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DB and IR Integration
 

DB and IR Integration

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Semantic search is going to be real soon

Semantic search is going to be real soon

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DB and IR Integration DB and IR Integration Presentation Transcript

  • in collaboration with Georgiana Ifrim, Gjergji Kasneci, Josiane Parreira, Maya Ramanath, Ralf Schenkel, Fabian Suchanek, Martin Theobald
  • DB and IR: Two Parallel Universes canonical application: accounting libraries data type: numbers, short strings text foundation: algebraic / logic based probabilistic / statistics based search paradigm: Boolean retrieval (exact queries, result sets/bags) ranked retrieval (vague queries, result lists) Database Systems Information Retrieval market leaders: Oracle, IBM DB2, MS SQL Server, etc. Google, Yahoo!, MSN, Verity, Fast, etc. parallel universes forever ?
  • Why DB&IR Now? – Application Needs
    • Global health-care management for monitoring epidemics
    • News archives for journalists, press agencies, etc.
    • Product catalogs for houses, cars, vacation places, etc.
    • Customer support & CRM in insurances, telcom, retail, software, etc.
    • Bulletin boards for social communities
    • Enterprise search for projects, skills, know-how, etc.
    • Personalized & collaborative search in digital libraries, Web, etc.
    • Comprehensive archive of blogs with time-travel search
    Simplify life for application areas like: Typical data: Disease (DId, Name, Category , Pathogen …) UMLS-Categories ( … ) Patient (… Age, HId, Date, Report , TreatedDId) Hospital (HId, Address …) Typical query: symptoms of tropical virus diseases and reported anomalies with young patients in central Europe in the last two weeks
  • Why DB&IR Now? – Platform Desiderata Structured data (records) Unstructured data (documents) Unstructured search (keywords) Structured search (SQL,XQuery) DB Systems IR Systems Search Engines Keyword Search on Relational Graphs (IIT Bombay, UCSD, MSR, Hebrew U, CU Hong Kong, Duke U, ...) Querying entities & relations from IE (MSR Beijing, UW Seattle, IBM Almaden, UIUC, MPI, … ) Platform desiderata (from app developer‘s viewpoint):
    • Flexible ranking on text, categorical, numerical attributes
      • cope with „too many answers“ and „no answers“
    • High update rate concurrently with high query load
    • Ontologies (dimensions, facets) for products, locations, org‘s, etc.
      • for query rewriting (relaxation, strengthening)
    • Complex queries combining text & structured attributes
      • XPath/XQuery Full-Text with ranking
    Integrated DB&IR Platform
  • Why DB&IR Forever? Turn the Web, Web2.0, and Web3.0 into the world‘s most comprehensive knowledge base („ semantic DB “) !
    • Data enrichment at very large scale
    • Text and speech are key sources of
    • knowledge production (publications, patents, conferences, meetings, ...)
    2000 2007 indexed Web 2 Bio. 20 Bio. Flickr photos --- 100 Mio. digital photos ? 150 Bio. Wikipedia 8 000 1.8 Mio. OECD researchers 7.4 Mio. 8.4 Mio. patents world-wide ? 60 Mio. US Library of Congres 115 Mio. 134 Mio. Google Scholar --- 500 Mio.
  • Outline • Past • Future • Present : Matter, Antimatter, and Wormholes : From Data to Knowledge : XML and Graph IR
  • Parallel Universes: A Closer Look Matter Antimatter
    • user = programmer
    • query = precise spec.
