [see term matching in Part 3 on search & ranking] • Edit Distance [G98] – Character Operations: insert, delete, replace – Given two strings, s and t, edit(s,t): • Minimum cost of operations transforming s to t • Exp.: edit(Eorror, Eror)=1, edit(great,grate)=2 – Aiming at: common typing errors – Problem: works not well with other type of errors • Exp.: D. White vs Dave White • Jaro Rule 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 133
  • Token-based • Q-gram – The q-grams are short character substrings of length q of the string – Example: 3-gram(White)=, ‘Whi’, ‘hit’, ‘ite’ - – set similarity then can be applied to the grams 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 134
  • Agenda • Problem overview • Attribute Level – (see term matching) • Instance Level – Effectiveness: learning – Efficiency: blocking • Dataset Level – (see collective entity linking) 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 135
  • Questions • Given instance attributes {Name, Institute, Gender, Publish} – Which ones are more important? – Which similarity measures should be adopted? – What is the threshold of similarity that should be adopted? 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 136
  • Bayes Decision Rule • Notation – A,B are two tables, of n comparable fields – tuple pairs – classes: M (match) and U (non-match) – random vector , xi shows the level of agreement of the ith field for • Decision rule: 01 Apr 2012 137 called likelihood ratio
  • Bayes Decision Rule • Given training data, assume p(xi|M) and p(xj|M) are independent for i≠j[5] • Extension: – Using an expectation maximization (EM) algorithm to estimate likelihood – Relax independent assumption – Decision with reject class 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 138
  • Agenda • Problem overview • Attribute Level – (see term matching) • Instance Level – Effectiveness: learning – Efficiency: blocking • Dataset Level – (see collective entity linking) 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 139
  • Blocking strategies Source Target • Used to reduce the number of instance comparison • Non-overlapping partitions • Canopies and clustering – overlapping partitions 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 140
  • Blocking strategies Blocking Attribute dependent Attribute agnostic When the source and target schema match. Otherwise 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 141
  • Attribute dependent • Blocking Key Value (BKV) – Sorted Neighborhood approach – Q-grams blocking technique • Blocking keys are highly discriminating attributes (e.g. last name, phone number) • Targeting homogeneous datasets b a (blocking key) c d 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 142
  • Sorted Neighborhood • Motivation: – similar records have similar values – multiple “cheap” passes faster than an “expensive” one • Goal: sort feature by a key to bring matching records close to each other • Methodology: – Create a key for every record (e.g. first 3 characters of last name) – Sort data by the key – Pair-wise comparison within a small sliding window – Multiple passes based on distinct key 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 143
  • Sorted Neighborhood • Example: ID Name SS Birthday ZIP r1 David Black 123-45 01.05.1985 76137 r2 Dauid Black 123-45 01.06.1985 76137 r3 David White 325-52 23.09.1984 84212 r4 David B. 126-53 30.10.1983 84123 r5 David B. 745-32 07.05.1973 84212 r1 r2 r4 r3 r5 r2 r1 r3 r4 r5 ZIP[1..3] Name[1..3] 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 144
  • Q-gram blocking • Motivation: similar matches have high overlaps of q-grams • Goal: relaxes the edit distance constraint to a weaker count constraint on the number of matching q-grams • Methodology: given two strings s and t, and a edit distance constraint k – Count Filtering: s and t must share LBs,t=max(|s|,|t|)-1-(k-1)*q q- grams – Position Filtering: s and t must share at least LBs,tpositional q-grams – Length Filtering: ||s|-|t||≤k 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 145
  • Q-gram blocking • Example: 3-gram s=abaxabaaba ##a,#ab,aba,bax,axa,xab,aba,baa,aab,aba,ba$,a$$ t=abaabaaba ##a,#ab,aba,baa,aab,aba,baa,aab,aba,ba$,a$$ ED(s,t)≤k → |Q(s) ∩ Q(t)| ≥ max(|s|,|t|)-1-(k-1)*q ED(s,t)=1, |Q(s) ∩ Q(t)|=9 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 146
  • Attribute dependent • Learning the attributes (blocking keys) – Decision tree – Maximum hyper-rectangles DrugBank DBPEDIA Label Drugname Sideeffect Page Title Name Producer Composition 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 147
  • Attribute dependent • Learning functions of similarity (e.g., Jaccard, Jaro, Levenshtein, Hamming, Cosine, etc.) DrugBank DBPEDIA Label= TitleDiclofenac Diclofenac Sodium =≈ 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 148
  • Attribute agnostic • Designed for heterogeneous information space.(i.e., loose schema binding, noise, missing or inconsistent values, as well as an unprecedented level of heterogeneity) • No knowledge about the schema software Corp. (blocking key) radio film 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 149
  • Attribute agnostic • “All tokens” • Reduce comparison space – Block purging, – Block scheduling, – Block enumeration, – Duplicate propagation, – Comparisons propagation, and – Comparisons pruning. software Corp. (blocking key) radio film 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 150
  • Entity Storage &Indexing
  • Indexing • Search requires matching and ranking – Matching selects a subset of the elements to be scored • The goal of indexing is to speed up matching – Retrieval needs to be performed in milliseconds – Without an index, retrieval would require scanning through the collection • The type of index depends on the types of data and queries to be supported – DB-style indexing – IR-style indexing 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 152
  • IR-style indexing • Index data as text – Create virtual documents from data – One virtual document per subgraph, resource or triple • typically: resource • Key differences to Text Retrieval – RDF data is structured – Minimally, queries on property values are required 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 153
  • Horizontal index structure • Two fields (indices): one for terms, one for properties • For each term, store the property on the same position in the property index – Positions are required even without phrase queries • Query engine needs to support the alignment operator • Dictionary is number of unique terms + number of properties 01 Apr 2012 154
  • Vertical index structure • One field (index) per property • Positions are not required – But useful for phrase queries • Query engine needs to support fields • Dictionary is number of unique terms • Number of fields could be a problem for merging, query performance 01 Apr 2012 155
  • Indexing using MapReduce • MapReduce is the perfect model for building inverted indices – Map creates (term, {doc1}) pairs – Reduce collects all docs for the same term: (term, {doc1, doc2…- – Sub-indices are merged separately • Term-partitioned indices 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 156
  • Search and Ranking
  • Outline • Expert Finding models • Entity Ranking in Wikipedia • Web Entity Retrieval • Entity Search over Structured Data • Relational Entity Search over Structured Data 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 158
  • From Documents to Entities • Document Search 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 159
  • From Documents to Entities • Entity Search 1. Ent1 2. Ent2 3. Ent3 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 160
  • A taxonomy of Entity Search tasks 01 Apr 2012 161
  • Expert Finding - Motivation • Scenario – In large companies competencies and skills are spread – Executives need to create a team for a new project: find staff with the right expertise – Someone needs to solve a problem – Example: I need an expert on ontology engineering 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 162
  • Expert Finding - Motivation • Goal – Use the digital content available in the enterprise – Create a ranking of people who are experts in the given topic 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 163
  • Motivation for System Support • Busy experts do not have time to maintain adequate descriptions of their continuously changing specialized skills • Expert seekers have poorly articulated requirements and are not fully enabled to judge a good expert from a bad one 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 164
  • Complicating factors • Volume of communication/publication is not a reliable indication of expertise • Certain topics engender more opinion than facts • Lack of information about past performance of experts • New employees don’t know about informal social networks • Access to expertise is often controlled (informally or formally, by the experts or their management) • Solutions to complex problems require diverse ranges of expertise 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 165
  • Evidence of Expertise • Email or bulletin board messages • Corporate communications • Shared folders in file system • Resumes and homepages • Employee database • Email flow • Bibliographic information • Software library usage • Search and publication history • Project time charges See also bibliography on TREC-ENT wiki: http://www.ins.cwi.nl/projects/trec-ent/wiki/index.php/Bibliography Content Social networks Activities 01 Apr 2012 166
