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Introduction to linked data; discussion of the challenges in using and integrating it; introducing the concept of reason-able views to LOD; FactForge - the fast track to the centre of the data web; ...

Introduction to linked data; discussion of the challenges in using and integrating it; introducing the concept of reason-able views to LOD; FactForge - the fast track to the centre of the data web; linked-data-friendly features in BigOWLIM

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Linked Data Management Presentation Transcript

  • 1. Linked Data Management enabling Searching and Querying Billions of Facts on the Web Also, showing How linked data establish Nietzsche as the most popular German entertainer Apr 2011
  • 2. Presentation Outline
    • Linked Data Introduction
    • Promises and Challenges
    • Reason-able Views to LOD
    • FactForge: the largest reason-able fact collection
    • Tips and tricks behind FactForge
    Apr 2011 # Linked Data Management
  • 3. Linked Data
    • The notion of “linked data” is defined by Tim Berners-Lee in http://www.w3.org/DesignIssues/LinkedData.html
    • It outlines an approach for bootstrapping a web of data, a prerequisite for the Semantic Web
    • It prescribes that
      • Data should be published on the WWW as RDF graphs
        • the basic Semantic Web representation format
      • In such a way that one can explore them across servers by following the links in the graph
        • in a manner similar to the way the HTML Web is navigated
      • It is viewed as a method for sharing and connecting pieces of data, information, and knowledge on the Semantic Web using URIs and RDF
    Apr 2011 # Linked Data Management
  • 4. Linked Data (2)
    • Technically, “linked data” are constituted by publishing and interlinking open data sources, following 4 principles. These are:
      • Using URIs (globally unique identifiers) as names for things
      • Using HTTP URIs, so that people can look up those names
      • Providing useful information when someone looks up a URI
        • To be concrete, linked data publishers should make sure that HTTP GET with a URI from the RDF graph returns the description of the resource, i.e. the set of statements where it appears as a subject (also known as RDF molecule)
      • Including links to other URI from other datasets, so people can discover more things
    Apr 2011 # Linked Data Management
  • 5. Linking Data Across Different Servers Apr 2011 rdfs:subClassOf rdfs:subClassOf rdf: type rdf: type rdf: type rdf: type rdf: type rdf: type # Linked Data Management myData: Maria ptop:childOf rdfs:subClassOf ptop:Agent ptop:Person ptop:Woman ptop:childOf ptop:parentOf rdfs:range owl:inverseOf inferred ptop:parentOf myData:Ivan owl:relativeOf owl:inverseOf owl:SymmetricProperty rdfs:subPropertyOf ptop:relativeOf owl:inverseOf owl:inverseOf
  • 6. Linking Open Data (LOD)
    • Linking Open Data W3C SWEO Community project http://esw.w3.org/topic/SweoIG/TaskForces/CommunityProjects/LinkingOpenData
    • Initiative for publishing “linked data” which already includes 50+ interlinked datasets and about 15B facts
    Apr 2011 # Linked Data Management
  • 7. Presentation Outline
    • Linked Data Introduction
    • Promises and Challenges
    • Reason-able Views to LOD
    • FactForge: the largest reason-able fact collection
    • Tips and tricks behind FactForge
    Apr 2011 # Linked Data Management
  • 8. Why Do People Not Use Linked Data?
    • Plenty of people in the IT world have heard about linked data and like the idea
    • However, the impact of linked data in enterprises is still very limited
    • Because:
      • There are no well established opinions about what linked data can “buy” for the enterprise or best practices for using it
        • What are the concrete benefits?
      • It is not clear what it would cost
        • What are the problems?
        • What are the associated risks?
    Apr 2011 # Linked Data Management
  • 9. Linked Data in the Enterprise: Why?