    • of info request
    • interaction via API
    • user = your kids
    • query = approximation
    • of user‘s real info needs
    • interaction process via GUI
    • strength: indexing, QP
    • weakness: user model
    • strength: ranking model
    • weakness: interoperability
    • eval. measure: efficiency
    • (throughput, response time,
    • TPC-H, XMark, …)
    • eval. measure: effectiveness
    • (precision, recall, F1, MAP, NDCG,
    • TREC & INEX benchmarks, …
  • DB IR 1990 1995 2000 2005 VAGUE (Motro) Proximal Nodes (Baeza-Yates et al.) WHIRL (Cohen) Prob. Datalog (Fuhr et al.) INEX XPath XPath Full-Text Prob. DB (Cavallo&Pittarelli) Prob. Tuples (Barbara et al.) Web Entity Search: Libra, Avatar, ExDB … Faceted Search: Flamenco … 1st Gen. XML IR: XXL, XIRQL, Elixir, JuruXML Multimedia IR Web Query Languages: W3QS, WebOQL, Araneus … Semistructured Data: Lore, Xyleme … 2nd Gen. XML IR: XRank,Timber, TIJAH, XSearch, FleXPath, CoXML, TopX, MarkLogic, Fast … Uncertain & Prob. Relations: Mystiq, Trio … Struct. Docs Deep Web Search Digital Libraries Graph IR
  • WHIRL: IR over Relations [W.W. Cohen: SIGMOD’98] Add text-similarity selection and join to relational algebra Example: Select * From Movies M, Reviews R Where M.Plot ~ ” fight“ And M.Year > 1990 And R.Rating > 3 And M.Title ~ R.Title And M.Plot ~ R.Comment Title Plot … Year Movies Title Comment … Rating Reviews Matrix Hero Matrix 1 Matrix Reloaded Matrix Eigenvalues Ying xiong aka. Hero Shrek 2 … matrix spectrum … orthonormal … … fight for peace … … sword fight … dramatic colors … … In ancient China … fights … sword fight … fights Broken Sword … In the near future … computer hacker Neo … … fight training … … cool fights … new techniques … … fights … and more fights … … fairly boring … 1999 2002 2004 In Far Far Away … our lovely hero fights with cat killer … 4 1 5 5
    • DB&IR for query-time data integration
    • More recent work: MinorThird, Spider, DBLife, etc.
    • But scoring models fairly ad hoc
    Scoring and ranking: s (<x,y>, q: A~B) = cosine (x.A, y.B) s (<x,y>, q 1  …  q m ) = x j ~ tf (word j in x)  idf (word j) with dampening & normalization
  • XXL: Early XML IR [Anja Theobald, GW: Adding Relevance toXML, WebDB’00] Which professors from Saarbruecken (SB) are teaching IR and have research projects on XML? Union of heterogeneous sources without global schema Similarity-aware XPath: // ~ Professor [//* = ” ~ SB“] [ // ~ Course [//* = ” ~ IR“] ] [ // ~ Research [//* = ” ~ XML“] ] Similarity-aware XPath: // ~ Professor [//* = ” ~ SB“] [ // ~ Course [//* = ” ~ IR“] ] [ // ~ Research [//* = ” ~ XML“] ] Professor Name : Gerhard Weikum Address ... City : SB Country : Germany Teaching Research Course Title : IR Description : Information retrieval ... Syllabus ... Book Article ... ... Project Title : Intelligent Search of Heterogeneous XML Data Funding : EU ... Name : Ralf Schenkel Lecturer Address: Max-Planck Institute for Informatics, Germany Activities Seminar Contents: Ranked retrieval … Literature: … Scientific Name: INEX task coordinator (Initiative for the Evaluation of XML …) Other Sponsor: EU …
  • XXL: Early XML IR [Anja Theobald, GW: Adding Relevance toXML, WebDB’00]
    • Scoring and ranking:
    • tf*idf for content condition
    • ontological similarity for
    • relaxed tag condition
    • score aggregation with
    • probabilistic independence
    Similarity-aware XPath: // ~ Professor [//* = ” ~ Saarbruecken“] [ // ~ Course [//* = ” ~ IR“] ] [ // ~ Research [//* = ” ~ XML“] ] Which professors from Saarbruecken (SB) are teaching IR and have research projects on XML? Motivation: Union of heterogeneous sources has no schema Professor Name : Gerhard Weikum Address ... City : SB Country : Germany Teaching Research Course Title : IR Description : Information retrieval ... Syllabus ... Book Article ... ... Project Title : Intelligent Search of Heterogeneous XML Data Funding : EU ... Name : Ralf Schenkel Lecturer Address: Max-Planck Institute for Informatics, Germany Activities Seminar Contents: Ranked retrieval … Literature: … Scientific Name: INEX task coordinator (Initiative for the Evaluation of XML …) Other Sponsor: EU … Wu&Palmer: |path| through lca(x,y) Dice coeff.: 2 #(x,y) / (#x + #y) on Web query expansion model: disjunction of tags magician wizard intellectual artist alchemist director primadonna professor teacher scholar academic, academician, faculty member scientist researcher HYPONYM (0.749) investigator mentor RELATED (0.48) lecturer
  • The Past: Lessons Learned
    • DB & IR : added flexible ranking to (semi) structured querying
    • to cope with schema and instance diversity
    • but ranking seems „ad hoc“ and
    • not consistently good in benchmarks
    • to win benchmark: tuning needed,
    • but tuning is easier if ranking is principled !