  • Assumptions • Content – Experts are mentioned in relevant documents – Experts author relevant documents • Social networks – People that interact are likely to share expertise – Evidence in records of information exchange (and co-authorship, co-work) and/or organizational structure 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 167
  • Two Basic Approaches Who should I ask about the copyright forms? • Document-based: rank docs, extract experts Copyright forms Lori Lori Lori Ellen Ian Lori Lori Ellen Lori 1. 2. 1. 4. 5. 3. 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 168
  • Document-based Expert Finding • Find and score documents about the topic – Title about topic – Abstract about topic • Aggregate scores for each distinct author 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 169
  • Two Basic Approaches Who should I ask about the copyright forms? • Document-based: rank docs, extract experts • Candidate-based: rank candidate profiles Copyright forms Lori Lori Lori Ellen Ian Lori Lori Ellen Lori 1. 2. 1. 4. 5. 3. Lori Copyright forms Ellen Ian 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 170
  • Voting model • Data fusion techniques • Each ranked document represents a vote for the expertise of a candidate • Vote aggregation: – Number of docs voting for each candidate – Scores of retrieved documents – Ranks of retrieved documents 01 Apr 2012 Craig Macdonald, IadhOunis: Voting for candidates: adapting data fusion techniques for an expert search task. CIKM 2006: 387-396
  • User-Oriented Model • Additional real-world constraints • Distance between user and expert – User previous knowledge on the topic – Contact time (organizational hierarchy, geo location, collaboration) 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web Elena Smirnova, KrisztianBalog: A User-Oriented Model for Expert Finding. ECIR 2011: 580-592 172
  • Additional Techniques Research Systems • Combine the two basic approaches • Estimate the quality of the evidence • Use of collection/structural knowledge – Treat emails different from documents – Treat email’s subject/sender/receiver different from body – Locate homepages See also TREC proceedings 2005-2007 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 173
  • Additional Techniques Research Systems • Use social network extracted from co- authorship or email lists • Relevance propagation over expertise graph • Use Web Search evidence 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 174
  • Expert Finding - References – P@noptic Expert [Craswell et al. Ausweb01] – Balog’s Model 1 and 2 [Balog et al. SIGIR06] – Voting Model [Macdonald and Ounis CIKM06, ECIR07, ECIR08] – Expertise evidence [Macdonald et al. ECIR08] – Vector Space Model [Demartini et al. ECIR09] – Web evidence [Serdyukov et al. TREC08] – User-Oriented Model [Smirnova and BalogECIR11] 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 175
  • Entity Ranking
  • Ranking… • People • Actors • … Car companies [i.e., insert your fav entity type here] Entity Ranking!!! 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 177
  • Wikipedia • Encyclopedia – multilingual, Web-based, free-content, openly- editable: errors are promptly corrected • Articles: – balanced, neutral, and encyclopedic, containing notable verifiable knowledge • Categories / sub-categories • Links, anchor text (Germany -> Albert Einstein) ECIR 2012 Tutorial - From Expert FindingtoEntity Search on the Web
  • Entities in Wikipedia • Art museums • Countries • Actors, Singers • Monarchs • Artists • Magicians • ... 01 Apr 2012 ECIR 2012 Tutorial - From Expert FindingtoEntity Search on the Web 179
  • Example Entity Ranking Scenarios • Impressionist art museums in Holland • Countries with the Euro currency • German car manufacturers • Artists related to Pablo Picasso • Countries involved in WWI • Actors who played Hamlet • English monarchs who married French women Many examples on http://www.ins.cwi.nl/projects/inex-xer/topics/ 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 180
  • Entity Ranking • Topical query Q • Entity (result) type TX • A list of entity instances Xs • An entity is represented by its Wikipedia page • Systems employ categories, structure, links 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 181
  • Tasks • Entity Ranking (ER) – Given Q and T, provide Xs • List Completion (LC) – Given Q and Xs[1..m] – Return Xs[m+1..N] 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 182
  • ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web Topic 60 Title olympic classes dinghy sailing Entities 470 (dinghy) (#816578) 49er (dinghy) (#1006535) Europe (dinghy) (#855087) Categories dinghies (#30308) Description The user wants the dinghy classes that are or have been olympic classes, such as Europe and 470. Narrative The expected answers are the olympic dinghy classes, both historic and current. Examples include Europe and 470. TX Q Xs 01 Apr 2012 183
  • Formal Model for Entity Ranking – Indexing • Entities • Data Sources “AlexandrePato” ID: ap12dH5a (born in; 1989) (playing with; acm15hDJ)
  • Formal Model for Entity Ranking • Searching – Users' Information Need – Entity Ranking System
  • Approaches to ES in Wikipedia • Exploit and refine the category structure – Wordnet to find entity types (e.g., a professor is a person) • Extend the query – Synonyms and related words (Wordnet synsets) • Exploit the link structure – Links in Wikipedia are usually entities – Search Keywords also in anchor text of outLinks 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 186
  • “Children” Categories Sitcoms YAGO subClassOf Latino Sitcoms Sitcoms in Canada BBC Television Sitcoms Sitcom Characters Situation Comedy Fictional Character Wikipedia Subcategories Wikipedia Category ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web
  • “Sibling” Categories Sitcoms YAGO subClassOf Latino Sitcoms Sitcoms in Canada BBC Television Sitcoms Situation Comedy Wikipedia Category YAGO subClassOf ... ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web
  • EntitySearch over Wikipedia • Searchfor manydifferententitytypes with onesystem! • Mainobservations – Link information isimportant – Cleaning the categorystructure of Wikipedia iscritical (YAGO) – NLP-basedtechniques on the userqueryimproveeffectiveness • Open issues – No temporalevolution of contentisconsidered – Wikipedia ismeant to be objective 01 Apr 2012 ECIR 2012 Tutorial - From Expert FindingtoEntity Search on the Web Gianluca Demartini, Claudiu S. Firan, TerezaIofciu, Ralf Krestel, and Wolfgang Nejdl. WhyFindingEntities in Wikipedia isDifficult, Sometimes. In: "Information Retrieval" 13(5): 534-567, Springer, October2010. 192
  • Time-Aware Entity Retrieval • In some cases the time dimension is available – News collections – Blog postings • News stories evolve over time – Entitiesappear/disappear – Analyse and exploit relevanceevolution – Decide aboutrelevanceatdocumentlevel • An Entity Search system can exploit the past to find relevant entities Gianluca Demartini, Malik Muhammad SaadMissen, Roi Blanco, Hugo Zaragoza. TAER: Time Aware Entity Retrieval. CIKM 2010, Toronto, Canada. 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 193
  • Time-Aware Entity Retrieval 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 194
  • Time-AwareEntityRetrieval • Evaluation – P3, P5, AvgPrec – Ties aware measures [McSherry and Najork, ECIR08] • Paired t-test – ** p<<0.01 – * p<0.05 • Related considered NonRelevant 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 195
  • History Features • We also tried – Weight history features with doc length – Weight history features with BM25 Feature P3 P5 MAP F(e,d) .65 .56 .60 F(e,d1) .58 .53 .56 F(e,d-1) .64 .56 .62* F(e,H) .66 .59** .66** CoOcc(e,H) .62 .57 .65** DF(e,H) .63 .57* .65** 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 196
  • Dataset and Analysis • TREC NoveltyTrack2004 – 25 eventtopics – 779 relevant news • Entityannotations (7481 entities) • Relevancejudgements • How usefulis to findrelevantsentences? – P(e isRel) 0.411 [0.404-0.417] – P(e isNotRel) 0.168 [0.163-0.173] – P(e isRel | sisRel) 0.547[0.534-0.559] – Sentences:  21727 total 1.46 entityoccurences  5122 relevant 1.88 entityoccurences 01 Apr 2012 ECIR 2012 Tutorial - From Expert FindingtoEntity Search on the Web 197
  • Data Analysis • How useful is looking at the past? – P(e|d1) 0.893 [0.881-0.905] – P(e|d-1) 0.701 [0.677-0.726] • Is useful to consider sentence co- occurence? P(e1,e2) Relevant Related NotRelevant NotAnEntity Relevant 0.24 0.08 0.03 0.07 Related 0.07 0.03 0.03 NotRelevant 0.07 0.05 NotAnEntity 0.04 01 Apr 2012 ECIR 2012 Tutorial - From Expert FindingtoEntity Search on the Web 198
  • Approach • Entity Ranking features for News articles – Local Features  F(e,d)  FirstSenLen  FirstSenPos  Fsubj  AvgBM25(q,s)  SumBM25(q,s)  History Features • Featurecombination – Linear and Machine Learning 01 Apr 2012 ECIR 2012 Tutorial - From Expert FindingtoEntity Search on the Web 199
  • Local Features Feature P5 MAP F(e,d) .56 .60 FirstSenLen .36 .45 FirstSenPos .31 .43 Fsubj .44 .50 AvgBM25(q,s) .30 .41 SumBM25(q,s) .44 .52 Feature P5 MAP All Tied .34 .42 20001 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web