    • To facilitate data integration
      • One can use LOD as an “interlingua” for enterprise data integration
      • Additional public information can help alignment and linking
    • To add value to proprietary data
      • Public data can allow more analytics on top of proprietary data
      • For instance, by linking to spatial data from Geonames
      • Better description and access to content, e.g. search for images
    • Make enterprise data more open
      • To make enterprise data easier to use outside the enterprise
      • Public identifiers and vocabularies can be used to access them
    Apr 2011 # Linked Data Management
  • 10. Linked Data in the Enterprise: Challenges
    • LOD is hard to comprehend
      • Diversity comes at a price
      • One needs to make a query against 200 different schemata and hundreds of thousands of classes and properties
    • LOD is unreliable
      • Most of the servers behind LOD today are slow
        • High down time
      • Dealing with data distributed on the web is slow
        • A federated SPARQL query that uses 2-3 servers within several joins can be *very* slow
      • No kind of consistency is guaranteed
        • Low commitment to the formal semantics and intended usage of the ontologies and schemata
    Apr 2011 # Linked Data Management
  • 11. Presentation Outline
    • Linked Data Introduction
    • Promises and Challenges
    • Reason-able Views to LOD
    • FactForge: the largest reason-able fact collection
    • Tips and tricks behind FactForge
    Apr 2011 # Linked Data Management
  • 12. Reason-able Views to the Web of Data
    • Reason-able views represent an approach for reasoning and management of linked data
    • Key ideas:
      • Group selected datasets and ontologies in a compound dataset
        • Clean up, post-process and enrich the datasets if necessary
        • Do this conservatively, in a clearly documented and automated manner, so that:
          • the operation can easily be performed each time a new version of one of the datasets is published
          • Users can easily understand the intervention made to the original dataset
      • Load the compound dataset in a single semantic repository
      • Perform inference with respect to tractable OWL dialects
      • Define a set of sample queries against the compound dataset
        • These determine the “level of service” or the “scope of consistency” contract offered by the reason-able view
    Apr 2011 # Linked Data Management
  • 13. Reason-able Views: Objectives
    • Make reasoning and query evaluation feasible
    • Guarantee a basic level of consistency
      • The sample queries guarantee the consistency of the data in the same way in which regression tests do for the quality of software
    • Guarantee availability
      • In the same way in which web search engines are usually more reliable than most of the web sites; they also to caching
    • Easier exploration and querying of unseen data
      • Lower the cost of entry through URI auto-complete and RDF search
      • Sample queries provide re-usable extraction patterns, which reduce the time for acquaintance with the datasets and their interconnections
    Apr 2011 # Linked Data Management
  • 14. Two Reason-able Views to the Web of Linked Data
    • FactForge (indicated in red on the next slide)
      • Some of the central LOD datasets
      • General-purpose information (not specific to a domain)
      • 1.2B explicit plus .9B inferred indexed, 10B retrievable statements
      • The largest upper-level knowledge base
      • http://www.factforge.net/
    • Linked Life Data (indicated in yellow)
      • 25 of the most popular life-science datasets
      • Complemented by gluing ontologies
      • 2.7B explicit and 1.4B inferred, total of 4.1B indexed statements
      • The largest non-synthetic dataset that was used for reasoning
      • http://www.linkedlifedata.com
    Apr 2011 # Linked Data Management
  • 15. Linking Open Data Datasets and Views (red and yellow) Apr 2011 # Linked Data Management
  • 16. Presentation Outline
    • Linked Data Introduction
    • Promises and Challenges
    • Reason-able Views to LOD
    • FactForge: the largest reason-able fact collection
    • Tips and tricks behind FactForge
    Apr 2011 # Linked Data Management
  • 17. FactForge: Fast Track to the Center of the Web of Data
    • Datasets : DBPedia, Freebase, Geonames, UMBEL, MusicBrainz, Wordnet, CIA World Factbook, Lingvoj
    • Ontologies: Dublin Core, SKOS, RSS, FOAF
    • Inference: materialization with respect to OWL 2 RL
      • Seems to completely cover the semantics of the data
      • owl:sameAs optimization in BigOWLIM allows reduction of the indices without loss of semantics, but big gains in performance
    • Free public service at http://www.factforge.net ,
      • Incremental URI auto-suggest
      • Query and explore through Forest and Tabulator, RelFinder
      • RDF Search : retrieve ranked list of URIs by keywords (see slide 26)
      • SPARQL end-point
    Apr 2011 # Linked Data Management
  • 18. Guess who is the most popular German entertainer?
    • PREFIX rdf: … (run the query at http://factforge.net/sparql )
    • SELECT * WHERE {
    • ?Person dbp-ont: birthPlace ?BirthPlace ;
    • rdf: type opencyc: Entertainer ;
    • ff: hasRDFRank ?RR .
    • ?BirthPlace geo-ont: parentFeature dbpedia: Germany .
    • } ORDER BY DESC(?RR) LIMIT 100
    • Without FF, answering such queries in real time is impossible:
    • Used data from: DBPedia, Geonames, UMBEL and MusicBrainz
    • Inference over types, sub-classes, and transitive relationships
    • The most popular entertainer born in Germany is:
      • Asking factual questions to a global KB can bring unexpected and strange results
      • We ask who is the most popular person, who qualifies as an entertainer
      • It uses a simple notion of popularity: RDFRank
    Apr 2011 F. Nietzsche # Linked Data Management
  • 19. The Modigliani Test for the Semantic Web
    • ReadWriteWeb’s founder Richard McManus: “… the tipping point for the Semantic Web may be when one can … deliver – using Linked Data – a comprehensive list of locations of original Modigliani art works … ”
    • http://www.readwriteweb.com/archives/the_modigliani_test_for_linked_data.php
    Apr 2011 # Linked Data Management
  • 20. The Modigliani Test: FactForge Results
  • 21. The FactForge Query Passing the Modigliani Test
    • PREFIX fb: … (check the query at http://factforge.net/sparql )
    • SELECT DISTINCT
    • ?painting_l ?owner_l ?city_fb_con ?city_db_loc ?city_db_cit
    • WHERE {
    • ?p fb: visual_art.artwork. artist dbpedia: Amedeo_Modigliani ;
    • fb: visual_art.artwork. owners [
    • fb: visual_art.artwork_owner_relationship. owner ?ow ] ;
    • ff: preferredLabel ?painting_l.