    • ontologies are mixed blessing :
    • quality diverse, concept similarity subtle,
    • danger of topic drift
    • ontology-based query expansion
    • (into large disjunctions)
    • poses efficiency challenge
    precision recall // ~Professor [...] // { Professor, Researcher, Lecturer, Scientist, Scholar, Academic, ... }[...] element gold produce Golden Delicious entity food substance solid edible fruit apple pome
  • Outline  Past • Future • Present : Matter, Antimatter, and Wormholes : From Data to Knowledge : XML and Graph IR
  • TopX: 2nd Generation XML IR ” Semantic“ XPath Full-Text query: / Article [ftcontains(// Person , ” Max Planck“)] [ftcontains(// Work , ” quantum physics“)] // Children [@ Gender = ” female“]// Birthdates supported by TopX engine: http://infao5501.ag5.mpi-sb.mpg.de:8080/topx/ http://topx.sourceforge.net
    • Exploit tags & structure for better precision
    • Can relax tag names & structure for better recall
    • Principled ranking by probabilistic IR (Okapi BM25 for XML)
    • Efficient top-k query processing (using improved TA)
    • Robust ontology integration (self-throttling to avoid topic drift)
    • Efficient query expansion (on demand, by extended TA)
    • Relevance feedback for automatic query rewriting
    [Martin Theobald, Ralf Schenkel, GW: VLDB’05, VLDB Journal]
  • Commercial Break [Martin Theobald, Ralf Schenkel, GW: VLDB’95] TopX demo today 3:30 – 5:30
  • Principled Ranking by Probabilistic IR odds for item d with terms d i being relevant for query q = {q 1 , …, q m } binary features, conditional independence of features [Robertson & Sparck-Jones 1976]
    • Now estimate p i and q i values from
      • relevance feedback,
      • pseudo-relevance feedback,
      • corpus statistics
    • by MLE (with statistical smoothing )
    • and store precomputed p i , q i in index
    „ God does not play dice.“ (Einstein) IR does. with related to but different from statistical language models Relationship to tf*idf
    • led to Okapi BM25 (wins TREC tasks)
    • adapted and extended to XML
    • in TopX, ...
  • Probabilistic Ranking for SQL SQL queries that return many answers need ranking
    • Examples:
    • Houses (Id, City, Price, #Rooms, View, Pool, SchoolDistrict, …)
    • Select * From Houses Where View = ”Lake“ And City In (”Redmond“, ”Bellevue“)
    • Movies (Id, Title, Genre, Country, Era, Format, Director, Actor1, Actor2, …)
    • Select * From Movies Where Genre = ”Romance“ And Era = ”90s“
    odds for tuple d with attributes X  Y relevant for query q: X 1 =x 1  …  X m =x m Estimate prob‘s, exploiting workload W: [S. Chaudhuri, G. Das, V. Hristidis, GW: TODS‘06]
    • Example: frequent queries
      • … Where Genre = ”Romance“ And Actor1 = ”Hugh Grant“
      • … Where Actor1 = ”Hugh Grant“ And Actor2 = ”Julia Roberts“
      • boosts HG and JR movies in ranking for Genre = ”Romance“ And Era = ”90s“
  • From Tables and Trees to Graphs Example: Conferences (CId, Title, Location, Year) Journals (JId, Title) CPublications (PId, Title, CId) JPublications (PId, Title, Vol, No, Year) Authors (PId, Person) Editors (CId, Person) Select * From * Where * Contains ” Gray, DeWitt, XML, Performance “ And Year > 95 Schema-agnostic keyword search over multiple tables : graph of tuples with foreign-key relationships as edges [BANKS, Discover, DBExplorer, KUPS, SphereSearch, BLINKS] Result is connected tree with nodes that contain as many query keywords as possible Ranking: with nodeScore based on tf*idf or prob. IR and edgeScore reflecting importance of relationships (or confidence, authority, etc.)