  • Is the past useful? • Looking at previous documents – Entity occurences so far F(e,H) – Docs where the entity appeared so far DF(e,H) – Entity occurences in the previous doc F(e,d-1) – Frequency of entity the first time F(e,d1) – Number of other entities with which the entity co-occured so far CoOcc(e,H) 20101 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web
  • History Features • * t-test pvalue< 0.05 ascompared with F(e,d) • ** t-test pvalue< 0.01 ascompared with F(e,d) Feature P5 MAP F(e,d) .56 .60 F(e,d1) .53 .56 F(e,d-1) .56 .62* F(e,H) .59** .66** CoOcc(e,H) .57 .65** DF(e,H) .57* .65** 20201 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web
  • 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 10 20 30 40 50 60 AvgPrec i-th document (i.e., history size+1) Using the History Using the History • Conclusion – Evidence from past documents is very important – Effectiveness should improve over time (run F(e,H)) 01 Apr 2012 203
  • Discussion • New searchtask: Time- AwareEntityRetrieval • Constructedevaluation benchmark • Experimental Evaluation – Investigated some features and combinations – Information from the pasthelpsmost – Obtain 15% improvement over F(e,d) 20401 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web
  • Ranking Entities on the Web
  • Ranking Entities on the Web • TREC Entity Track 2009-2010 – 50M web pages (including Wikipedia) – Find related entities (return homepages) 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 206
  • Ranking Entities on the Web • Approaches – Use Wikipedia (and infoboxes) as background info – Extract entities from tables and lists – Find the homepage given the entity name (see ENS) • Barack Obama ->www.barackobama.com • Since 2010: 1 billion web pages 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 207
  • Related Entity Finding • Approaches – Kaptein et al., CIKM10 • Exploits Wikipedia to improve entity retrieval effectiveness • Identifies entity types • Wikipedia external links as source for entity homepage • Anchor text index for entity search – Bron et al, CIKM10 • Entity co-occurence • Entity type filtering • Context (relation type) • Wikipedia-based homepage finding 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 208
  • Discussion 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 209
  • Expert Finding - Key Requirements • Identify experts via self-nomination and/or automated analysis of expert communications, publications, and activities • Classify the type and level of expertise of individuals and communities • Validate the breadth and depth of expertise of an individual • Recommend experts, including the ability to rank order experts on multiple dimensions including skills, experience, certification and reputation 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 210
  • Current systems • Hardly validate the breadth and depth of expertise – Count mentions – Weight with relevance score – Sometimes weight with authority of document containing candidate mention • Do not really attempt to classify the type and level of expertise 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 211
  • Evidence of Expertise • Information about true expertise is often not explicit in artifacts (as opposed to factual knowledge) • Information about expertise is expressed using specialized terms and concepts 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 212
  • How to improve? • Integrate more sources of evidence – CV information – Project related data • Including temporal information – Training data (HR dept) • Cost of achieving this evidence for expert vs. non-expert as weighting factor – Participation in TREC, authoring a book, getting a PhD in IR, ... Raymond D'Amore, Expert Finding in Disparate Environments, 2008 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 213
  • However... • Two types of challenges to be overcome: – System challenge – Evaluation challenge 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 214
  • System Challenges • Multi-lingual entity extraction • Privacy management – E.g., Tacit can email top N experts with private profiles (only recipient knows) • Interoperability with heterogeneous data sources – IMAP, Exchange, Lotus Notes – LDAP, JDBC/ODBC, XML repositories, Peoplesoft, Oracle Financials, Word/Excel/PDF, ... 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 215
  • Where is my data? • > 80% of data not in relational databases – Documents, spreadsheets, presentations – Web pages – Email, instant messages, news feeds – Images, audio, video 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 216
  • Dataspaces • The complete set of information belonging to one organization or task • Examples: – Personal dataspace – Enterprise dataspace – Community dataspace • E.g., scientific, sports club, ... “From Databases to Dataspaces: A New Abstraction for Information Management”, Michael Franklin, Alon Halevy, David Maier, SIGMOD Record, December 2005. 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 217
  • Conclusions so far... • Expert finding could in principle use many more resources that indicate expertise, possibly more reliably, but it is difficult to setup the research – System challenges – Data availability • Motivates research in operational setting – E.g., Raymond D'Amore, Expert Finding in Disparate Environments, 2008 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 218
  • Entity Search - Discussion • Similar challenges as Expert Finding • Entity information is spread over the Web – In different formats (HTML, RDF, images) – It is redundant (Wikipedia, DBPedia, homepage) – It varies over time (e.g., population of a country) – It is inconsistent (neutrino vs light speed) 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 219
  • Entity Search - References • Approaches exploit – Wikipedia structure (links, categories) • Kaptein et al., CIKM10 (REF) • Demartini et al., IRJ 2010 (XER) – Entity relations • Bron et al., CIKM10 (REF) • Demartini et al., CIKM10 (TAER) – Graph-based methods • Rode et al., INEX08 (XER) • Iofciu et al., ECIR11 (XER) – Probabilistic Models • Weerkamp et al., INEX08 (XER) • Balog et al., ECIR10 (XER) 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 220
  • Entity Search - Discussion • Structured data may be the way to improve search effectiveness – Entity identifiers – Entity relations 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 221
  • Ad-hoc Object Retrieval
  • Introduction • Unstructured or hybrid search over RDF data – Supporting end-users • Users who can not express their need in SPARQL – Dealing with large-scale data • Giving up query expressivity for scale – Dealing with heterogeneity • Users who are unaware of the schema of the data • No single schema to the data – Example: 2.6m classes and 33k properties in Billion Triples 2009 • Entity search – Queries where the user is looking for a single entity named or described in the query – e.g. kaz vaporizer, hospice of cincinnati, mst3000 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 223
  • Use cases in web search Top-1 entity with structured data Related entities Structured data extracted from HTML 224
  • Architecture overview Doc 1. Download, uncompress, con vert (if needed) 2. Sort quads by subject 3. Compute Minimal Perfect Hash (MPH) map map reduce reduce map reduce Index 3. Each mapper reads part of the collection 4. Each reducer builds an index for a subset of the vocabulary 5. Optionally, we also build an archive (forward-index) 5. The sub-indices are merged into a single index 6. Serving and Ranking 1st part of the talk 2nd part 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 225
  • Vertical index structure (reminder) • One field (index) per property • Positions are not required • Query engine needs to support fields  Dictionary is number of unique terms  Occurrences is number of tokens ✗ Number of fields is a problem for merging, query performance • In experiments we index the N most common properties 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 226
  • BM25F Ranking BM25(F) uses a term-frequency (tf) that accounts for the decreasing marginal contribution of terms where vsis the weight of the field tfsi is the frequency of term i in field s Bsis the document length normalization factor: lsis the length of field s avls is the average length of s bsis a tunable parameter 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 227 Roi Blanco, Peter Mika, SebastianoVigna: Effective and Efficient Entity Search in RDF Data. International Semantic Web Conference 2011:83-97
  • BM25F ranking cont. • Final term score is a combination of tf and idf where k1 is a tunable parameter wIDF is the inverse-document frequency: • Finally, the score of a document D is the sum of the scores of query terms q 01 Apr 2012 228
  • Hierarchicalentitymodel • Unstructured, structuredandhierarchicalentitymodel • Hierrachicalentitymodel – Predicate type generation – Predicate generation: importance of a predicate within its type – Term generation: importance of a term is determined by the predicate in which it occurs and all other predicates of that type in the entity 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 229 Robert Neumayer, Krisztian Balog, and Kjetil Nørvåg. On the modeling of entities for ad-hoc entity search in the web of data. In ECIR'12.