    • ?ow ff: preferredLabel ?owner_l .
    • OPTIONAL { ?ow fb: location. location.containedby [
    • ff: preferredLabel ?city_fb_con ] } .
    • OPTIONAL { ?ow dbp-prop: location ?loc.
    • ?loc rdf: type umbel-sc: City ;
    • ff: preferredLabel ?city_db_loc }
    • OPTIONAL { ?ow dbp-ont: city [ ff: preferredLabel ?city_db_cit ] }
    • }
    Apr 2011 # Linked Data Management
  • 22. FactForge Loading and Inference Statistics Apr 2011 # Linked Data Management Dataset Explicit Indexed Triples ('000) Inferred Indexed Triples ('000) Total # of Stored Triples ('000) Entities ('000 of nodes in the graph) Inferred closure ratio Sechmata and ontologies 11 7 18 6 0.6 DBpedia (categories) 2,877 42,587 45,464 1,144 14.8 DBpedia (sameAs) 5,544 566 6,110 8,464 0.1 UMBEL 5,162 42,212 47,374 500 8.2 Lingvoj 20 863 883 18 43.8 CIA Factbook 76 4 80 25 0.1 Wordnet 2,281 9,296 11,577 830 4.1 Geonames 91,908 125,025 216,933 33,382 1.4 DBpedia core 560,096 198,043 758,139 127,931 0.4 Freebase 463,689 40,840 504,529 94,810 0.1 MusicBrainz 45,536 421,093 466,630 15,595 9.2 Total 1,177,961 881,224 2,058,185 283,253 0.7
  • 23. Post-processing
    • Several kinds of post-processing were performed to allow easier navigation and browsing
    • For each URI we generate:
      • Preferred label , used for visualisation instead of the URI
      • Preferred image
      • Text snippet
      • RDF Rank , similar to Google’s PageRank
      • These are available as auxiliary statements, retrievable through system predicates
        • E.g. ff: preferredLabel and om: hasRDFRank
    Apr 2011 # Linked Data Management
  • 24. Final FactForge Statistics
      • Number of entities (RDF graph nodes): 405M
      • Number of inserted statements (NIS): 1,3B
        • Explicit statements from 3 rd party datasets: 1,2B
        • Auxiliary statements added after postprocessomg: ~120M
      • Number of stored statements (NSS): 2.2B
        • Including statements that were inferred and materialized: 0.9B
      • Number of retrievable statements (NRS): 9.8B
        • Statements “compressed” through BigOWLIM’s owl:sameAs optimisation (see slide #46): 7.6B
    Apr 2011 # Linked Data Management
  • 25. FactForge Provides Unique Query Capabilities
    • Unmatched factual knowledge querying capabilities
      • Scope : the largest and most diverse integrated dataset, including 8 of the central LOD datasets
      • Analytical power : the semantics of the data and links between them is fully accounted for during query evaluation
      • RDFRank indicates importance in the integrated dataset
    • No other service supports even one of the following:
      • Query evaluation against DBPedia, Freebase or Geonames considering their semantics
      • Allows simultaneous querying of multiple datasets, considering the semantics of the links between them
    Apr 2011 # Linked Data Management
  • 26. Presentation Outline
    • Linked Data Introduction
    • Promises and Challenges
    • Reason-able Views to LOD
    • FactForge: the largest reason-able fact collection
    • Tips and tricks behind FactForge
    Apr 2011 # Linked Data Management
  • 27. Linked-Data-Friendly BigOWLIM Features
    • There is a set of advanced features of BigOWLIM which were inspired by the development of FactForge and LinkedLifeData
    • Now, those are available in the BigOWLIM and appear to be useful even in projects not related to linked data
    • Two of those are presented in the following slides:
      • RDF Search
      • owl:sameAs optimisation
    Apr 2011 # Linked Data Management
  • 28. RDF Search - Advanced FTS in RDF Graphs
    • Objective:
      • Be able to search in an RDF graph by keywords
      • Get usable results (standalone literals are marginally useful)
    • What and how to index:
      • Index URIs
      • Acquire text representation for each URI, by collecting the text from its “RDF molecule”
        • RDF Molecule: the description of the node, including all outgoing statements
      • Index the text representations with standard FTS methods
    • What to return as result:
      • List of URIs, ranked by FTS + RDFRank metric
      • Present them with human-readable labels and text snippets
    Apr 2011 # Linked Data Management
  • 29.