    • Related use cases:
    • XML beyond trees
    • RDF graphs
    • ER graphs (e.g. from IE)
    • social networks
    Top-k querying: compute best trees, e.g. Steiner trees (NP-hard)
  • The Present: Observations & Opportunities
    • Probabilistic IR and statistical language models
    • yield principled ranking and high effectiveness
    • (related to prob. relational models (Suciu, Getoor, …) but different)
    • Structural similarity and ranking
    • based on tree edit distance (FleXPath, Timber, …)
    • Aim for comprehensive XML ranking model
    • capturing content, structure, ontologies
    • Aim to generate structure skeleton
    • in XPath query from user feedback
    • Good progress on performance
    • but still many open efficiency issues
    actor movie movie plot director movie actor actor director plot ” life physicist Max Planck“ //article[//person ”Max Planck“] [//category ”physicist“] //biography
  • Outline  Past • Future  Present : Matter, Antimatter, and Wormholes : From Data to Knowledge : XML and Graph IR
  • Knowledge Queries Nobel laureate who survived both world wars and his children drama with three women making a prophecy to a British nobleman that he will become king proteins that inhibit both protease and some other enzyme connection between Thomas Mann and Goethe differences in Rembetiko music from Greece and from Turkey neutron stars with Xray bursts > 10 40 erg s -1 & black holes in 10‘‘ market impact of Web2.0 technology in December 2006 sympathy or antipathy for Germany from May to August 2006 Turn the Web, Web2.0, and Web3.0 into the world‘s most comprehensive knowledge base („ semantic DB “) ! Answer „knowledge queries“ such as:
  • Three Roads to Knowledge
    • Handcrafted High-Quality Knowledge Bases
    • (Semantic-Web-style ontologies, encyclopedias, etc.)
    • Large-scale Information Extraction & Harvesting:
    • (using pattern matching, NLP, statistical learning, etc.
    • for product search, Web entity/object search, ...)
    • Social Wisdom from Web 2.0 Communities
    • (social tagging, folksonomies, human computing,
    • e.g.: del.icio.us, flickr, answers.yahoo, iknow.baidu, ...)
    • Social Wisdom from Web 2.0 Communities
    • (social tagging, folksonomies, human computing,
    • e.g.: del.icio.us, flickr, answers.yahoo, iknow.baidu, ...)
  • High-Quality Knowledge Sources
    • universal „common-sense“ ontologies:
      • SUMO (Suggested Upper Merged Ontology): 60 000 OWL axioms
      • Cyc: 5 Mio. facts (OpenCyc: 2 Mio. facts)
    • domain-specific ontologies:
      • UMLS (Unified Medical Language System): 1 Mio. biomedical concepts
      • 135 categories, 54 relations (e.g. virus causes disease | symptom)
      • GeneOntology, etc.
    • thesauri and concept networks:
      • WordNet: 200 000 concepts (word senses) and hypernym/hyponym relations
      • can be cast into OWL-lite (or typed graph with statistical weights)
    • lexical sources:
      • Wikipedia (1.8 Mio. articles, 40 Mio. links, 100 languages) etc.