  • Query Independent Ranking • The question is not which answer is more relevant; i.e. all answers are relevant • The task is finding out which of the answers should be ranked higher • Importance is subjective • Closely related to the popularity of a resource? 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 230 Lorand Dali, Blaz Fortuna, Thanh Tran Duc and Dunja Mladenic Learning the Query-Independent Ranking of RDF Entity Search Results In Proceedings of 9th Extended Semantic Web Conference (ESWC'12)
  • Towns from Andhra Pradesh • Hyderabad • Srisailam • Chittoor • Masulipatnam • Chandavaram • Mahbubnagar • Gooty • Vijaywada • … 1. All answers are relevant 2. Ranking is important 3. Ranking is static 4. Hard to obtain the true ranking 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 231
  • Learning to Rank • Machine learning approach to building a ranking model • We know the true ranking (golden standard) • We represent each answer (resource) as a feature vector • The final score is a linear combination of the features, and the weights have to be learned A B C Pairwise preferences A better than B A better than C B better than C true ranking 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 232
  • Ranking Features • Importance derived – from Graph analysis – from Wikipedia – from Web search engine – from other external sources (N-gram databases) 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 233
  • Graph Features 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 234
  • Graph Features • Pagerank • Hubs and Authorities • RDF graph features – nRSubj - number of relations where this resource appears as the subject – nRObj - number of relations where this resource appears as the object – nLiteral - number of relations where this resource appears as the subject and the object is a literal 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 235
  • Importance of Wikipedia Pages • Popularity – How many people visited a particular page during June-January 2010 – Data obtained from the Wikipedia access logs available at: http://dammit.lt/wikistats – Captures importance from the point of view of users • Page length – How much text a Wikipedia page contains – Importance from the authors’ perspective • Number of edits – Importance from the editors’ perspective 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 236
  • Features Approximating Importance Correlate Well • Compare rank based on page length and based number of edits with page popularity Spearman’s CC NDCG Page length 0.60 0.84 Number of edits 0.78 0.93 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 237
  • Web Search Features • How many search results do we get in a web search if we search for: – The answer’s name – Keywords from the answer’s description • We used Yahoo! BOSS services to do the search • Querying the web for many resources is expensive 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 238
  • N-gram features • Similar to web search features • We look how many times the name of a resource appears in a large N-gram database (e.g. Google N-grams, Google Book N- grams, etc.) • A cheaper way to see how many times a resource appears on the web or in books 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 239
  • Relational Entity Search
  • Introduction • Intuitive keyword search interface over databases • “A direction” of semantic search, which employs semantics of – Relational information (structured data) in – Different datasets to produce complex structured, aggregated results to answer complex information needs • Short version of the Semantic Search tutorial at ESSIR’11 – Matching Techniques – Ranking Techniques • Complementary to DB keyword search tutorial, emphasizes – The role of textual data: data graphs with textual content nodes – Ranking [Chen et al, SIGMOD09] 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 241
  • Relational Entity Search Matching
  • Structure • Keyword search: keywords over data graphs – Term matching – Content matching – Structure matching • Schema-based keyword search • Schema-agnostic keyword search – Online search algorithms – Index-based approaches 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 243
  • Keyword search approaches • Finding “substructures” matching keyword nodes • Different result semantics for different types of data – Textual data (Web pages connected via hyperlink) – DB (tuple connected via foreign keys) – XML (elements/attributes via parent-child edges) • Commonly used results: Steiner tree / subgraph – Connect keyword matching elements – Contain one keyword matching element for every query keyword – Minimal substructures: closely connected keyword nodes • Query is ambiguous, lacks explicit structure constraints – NP-hard, thus efficiency of matching is a problem – Large amounts on candidate matches, thus ranking is a problem 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 244
  • Keyword search on hybrid data graphs Alice Bob is a good friend of mine. We went to the same university, and also shared an apartment in Berlin in 2008. The trouble with Bob is that he takes much better photos than I do: trouble with bob Bob sunset.jpg Beautiful Sunset Thanh KIT Germany Semantic Search 2009 Germany PeterFluidOps 34 knowssomeoneworksat KITapartment shared Berlin Alice Example information need “Information about a friend of Alice, who shared an apartment with her in Berlin and knows someone working at KIT.” Term matching Content matching Structure matching 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 245
  • Term matching • Distance-based (syntax) – Levenshtein distance (edit distance) – Hamming distance – Jaro-Winkler distance • Dictionary-based (semantics) – Taxonomy – Dictionary of similar words – Translation memory – Ontologies 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 246
  • Content matching • Retrieve partial matches • Inverted list (inverted index) ki {< d1, pos, score, ...>, < d2, pos, score, ...>, ...} • Combine partial matches: union or join shared shared berlin alice= = shared Berlin Alice shared Berlin Alice D1 D1 D1 01 Apr 2012 247
  • Structure matching • Retrieve structured data given patterns (e.g. triple patterns) • Index on tables • Multiple “redundant” indexes to cover different access patterns • Combine: union or join • Blocking, e.g. linear merge join (required sorted input) • Non-blocking, e.g. symmetric hash-join • Materialized join indexes SP-index PO-index = = = ?x ns:knows ?y. ?x ns:knows ?z. ?z ns: works ?v. ?v ns:name “KIT” Per1 ns:works?v ?vns:name “KIT” Per1 ns:works Ins1 Ins1 ns:name KIT Per1 ns:worksIns1 Ins1ns:name KIT Structure not explicitly given in query  exploration / other kinds of join 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 248
  • Structure • Keyword search: keywords over data graphs – Term matching – Content matching – Structure matching • Schema-based keyword search • Schema-agnostic keyword search – Online search algorithms – Index-based approaches 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 249
  • Matching in keyword search – schema-based Alice Bob KIT • Operate on schema graph • Query interpretation – Compute queries instead of results – Query presentation – Query processing by DB engine • Leverage the power of underlying DB query engine Result 1 Result 2 [Tran et al, ICDE09] [Hristidiset al, VLDB02] [Agrawalet al, ICDE02] [Qinet al, SIGMOD09] 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 250
  • Structure • Keyword search: keywords over data graphs – Term matching – Content matching – Structure matching • Schema-based keyword search • Schema-agnostic keyword search – Online search algorithms – Index-based approaches 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 251
  • Matching in keyword search – schema-agnostic Alice Bob KIT • Operate on data graph – No schema needed – Flexibly support different types of data e.g. hybrid data graphs – Native tailored optimization • Online in-memory graph search • Using materialized indexes Result 1 Result 2 [He et al, SIGMOD07] [Li et al, SIGMOD08] [Tran et al, CIKM11] [Kacholiaet al, VLDB05] 01 Apr 2012 252
  • Online search – top-k exploration• Compute Steiner tree with distinct roots • Backward expansion strategy • Run Dijkstra’s single-source-shortest-path algorithms – Explore shortest keyword-root paths – To find root (an answer) – Until k answers are found – Approximate: no top-k guarantee, i.e. further answers found later from other expansion paths may have higher score • Complete top-k: terminate safely when lower bound of top-k candidate is higher than upper bound of what can be achieved with remaining inputs [Bhalotia et al, ICDE02] Alice Bob KIT Result 1 01 Apr 2012 253