    • The ranking is based on the standard vector-space-model relevance, boosted by RDFRank
    • Sample Query
    Apr 2011 RDF Search – Advanced FTS in RDF Graphs (2) PREFIX gossip: <http://www.....gossipdb.owl#> PREFIX onto: <http://www.ontotext.com/> SELECT * WHERE { ?person gossip:name ?name . ?name onto:luceneQuery &quot;American AND life~&quot; . } # Linked Data Management
  • 30.
    • owl:sameAs declares that two different URIs denote one and the same resource or object in the world
      • Most often, it is used to align different identifiers of the same real-world entity used in different data sources
    • Example, encoding that there are three different URIs for Bulgaria and two for Sofia (that is part of Bulgaria)
    Apr 2011 The Honey of owl:sameAs
      • dbpedia: Sofia owl: sameAs geonames: 727011
      • geonames: 727011 geo-ont: parentFeature geonames: 732800
      • dbpedia: Bulgaria owl: sameAs geonames: 732800
      • dbpedia: Bulgaria owl: sameAs opencyc-en:Bulgaria,
    # Linked Data Management
  • 31.
    • According to the standard semantics of owl:sameAs:
      • It is a transitive and symmetric relationship
      • Statements, asserted using one of the equivalent URIs should be inferred to appear with all equivalent URIs placed in the same position
      • Thus the 4 statements in the example lead to 10 inferred statements :
    Apr 2011 The Sting of owl:sameAs
      • geonames:727011 owl:sameAs dbpedia:Sofia
      • geonames:732800 owl:sameAs dbpedia:Bulgaria
      • geonames:732800 owl:sameAs opencyc-en:Bulgaria
      • opencyc-en:Bulgaria owl:sameAs dbpedia:Bulgaria
      • opencyc-en:Bulgaria owl:sameAs geonames:732800
      • dbpedia:Sofia geo-ont:parentFeature geonames:732800
      • dbpedia:Sofia geo-ont:parentFeature opencyc-en:Bulgaria
      • dbpedia:Sofia geo-ont:parentFeature dbpedia:Bulgaria
      • geonames:727011 geo-ont:parentFeature opencyc-en:Bulgaria
      • geonames:727011 geo-ont:parentFeature dbpedia:Bulgaria
    # Linked Data Management
  • 32. Apr 2011 The Honey and the Sting of owl:sameAs E11 E22 E12 E21 E23 # Linked Data Management
  • 33. Apr 2011 The Honey and the Sting of owl:sameAs E11 E22 E12 E21 E23 # Linked Data Management
  • 34.
    • BigOWLIM features an optimisation that allows it to use a single master-node in its indices to represent a class of sameAs-equivalent URIs
    • Avoids inflating the indices with multiple equivalent statements
      • Imagine a statement, which has 5 sameAs-equivalents of its object, 2 of its predicate, and 3 of its object. Such statement would have 30 replicas in the indices after forward-chaining if such an optimisation is not used
    • Helps presenting compact query results
        • The sameAs equivalence can result in multiplication of the bindings of the variables in the process of query evaluation with both forward- and backward-chaining. This leads to expansion of the result-set with rows which differ only by referring to different URIs of one and the same class
        • Optionally query results can be expanded, as if there is no optimisation
    Apr 2011 owl:sameAs Optimisation # Linked Data Management
  • 35.
    • The owl:sameAs optimisation is carefully designed and implemented to make sure that:
      • All the inferences that follow from the application of the standard owl:sameAs semantics are inferable with the optimisation also
      • One can correctly determine the “original” version of the statement, i.e. which URIs were used when the statement was asserted
      • One can still get all the variations of all statements, if desired
        • the standard semantics can be simulated upon retrieval in a manner which makes the owl:sameAs an implementation detail which is transparent to end users who are not worried about expanded result sets
    • Without this optimisation reasoning with linked data becomes inefficient and the query results become overly inflated
    Apr 2011 owl:sameAs Optimisation # Linked Data Management
  • 36. Thank you!
    • We develop core semantic technology
    • Ontotext invested 200 person-years, partnered with 100 leading groups,
    • created some of the most popular tools, and delivered multiple solutions.
    • We know what works and what doesn’t
    • Ontotext set many benchmarks and advanced the frontiers of the semantic databases.
    • We invented the “semantic annotation” – linking text with data
    • Now we are prepared to
    • interlink your data, your content, and the web
    Apr 2011 # Linked Data Management