    • hand-tagged natural-language corpora:
      • TEI (Text Encoding Initiative) markup of historic encyclopedia
      • FrameNet: sentences classified into frames with semantic roles
    growing with strong momentum
  • High-Quality Knowledge Sources General-purpose thesauri and concept networks: WordNet family enzyme -- (any of several complex proteins that are produced by cells and act as catalysts in specific biochemical reactions) => protein -- (any of a large group of nitrogenous organic compounds that are essential constituents of living cells; ...) => macromolecule, supermolecule ... => organic compound -- (any compound of carbon and another element or a radical) ... => catalyst, accelerator -- ((chemistry) a substance that initiates or accelerates a chemical reaction without itself being affected) => activator -- ((biology) any agency bringing about activation; ...)
    • can be cast into
    • OWL-lite or into
    • graph, with weights for relation strengths
    • (derived from co-occurrence statistics)
  • High-Quality Knowledge Sources Wikipedia and other lexical sources
  • Exploit Hand-Crafted Knowledge {{Infobox_Scientist | name = Max Planck | birth_date = [[April 23]], [[1858]] | birth_place = [[Kiel]], [[Germany]] | death_date = [[October 4]], [[1947]] | death_place = [[Göttingen]], [[Germany]] | residence = [[Germany]] | nationality = [[Germany|German]] | field = [[Physicist]] | work_institution = [[University of Kiel]]</br> [[Humboldt-Universität zu Berlin]]</br> [[Georg-August-Universität Göttingen]] | alma_mater = [[Ludwig-Maximilians-Universität München]] | doctoral_advisor = [[Philipp von Jolly]] | doctoral_students = [[Gustav Ludwig Hertz]]</br> … | known_for = [[Planck's constant]], [[Quantum mechanics|quantum theory]] | prizes = [[Nobel Prize in Physics]] (1918) … Wikipedia, WordNet, and other lexical sources
  • YAGO: Yet Another Great Ontology [F. Suchanek, G. Kasneci, GW: WWW 2007]
    • Turn Wikipedia into explicit knowledge base (semantic DB)
    • Exploit hand-crafted categories and templates
    • Represent facts as explicit knowledge triples :
    • relation (entity1, entity2)
    • (in 1st-order logic, compatible with RDF, OWL-lite, XML, etc.)
    • Map (and disambiguate) relations into WordNet concept DAG
    entity1 entity2 relation Max_Planck Kiel bornIn Kiel City isInstanceOf Examples:
  • YAGO Knowledge Representation Entity Max_Planck April 23, 1858 Person City Country subclass Location subclass instanceOf subclass subclass bornOn “ Max Planck” means “ Dr. Planck” means subclass October 4, 1947 diedOn Kiel bornIn Nobel Prize Erwin_Planck FatherOf hasWon Scientist means “ Max Karl Ernst Ludwig Planck” Physicist instanceOf subclass Biologist subclass concepts individuals words Online access and download at http://www.mpi-inf.mpg.de/~suchanek/yago/ Accuracy: 97% Knowledge Base # Facts KnowItAll 30 000 SUMO 60 000 WordNet 200 000 OpenCyc 300 000 Cyc 5 000 000 YAGO 6 000 000
  • NAGA: Graph IR on YAGO [G. Kasneci et al.: WWW‘07] queries with regular expressions Ling $x scientist isa hasFirstName | hasLastName $y Zhejiang locatedIn * worksFor conjunctive queries Beng Chin Ooi (coAuthor | advisor) * Kiel $x scientist isa bornIn Graph-based search on YAGO-style knowledge bases with built-in ranking based on confidence and informativeness  statistical language model for result graphs
  • Ranking Factors
    • Confidence:
    • Prefer results that are likely to be correct
      • Certainty of IE
      • Authenticity and Authority of Sources
    • Informativeness:
    • Prefer results that are likely important
    • May prefer results that are likely new to user
      • Frequency in answer
      • Frequency in corpus (e.g. Web)
      • Frequency in query log
    • Compactness:
    • Prefer results that are tightly connected