  • Taxonomy of matching approaches • Schema-based vs. schema-agnostic • Online search – Complete top-k – Approximate top-k – Backward expansion, bidirectional search, undirected subgraph exploration, dynamic programming • Indexing for retrieval + join for combine – Path retrieval, then path join – Graph retrieval, then graph pruning – Graph retrieval, then neighborhood / graph join (neighborhood indexed as a set of paths) 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 254
  • Relational Entity Search Ranking
  • Structure • Ranking paradigms – Explicit model of relevance – No notion of relevance • Features – Content-based – Structure-based – Structured-content-based 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 256
  • Ranking paradigms • No explicit notion of relevance: similarity between the query and the document model – Vector space model (cosine similarity) – Language models (KL divergence) • Explicit relevance model – Foundation: probability ranking principle – Ranking results by the posterior probability (odds) of being observed in the relevant class: )),...,(,),...,((),( ,,1,,1 qkqdtd wwwwCosdqSim )|( )|( log()|()||(),( d q q Vt dq tP tP tPKLdqSim ))|(1()|()|( DtDt NtPRtPRDP 01 Apr 2012 257
  • Features • Features are orthogonal to retrieval models – Weights for query / document vectors? – Language models for document / queries? – Relevance models? – What to use for learning to rank? 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 258
  • Features Dealing with ambiguities • Content features – Co-occurrences • Terms K that often co-occur form a contextual interpretation, i.e. topics (cluster hypothesis, distributional semantics) • “Berlin” and “apartment”  geographic context  Berlin as city – Frequencies: d more likely to be “about” a query term k when d more often, mentions k (probabilistic IR) • Structure features – Structured-content-based: consider relevance at fine- grained level of attributes – Link-based popularity – Proximity-based Term ambiguity Content ambiguity Structure ambiguity 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 259
  • Content-based features – frequency • Document statistics, e.g. – Term frequency – Document length • Collection statistics, e.g. – Inverse document frequency – Background language models )|()1( || )|( CtP d tf tP d idf d tf w dt || , • An object is more likely about “Berlin”? • When it contains a relatively high number of mentions of the term “Berlin” • When number of mentions of term in the overall collection is relatively low 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 260
  • Structure-based features – links • PageRank – Link analysis algorithm – Measuring relative importance of nodes – Link counts as a vote of support – The PageRank of a node recursively depends on the number and PageRank of all nodes that link to it (incoming links) • ObjectRank – Types and semantics of links vary in structured data – Authority transfer schema graph specifies connection strengths – Recursively compute authority transfer data graph • An object (about “Berlin”) is more important? • When a relatively large number of objects are linked to it [Hristidiset al, TDS08] How to incorporate it into a content-based retrieval model? 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 261
  • • EASE, XRANK, BLINKS, etc. • EASE – Proximity between a pair of keywords – Overall score of a JRT is aggregation on the score of keyword pairs • XRANK – Ranking of XML documents / elements – Proximity of n is defined based on w, the smallest text window in n that contains all search keywords Structure-based features – proximity • A structured result (e.g. Steiner tree) is more relevant? • When it is more compact s.t. elements are closely related [Li et al, SIGMOD08] [Guoet al, SIGMOD03] adopted from: [Chen et al, SIGMOD09] How to incorporate it into a content-based retrieval model? 262
  • Structured-content-based model • Consider structure of objects during content-based modeling, i.e., to obtain structured content-based model – Content-based model for structured objects, structured documents, database tuples… )|()|( f Ff fd d tPtP • An object is more likely about “Berlin”? • When its (important) fields / attributes contain a relatively high number of mentions of the term “Berlin” 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 263
  • P(w|Q) w .077 palestinian .055 israel .034 jerusalem .033 protest .027 raid .011 clash .010 bank .010 west .010 troop … sample probabilities UMM k i ik MqPMwPMPqqwP 1 1 )|()|()()...,( Palestinian Israeli raids ??? q1 q2 q3 w M M M )...( )...,( )...|()|( 1 1 1 k k k qqP qqwP qqwPRwP Structured-content-based model Relevance model [Lavrenkoet al, SIGIR01] 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 264
  • Structured-content-based model Edge-specific relevance model • Given a query Q=,q1,…,qn}, a set of resources (FR) are retrieved – E.g. Q={Hepburn, Holiday}, FR = {m1, p1, p4,m2, p2m2,m3} • Based on FR results, an edge specific RMFR is constructed for each unique edge e: [Biceret al, CIKM11] 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 265
  • • Edge-specific resource model: – Smoothing with model for the entire resource • Use RM for query expansion: the score of a resource calculated based on cross-entropy of edge-specific RMFR and edge-specific RMr: – Alpha allows to control the importance of edges Structured-content-based model Edge-specific relevance model 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 266
  • Ranking Steiner tree / join result tuples (JRT) • Ranking aggregated JRTs: – The cross entropy between the edge-specific RMFR (query model) and geometric mean of combined edge-specific RMJRT: • The proposed ranking function is monotonic with respect to the individual resource scores (a necessary property for using top-k keyword search algorithms) 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 267
  • Taxonomy of ranking approaches • Explicitly vs. non-explicitly relevance-based • Different approaches for model construction – Content-based ranking – Structure-based ranking – Structured-content-based ranking 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 268
  • • Agrawal, S., Chaudhuri, S., and Das, G. (2002). DBXplorer: A system for keyword-based search over relational databases. In ICDE, pages 5-16. • Amer-Yahia, S. and Shanmugasundaram, J. (2005). XML full-text search: Challenges and opportunities. In VLDB, page 1368. • Bao, Z., Ling, T. W., Chen, B., and Lu, J. (2009). Effective xml keyword search with relevance oriented ranking. In ICDE, pages 517-528. • Bhalotia, G., Nakhe, C., Hulgeri, A., Chakrabarti, S., and Sudarshan, S. (2002). Keyword Searching and Browsing in Databases using BANKS. In ICDE, pages 431-440. • Bicer, V., Tran, T. (2011): Ranking Support for Keyword Search on Structured Data using Relevance Models. In CIKM. • Bizer, G., Lehmann, J., Kobilarov, G., Auer, S., Becker, C., Cyganiak, R., Hellmann, S. (2009): DBpedia - A crystallization point for the Web of Data. J. Web Sem. (WS) 7(3):154-165 • Chen, Y., Wang, W., Liu, Z., Lin, X.(2009): Keyword search on structured and semi-structured data. SIGMOD 2009:1005-1010 • Ding, B., Yu, J. X., Wang, S., Qin, L., Zhang, X., and Lin, X. (2007). Finding top-k min-cost connected trees in databases. In ICDE, pages 836-845. • Golenberg, K., Kimelfeld, B., and Sagiv, Y. (2008). Keyword proximity search in complex data graphs. In SIGMOD, pages 927-940. • Guo, L., Shao, F., Botev, C., and Shanmugasundaram, J. (2003). XRANK: Ranked keyword search over XML documents. In SIGMOD. • He, H., Wang, H., Yang, J., and Yu, P. S. (2007). BLINKS: Ranked keyword searches on graphs. In SIGMOD, pages 305-316. • Hristidis, V., Hwang, H., and Papakonstantinou, Y. (2008). Authority-based keyword search in databases. ACM Trans. Database Syst., 33(1):1-40 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 269
  • • Hristidis, V. and Papakonstantinou, Y. (2002). Discover: Keyword search in relational databases. In VLDB. • Kacholia, V., Pandit, S., Chakrabarti, S., Sudarshan, S., Desai, R., and Karambelkar, H. (2005). Bidirectional expansion for keyword search on graph databases. In VLDB, pages 505-516. • Kimelfeld, B. and Sagiv, Y. (2006). Finding and approximating top-k answers in keyword proximity search. In PODS, pages 173-182. • Ladwig, G., Tran, T. (2011): Index Structures and Top-k Join Algorithms for Native Keyword Search Databases. In CIKM. • Lavrenko, V. Croft, W.B. (2001): Relevance-Based Language Models. In SIGIR, pages 120-127. • Li, G., Ooi, B. C., Feng, J., Wang, J., and Zhou, L. (2008). EASE: an effective 3-in-1 keyword search method for unstructured, semi-structured and structured data. In SIGMOD. • Liu, F., Yu, C., Meng, W., and Chowdhury, A. (2006). Effective keyword search in relational databases. In SIGMOD, pages 563-574. • Luo, Y., Lin, X., Wang, W., and Zhou, X. (2007). SPARK: Top-k keyword query in relational databases. In SIGMOD, pages 115-126. • Qin, L., Yu J. X., Chang, L. (2009) Keyword search in databases: the power of RDBMS. In SIGMOD, pages 681-694. • Tran, T., Herzig, D., Ladwig, G. (2011): SemSearchPro: Using Semantics throughout the Search Process. In Journal of Web Semantics, 2011. • Tran, T., Wang, H., Rudolph, S., Cimiano, P. (2009): Top-k Exploration of Query Graph Candidates for Efficient Keyword Search on RDF. In ICDE. • VagelisHristidis, L. G. and Papakonstantinou, Y. (2003). Efficient ir-style keyword search over relational databases. In VLDB. 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 270