      • Size of answer graph
    bornIn (Max Planck, Kiel) from „ Max Planck was born in Kiel“ (Wikipedia) livesIn (Elvis Presley, Mars) from „ They believe Elvis hides on Mars“ (Martian Bloggeria) q: isa (Einstein, $y) isa (Einstein, scientist) isa (Einstein, vegetarian) q: isa ($x, vegetarian) isa (Einstein, vegetarian) isa (Al Nobody, vegetarian) Einstein vegetarian Bohr Nobel Prize Tom Cruise 1962 isa isa bornIn diedIn won won
  • Information Extraction (IE): Text to Records combine NLP, pattern matching, lexicons, statistical learning Max Planck 4/23, 1858 Kiel Albert Einstein 3/14, 1879 Ulm Mahatma Gandhi 10/2, 1869 Porbandar Person BirthDate BirthPlace ... Person ScientificResult Max Planck Quantum Theory Person Collaborator Max Planck Albert Einstein Max Planck Niels Bohr Planck‘s constant 6.226  10 23 Js Constant Value Dimension
  • Knowledge Acquisition from the Web
    • Learn Semantic Relations from Entire Corpora at Large Scale
    • (as exhaustively as possible but with high accuracy)
    • Examples:
    • all cities, all basketball players, all composers
    • headquarters of companies, CEOs of companies, synonyms of proteins
    • birthdates of people, capitals of countries, rivers in cities
    • which musician plays which instruments
    • who discovered or invented what
    • which enzyme catalyzes which biochemical reaction
    Existing approaches and tools (Snowball [Gravano et al. 2000], KnowItAll [Etzioni et al. 2004], …): almost-unsupervised pattern matching and learning: seeds (known facts)  patterns (in text)  (extraction) rule  (new) facts
  • Methods for Web-Scale Fact Extration city(Beijing) plays(Coltrane, sax) city(Beijing) old center of Beijing plays(Coltrane, sax) sax player Coltrane city(Beijing) old center of Beijing old center of X plays(Coltrane, sax) sax player Coltrane Y player X Example: city (Seattle) in downtown Seattle city (Seattle) Seattle and other towns city (Las Vegas) Las Vegas and other towns plays (Zappa, guitar) playing guitar: … Zappa plays (Davis, trumpet) Davis … blows trumpet seeds  text  rules  new facts Example: city (Seattle) in downtown Seattle in downtown X city (Seattle) Seattle and other towns X and other towns city (Las Vegas) Las Vegas and other towns X and other towns plays (Zappa, guitar) playing guitar: … Zappa playing Y: … X plays (Davis, trumpet) Davis … blows trumpet X … blows Y Example: city (Seattle) in downtown Seattle in downtown X city (Seattle) Seattle and other towns X and other towns city (Las Vegas) Las Vegas and other towns X and other towns plays (Zappa, guitar) playing guitar: … Zappa playing Y: … X plays (Davis, trumpet) Davis … blows trumpet X … blows Y Example: city (Seattle) in downtown Seattle in downtown X city (Seattle) Seattle and other towns X and other towns city (Las Vegas) Las Vegas and other towns X and other towns plays (Zappa, guitar) playing guitar: … Zappa playing Y: … X plays (Davis, trumpet) Davis … blows trumpet X … blows Y in downtown Beijing city(Beijing) Coltrane blows sax plays(C., sax) Assessment of facts & generation of rules based on statistics Rules can be more sophisticated: playing NN: (ADJ|ADV)* NP & class(NN)=instrument & class(head(NP))=person  plays(head(NP), NN)
  • Performance of Web-IE State-of-the-art precision/recall results: Anecdotic evidence: invented (A.G. Bell, telephone) married (Hillary Clinton, Bill Clinton) isa (yoga, relaxation technique) isa ( zearalenone, mycotoxin) contains (chocolate, theobromine) contains (Singapore sling, gin) invented (Johannes Kepler, logarithm tables) married (Segolene Royal, Francois Hollande) isa (yoga, excellent way) isa (your day, good one) contains (chocolate, raisins) plays (the liver, central role) makes (everybody, mistakes) relation precision recall corpus systems countries 80% 90% Web KnowItAll cities 80% ??? Web KnowItAll scientists 60% ??? Web KnowItAll headquarters 90% 50% News Snowball, LEILA birthdates 80% 70% Wikipedia LEILA instanceOf 40% 20% Web Text2Onto, LEILA Open IE 80% ??? Web TextRunner precision value-chain: entities 80%, attributes 70%, facts 60%, events 50%
  • Beyond Surface Learning with LEILA Almost-unsupervised Statistical Learning with Dependency Parsing Limitation of surface patterns: who discovered or invented what “ Tesla ’s work formed the basis of AC electric power ” Learning to Extract Information by Linguistic Analysis [F.Suchanek, G.Ifrim, GW: KDD‘06]
    • LEILA outperforms other Web-IE methods
    • in terms of precision, recall, F1, but:
    • dependency parser is slow
    • one relation at a time
    “ Al Gore funded more work for a better basis of the Internet ” (Cologne, Rhine), (Cairo, Nile), … (Cairo, Rhine), (Rome, 0911), (  ,  [0..9]*  ), … Paris was founded on an island in the Seine (Paris, Seine) Ss Pv MVp Ds Js DG Js MVp NP VP VP PP NP NP PP NP NP Cologne lies on the banks of the Rhine Ss MVp DMc Mp Dg Js Jp NP PP VP NP PP NP NP NP People in Cairo like wine from the Rhine valley Mp Js Os Sp Mvp Ds Js AN NP NP PP VP PP NP NP NP NP
  • IE Efficiency and Accuracy Tradeoffs
    • precision vs. recall: two-stage processing (filter pipeline)
      • recall-oriented harvesting
      • precision-oriented scrutinizing
    • preprocessing
      • indexing: NLP trees & graphs, N-grams, PoS-tag patterns ?
      • exploit ontologies ? exploit usage logs ?
    • turn crawl&extract into set-oriented query processing
      • candidate finding
      • efficient phrase, pattern, and proximity queries
      • optimizing entire text-mining workflows [Ipeirotis et al.: SIGMOD‘06]
    IE is cool, but what‘s in it for DB folks? [see also tutorials by Cohen, Doan/Ramakrishnan/Vaithyanathan, Agichtein/Sarawagi]
  • The Future: Challenges
    • Generalize YAGO approach (Wikipedia + WordNet)
    • Methods for comprehensive, highly accurate
    • mappings across many knowledge sources
      • cross-lingual, cross-temporal
      • scalable in size, diversity, number of sources
    • Pursue DB support towards efficient IE (and NLP)
    • Achieve Web-scale IE throughput that can
      • sustain rate of new content production (e.g. blogs)
      • with > 90% accuracy and Wikipedia-like coverage
    • Integrate handcrafted knowledge with NLP/ML-based IE
    • Incorporate social tagging and human computing
  • Outline  Past  Future  Present : Matter, Antimatter, and Wormholes : From Data to Knowledge : XML and Graph IR
  • Major Trends in DB and IR malleable schema (later) deep NLP, adding structure record linkage info extraction graph mining entity-relationship graph IR ontologies ranking Database Systems Information Retrieval statistical language models data uncertainty programmability search as Web Service dataspaces Web objects Web 2.0 Web 2.0
  • Conclusion
    • DB&IR integration agenda:
      • models − ranking, ontologies, prob. SQL ?, graph IR ?
      • languages and APIs − XQuery Full-Text++ ?
      • systems − drop SQL, go light-weight ?
      • − combine with P2P, Deep Web, ... ?
    • Rethink progress measures and experimental methodology
    • Address killer app(s) and grand challenge(s):
      • from data to knowledge (Web, products, enterprises)
      • integrate knowledge bases , info extraction, social wisdom
      • cope with uncertainty ; ranking as first-class principle
    • Bridge cultural differences between DB and IR:
      • co-locate SIGIR and SIGMOD
  • DB&IR: Both Sides Now
    • Joni Mitchell (1969): Both Sides Now
        • I've looked at life from both sides now, From up and down, and still somehow It's life's illusions i recall. I really don't know life at all.
    Thank You ! DB&IR