  • Entity Search - Conclusions • Historically: • Expert Finding came first • Generalized to Entity Search – First on Wikipedia (easier) – Then on the Web (harder) • Over structured data – AOR – Relational Entity Search 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 271
  • Entity Search - Conclusions • What helps for Expert Finding? – Good entity extraction – Diverse evidence (Enterprise, web, user profiles) • What helps for Entity Search? – Wikipedia / DBPedia – Entity types – Anchors 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 272
  • Next Steps for Entity Search • Improve effectiveness for existing tasks – Errors propagate along the pipeline – Improve individual components (extraction, linking, de-duplication, etc.) – Improve ranking models by considering additional evidence (as for expert finding) • Work on top of Entity Search – New User experiences (based on entities) 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 273
  • Next Steps for Entity Search • Novel entity-oriented search tasks – Summary generation – Entity Attribute Search (“At which age Nobel prize winners in physics died?”) – Entity Popularity (rank Nobel laureates by popularity) – Relation Search (“how are Michelle Obama, Barack Obama, George Bush related?”) – Event Search (“Jazz concerts in Barcelona”) 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 274
  • Tutorial Outline • Morning – Data (Peter) – Data Management (Thanh) • Afternoon – Search and Ranking (Gianluca &Thanh) – Evaluation (Arjen) 01 Apr 2012 275 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web
  • Evaluation
  • Outline • TREC Enterprise • INEX Entity Ranking • TREC Entity • INEX Data Centric • SemSearch challenge 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 277
  • Relevance TREC working definition: If you were writing a report on the subject of the topic and would use the information contained in the document in the report, then the document is relevant. 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 279
  • Relevance – issues • Binary relevance – Regardless of how small the piece of relevant information is in relation to the rest of the document! • Considered only in isolation – Regardless of what other relevant information the user has already seen 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 280
  • Question: • How well does this working definition of relevance capture ‘real’ searcher behaviour? – Ideal PhD student  – Intelligence officer – Medical doctor – Patent lawyer – My mum 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 281
  • Question: • What happens if we change the definition of relevance to reflect different types of searches? 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 282
  • Entity Ranking • Many users search for specific entities instead of just any type of documents 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 283
  • Ranking People • Expert Finding in TREC-ENT (Enterprise Track) • Collection: – Corpus: crawl of *.w3.org sites – People: names of 1092 people who may be experts • Query: – ‘information retrieval’ • Results: – A list of people who know about information retrieval 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 284
  • C-TEST • TODO • C-TEST – http://es.csiro.au/C-TEST 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 285
  • Expert Finding 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 286
  • Evaluation Challenge • How to get realistic data for research evaluation? • So far: build corpora from externally facing parts of company intranets 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 287
  • Evaluation: Enterprise Track • Goal – Build a test collection for Enterprise Search • Corpora – W3C corpus (2005, 2006) – CSIRO corpus (2007) • Tasks – Email search (2005, 2006) – Expert finding (2005-2007) – Document search (2007) 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 288
  • W3C Collection • A crawl of the W3C web, containing email lists, wiki, web pages, etc. – 5.7 Gb crawl of w3.org sites (in June 2004) – 331,037 documents, including ~175,000 emails • A candidate list 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 289
  • W3C Limitations • Topics and assessments developed by researchers instead of users • Difficult to situate information needs in an organization you are not part of – representative of new members? • Public e-mail lists; no private/personal communication, and receivers unknown 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 290
  • CERC • Real enterprise – CSIRO is the Commonwealth Scientific and Industrial Research Organisation – 50 sites in Australia and around the world – 17 divisions, conducting research in areas such as industrial physics, mining, and ICT • Real users – Science communicators 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 291
  • CERC Limitations • Real data, but... • TREC has to use externally-facing data – Publicly available pages from csiro.au only – Can’t address security, databases, click data • No candidate list! – Participants do the assessments 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 292
  • UvT Expert Collection • Based on a publicly accessible database of UvT employees who are involved in research or teaching • ~38,000 documents – Research descriptions – Course descriptions – Publications – Personal homepages Balog et al., Broad Expertise Retrieval in Sparse Data Environments, SIGIR 2007 01 Apr 2012 293
  • UvT Expert Collection • Clean, heterogeneous, structured, and focused (but comprises a limited number of documents) • Contains hierarchical information from an organization (institutes and faculties) • List of expertise areas provided by the employees themselves – http://ilk.uvt.nl/uvt-expert-collection/ Balog et al., Broad Expertise Retrieval in Sparse Data Environments, SIGIR 2007 01 Apr 2012 294
  • ‘In Situ’ Evaluation Thomas and Hawking, Evaluation by comparing result sets in context, CIKM 2006 01 Apr 2012 295
  • ‘In Situ’ Evaluation • Advantages – Evaluation on live search system – Click-through data useful in inferring preference, even in the absence of explicit user judgements. • Limitations – Experimenter effect – Experiments not repeatable – A is better than B, but by how much? Multi-way comparisons? Thomas and Hawking, Evaluation by comparing result sets in context, CIKM 2006 01 Apr 2012 296
  • Alternative benchmark model? • Standard benchmark distributes the data + queries + relevance information • Q: Could we distribute the systems instead? – Need organization(s) willing to evaluate in own intra-net, with their own users – Need participants with robust systems that run off-site, unmonitored... 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 297
  • INEX Entity Ranking 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 298
  • Entity Ranking • Topical query Q • Entity (result) type TX • A list of entity instances Xs 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 299
  • Topic 60 Title olympic classes dinghy sailing Entities 470 (dinghy) (#816578) 49er (dinghy) (#1006535) Europe (dinghy) (#855087) Categories dinghies (#30308) Description The user wants the dinghy classes that are or have been olympic classes, such as Europe and 470. Narrative The expected answers are the olympic dinghy classes, both historic and current. Examples include Europe and 470. TX Q Xs 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 300
  • INEX-XER • INEX XML Entity Ranking Track • Assumptions: – Entities (Xs) are represented as Wikipedia pages – Binary relevance 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 301
  • Examples of Wikipedia Entities (TX) • Art museums and galleries • Countries • Famous people • Monarchs of the British Isles • Artists • Magicians 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 302
  • Entity Ranking Scenarios (Q,TX) • Impressionist art museums in Holland • Countries with the Euro currency • German car manufacturers • Artists related to Pablo Picasso • Countries involved in WWI • Actors who played Hamlet • Films shot in Venice • Paul Auster novels Many more examples on http://www.ins.cwi.nl/projects/inex-xer/topics/ 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 303
  • Tasks • Entity Ranking (ER) – Given Q and TX, provide Xs • List Completion (LC) – Given Q and Xs[1..m] – Return Xs[m+1..N] 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 304
  • Topic 60 Title olympic classes dinghy sailing Entities 470 (dinghy) (#816578) 49er (dinghy) (#1006535) Europe (dinghy) (#855087) Categories dinghies (#30308) Description The user wants the dinghy classes that are or have been olympic classes, such as Europe and 470. Narrative The expected answers are the olympic dinghy classes, both historic and current. Examples include Europe and 470. 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 305
  • Easy Baseline? //article[ about(Category:T and Q)]/title 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 306
  • But not an easy task… • Categories related to car companies: – Peugeot_vehicles – Citroën_vehicles – Mercedes-Benz platforms – DaimlerChrysler – Motor vehicle manufacturers of Germany – List_of_automobile_manufacturers – Companies established in 1926 – … 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 307
  • XML Structure to Rescue? • Q = ‘James Bond’ • Xs = *Sean Connery, Roger Moore, …+ • Corpus data: – Category:James Bond Actors – Passage Over 40 years after he first played the role, Connery is still widely regarded as the definitive cinematic incarnation of James Bond, despite popular interpretations by the likes of Roger Moore, Pierce Brosnan and Timothy Dalton, the latter often considered closer to the Bond of the novels. – Passage To date, six actors have portrayed James Bond in the official series. They are: • Sean Connery (1962–1967; 1971) • George Lazenby (1969) • Roger Moore (1973–1985) • Timothy Dalton (1987–1989) • Pierce Brosnan (1995–2002) • Daniel Craig (2006–present) • So, should exploit XML Structure!!! 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 308
  • Structure in the Collection • Wikipedia contains semantically very rich annotations: – Wikipedia Categories – Wikipedia Lists – Times (1930, 1931, 1932, etc. etc.) – Names – Disambiguation pages 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 309
  • 01 Apr 2012 310
  • So... Recall our question: • What happens if we change the definition of relevance to reflect different types of searches? 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 311
  • INEX-XER Results • XER Topics created by participants: – Today, 38 genuine XER topics with completed assessments – ER: 8 groups, 18 runs submitted – LC: 6 groups, 10 runs submitted – Pool depth 50 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 312
  • Ad-hoc vs. XER • Many ad-hoc topics can serve as entity ranking topics (but articles containing relevant passages must be re-assessed) – INRIA&RMIT created 30 training topics based on the INEX 2006 ad-hoc collection – XER Organizers created a similar set of 18 topics based on the INEX 2007 ad-hoc collection 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 313
  • Not relevant for XER… • Articles on topic are not necessarily relevant entities – Actually, they are surprisingly often not! • Articles that are or contain hubs (e.g., ‘list of …’ pages) are not entities either, so they are also not considered relevant 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 314
  • Ad-hoc vs. XER • On the 18 ad-hoc ’07 topics that were re- assessed as XER topics, only about 35% out of original relevant documents have been assessed as relevant – Most relevant Steven Seagul results are movies (17/20); but most ‘French president in the 5th republic’ results are not presidents or not presidents in the 5th republic (6/129), and most Bob Dylan songs are not songs (32/267) 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 315
  • Ad-hoc vs. XER • E.g., ‘Bob Dylan songs’ relevant articles/passages in ad- hoc, but non-relevant entities in XER: – Bob Dylan – The Band – Albums (however… what about track list?) – Don’t look back (film) – March on Washington for Jobs and Freedom (speech) – 19XX *in music+, 19XX grammy awards… – Singer-songwriters that play covers/tributes/… – Cities where Bob Dylan lived Etc.etc. 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 316
  • XER Close(r) to ‘Real’ User Need? • Example: Ad-hoc topic 509, ‘songs of Bob Dylan’ – Relevant passage… The theme song is called This Wheel's on Fire written by Bob Dylan and performed in this instance by Julie Driscoll. …however hidden in middle of lengthy page about the ‘Absolutely fabulous’ TV series • Clearly, the song’s article itself is the preferable result, even for the ad-hoc task 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 317
  • Perceived pool quality • Participants missed only some entities they know of… – Toyota (‘hybrid cars’) – Goat (‘herbivorous mammals’) – Hillary Clinton (‘women in politics’) • More of a problem for ad-hoc derived pools? 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 318
  • Using hubs for validation • Steven Seagal entity page – Film-o-graphy: • Prince of Central Park not in assessments • Further web search: producer, not actor! 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 319
  • Q: Non-topic-authors as judge? Q: Use external information for judging? 01 Apr 2012 320
  • Q: Assessor’s Topic Knowledge not in Collection? 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 321
  • TREC (Web) Entity (Search) 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 322
  • Related Entity Finding (REF) • Topics: – Input Entity: Name + Homepage – Target Type: Person | Organisation | Product | Location – Narrative: Description of the relation in free text 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 323
  • Entity Representation (on the Web) • Primary Entity Homepage: devoted to and in control of the entity – Gain = 3 • Relevant Entity Homepage: devoted to the entity, but not in its control – Gain = 1 • Not an Entity Homepage: – Wikipedia Page – News articles and blog posts, even if exclusively about the entity 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 324
  • The 2009 Pilot • 20 topics – Target Type: Person | Organisation | Product – 13 participants 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 325
  • Example Topics • Organizations that award Nobel prizes • CDs released by the King's Singers • Companies that John Hennessey serves on the board of • Universities that are members of the SEC conference for football • Scotch whisky distilleries on the island of Islay 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 326
  • Lessons Learned • Not that many entities in ClueWeb B – Makes it difficult to define good topics, especially product topics • Wikipedia/DBPedia dominate approaches and results 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 327
  • 2010 • ClueWeb A English • 50 new REF topics – Target Type: Person | Organisation | Product | Location – 47 topics judged, pool depth 20 • 20 2009 topics re-assessed • Wikipedia pages not accepted as valid answers, primary homepages rewarded more – Evaluation script: http://trec.nist.gov/data/entity/10/eval-entity.pl • 14 participants 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 328
  • 2011 • Modifications to simplify the evaluation and to improve cross-system comparison – Binary relevance (only Primary HP) – Groups using Web Search Engines should also submit a run using the Lemur ClueWeb Online Query Service • 50 new REF topics – Target Type not limited (extracted from Narrative) – Support document per answer (could be WP page) • 10 participants (only 4 doing REF ) 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 329
  • Entity List Completion (2010) • BTC-2009 – Billion Triple Collection – LOD sample, 1.14B statements, 17GB zipped • 13 topics – Out of 20 REF 2009, skipping topics without results or without example entities – Mapped type to most specific class in DBPedia ontology – Mapped known relevant entity HPs to URIs in BTC- 2009 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 330
  • ELC Assessments (2010) • By track coordinators (yes, I have read my share of the semantic web ) • Many answers of correct type, but not in correct relation to example entities (and, many descriptors of input entity had been retrieved) • 5 topics without relevant entities, 1 topic “un- judgeable” 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 331
  • “Semantic” Sub-tasks 2011 • Sindice-2011 dataset – Various Linked Data RDF dumps – 1.7B entities, 11B RDF statements (230M docs) – Web documents containing RDF, RDFa and Microformats documents (crawled by the Sindice Bot, since 2009) http://data.sindice.com/trec2011/ S. Campinas, D. Ceccarelli, T. E. Perry, R. Delbru, K. Balog, and G. Tummarello. The Sindice-2011 Dataset for Entity-Oriented Search in the Web of Data. In EOS, SIGIR 2011 workshop, July 28, Beijing, China. 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 332
  • “Semantic” Sub-tasks 2011 • REF-LOD – Like REF, but results are URIs in Sindice instead of docids in ClueWeb • But, not a re-usable collection – only 1 participant • Entity List Completion – Like ELC 2010, but examples not given as URI, but only as ClueWeb identifier 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 333
  • Meanwhile at SemEval... 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 334
  • Web People Search • WePS1 (2007) focused on Disambiguation of person names in search results – determine #people for that person name – assign documents to each referent • WePS2 (2009) introduced Attribute Extraction – 18 attribute types including birthdate and – place, occupation, affiliation, ... • WePS3 (2010) introduced disambiguation for organization names (motivated by Online Reputation Management) http://nlp.uned.es/weps/01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web
  • Other Entity Oriented Evaluations • Where to go, now that TREC Entity is “on a break”?! 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 336
  • TREC Knowledge Base Acceleration • Given – Incomingtextstream (newsandsocial media content) • First month w/ human-generatedlabels as training data – A target entityfrom a knowledge base (fornow: people, specifiedbytheirFreebaseand Wikipedia entries) • Score each item (“document”) based on how “pertinent” it is to the target KB node 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 337
  • TAC Knowledge Base Population • Tasks related to extracting information about entities with reference to an external knowledge source (Wikipedia infoboxes) • KBP 2011 had three tasks: – entity-linking: given an entity name (person, organization, or geopolitical entity) and a document containing that name, determine the KB node for that entity or add a new node for the entity if it is not already in the KB – slot-filling: given a named entity and a pre-defined set of attributes (“slots”) for the entity type, augment a KB node for that entity by extracting all new learnable slot values from a large corpus of documents – temporal slot-filling: similar to the regular slot-filling task, but also requests time intervals to be specified for each extracted slot value. 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 338
  • CLEF RepLab • Online reputation management (ORM) – Continuation of the WePS3 ORM task – Two tasks, evaluated on Twitter data: • Monitoring task: thematically cluster tweets including a company’s name • Profiling task: annotate tweets according to their polarity 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 339
  • INEX Data-Centric Track 2011 • IMDB data collection – XML generated from plain text files published on the IMDB web site on April 10, 2010 – Movies and persons involved in movies, e.g. actors/actresses, directors, producers etc. • ~4M objects, including 1.5M movies and 1.8M actors • Each object is richly structured – Each movie has title, rating, directors, actors, plot, keywords, genres, release dates, trivia, etc. – Each person has name, birth date, bio, filmography, etc. 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 340
  • INEX Data-Centric Track 2011 • Ad-hoc – Return a ranked list of results, i.e. objects, or equivalently documents in the IMDB collection, estimated relevant to the user’s information need – Known-item, list and informational topics • Faceted search task – Recommend appropriate facet-values for the user to refine the query and thus quickly identify what he/she really wants in the large set of results. – A User Simulation System (USS) interacts with a so-called FSM to simulate a faceted search session; using simulated interaction cost as evaluation measure (alongside NDCG of facet values) 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 341
  • INEX Data-Centric Track 2012 • 2012 uses DBPedia and Wikipedia 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 342
  • Evaluation Ad-hoc Object Retrieval
  • Semantic Search Evaluation • Started at SemSearch 2010 • Two tasks – Entity Search • Queries where the user is looking for a single real world object • Pound et al., Ad-hoc Object Retrieval in the Web of Data, WWW 2010. – List search (new in 2011) • Queries where the user is looking for a class of objects 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 344
  • Semantic Search Evaluation • Billion Triples Challenge 2009 dataset • Evaluated using Amazon’s Mechanical Turk – See Halpin et al. Evaluating Ad-Hoc Object Retrieval, IWEST 2010 • Prizes sponsored by Yahoo! Labs (2011) 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 345
  • SemSearch Entity Search Track • Retrieval of data related to a single entity • 50 queries explicitly naming an entity(possibly w/ context) – Selected from Search Query Tiny Sample v1.0 (i.e., Yahoo! Webscope): web search queries sampled from US query log of January, 2009. – Queries asked by at least three different users; queries with long number sequences removed (privacy reasons) • 2011: same setup, but queries derived from a mix of Microsoft and Yahoo! Logs 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 346
  • SemSearch List Query Track • List queries –Queries that describe a set of entities –The answer is a closed set –Relatively small number of possible answers –The answer is not likely to change 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 347
  • SemSearch List Query Track • Hand-picked but not hand-written –Yahoo! Search logs • Queries from the Tiny Sample v1.0 dataset • Queries with clicks on Wikipedia –TrueKnowledge • Recent queries 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 348
  • Data set • Billion Triples Challenge 2009 data set –Blank nodes encoded as URIs • A data set combining crawls of multiple semantic search engines –doesn’t necessarily match the current state of the Web –doesn’t necessarily match the coverage of any particular search engine http://km.aifb.uni-karlsruhe.de/ws/dataset_semsearch2010/000-CONTENTS01 Apr 2012
  • Collecting the results • Submissions via semsearch.yahoo.com – NTNU, IIIT Hyderabad, DERI, U of Delaware, Daiict – max. 3 submissions per team per track • Pooling of results – Top 20 results are evaluated – Despite validation, still problems • e.g. N-Triples encoded URIs, lowercased URIs • Collecting triples for each result – All triples where the URI is the subject – Discarded URIs that didn’t appear as subject • Rendering result display – Values are clipped at 300 chars (last # or / for object-properties) – RDF built-ins shown first – Preference to English language values 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 350
  • semsearch.yahoo.com
  • Assessment with Amazon Mechanical Turk – Evaluation using non-expert judges • Paid $0.2 per 12 results – Typically done in 1-2 minutes – $6-$12 an hour – Sponsored by the European SEALS project • Each result is evaluated by 5 workers – Workers are free to choose how many tasks they do • Makes agreement difficult to compute 0 50 100 150 200 250 300 Number of tasks completed per worker (2010) 352
  • Evaluation form 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 353
  • Evaluation form 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 354
  • Catching the bad guys – Payment can be rejected for workers who try to game the system • An explanation is commonly expected, though cheaters rarely complain – We opted to mix control questions into the real results • Gold-win cases that are known to be perfect • Gold-loose cases that are known to be bad – Metrics • Avg. and std. dev on gold-win and gold-loose results • Time to complete
  • Catching the bad guys 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 356 Worker Known bad Real Known Good Total N Time to complete (sec)N Mean N Mean N Mean badguy 20 2.556 200 2.738 20 2.684 240 29.6 goodguy 13 1 130 2.038 13 3 156 95 whoknows 1 1 21 1.571 2 3 24 83.5
  • Lessons learned • Finding complex queries is not easy – Query logs from web search engines contain simple queries – Semantic Web search engines are not used by the general public • Computing agreement is difficult – Each judge evaluates a different number of items • Result rendering is critically important • The Semantic Web is not necessarily all DBpedia – sub30-RES.3 40.6% DBpedia – sub31-run3 93.8% Dbpedia • Follow up experiments validated the Mechanical Turk approach – Blanco et al. Repeatable and Reliable Search System Evaluation using Crowd-Sourcing, SIGIR2011 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 357
  • SemSearch Next Steps • Achieve repeatability –Simplify our process and publish our tools –Automate as much as possible… except the Turks ;) • Web site for evaluation –Continuous submission? • Join the discussion at semsearcheval@yahoogroups.com 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 358
  • Entity Search Evaluation • What’s next? – Continuous evaluation for AOR – INEX Data-Centric Track 2012 – Knowledge Base Acceleration Track at TREC 2012 – New entity-centric search tasks? – New data collections? 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 359
  • 01 Apr 2012 ECIR 2012 Tutorial - From Expert Finding to Entity Search on the Web 360