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Creating and Exploiting a Web of Semantic Data

Creating and Exploiting a Web of Semantic Data. Overview. Introduction Semantic Web 101 Recent Semantic Web trends Examples: DBpedia, Wikitology Conclusion. The Age of Big Data. Massive amounts of data is available today

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Creating and Exploiting a Web of Semantic Data

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  1. Creating and Exploiting a Web of Semantic Data

  2. Overview • Introduction • Semantic Web 101 • Recent Semantic Web trends • Examples: DBpedia, Wikitology • Conclusion

  3. The Age of Big Data • Massive amounts of data is available today • Advances inmany fields driven by availability of unstructured data, e.g., text, audio, images • Increasingly, large amounts of structured and semi-structured data is also online • Much of this available in the Semantic Web language RDF, fostering integration and interoperability • Such structured data is especially important for the sciences

  4. Twenty years ago… Tim Berners-Lee’s 1989 WWW proposal described a web of rela- tionships among named objects unifying many information management tasks Capsule history • Guha’s MCF (~94) • XML+MCF=>RDF (~96) • RDF+OO=>RDFS (~99) • RDFS+KR=>DAML+OIL (00) • W3C’s SW activity (01) • W3C’s OWL (03) • SPARQL, RDFa (08) • Rules (09) http://www.w3.org/History/1989/proposal.html

  5. Ten years ago …. • The W3C started developing standards for the Semantic Web • The vision, technology and use cases are still evolving • Moving from a web of documents to a web of data

  6. Today 4.5 billion integrated facts published on the Web as RDF Linked Open Data

  7. Tomorrow Large collections of integrated facts published on the Web for many disciplines and domains

  8. W3C’s Semantic Web Goal “The Semantic Web is an extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in cooperation.” -- Berners-Lee, Hendler and Lassila, The Semantic Web, Scientific American, 2001

  9. Contrast with a non-Web approach • The W3C Semantic Web approach is • Distributed • Open • Non-proprietary • Standards based

  10. How can we share data on the Web? • POX, Plain Old XML, is one approach, but it has deficiencies • The Semantic Web languages RDF and OWL offer a simpler and more abstract data model (a graph) that is better for integration • Its well defined semantics supports knowledge modeling and inference • Supported by a stable, funded standards organization, the World Wide Web Consortium

  11. Simple RDF Example http://umbc.edu/~finin/talks/idm02/ dc:Title “Intelligent Information Systemson the Web and in the Aether” dc:Creator Note: “blank node” bib:Aff bib:email http://umbc.edu/ bib:name “finin@umbc.edu” “Tim Finin”

  12. The RDF Data Model • An RDF document is an unordered collection of statements, each with a subject, predicate and object • Such triples can be thought of as a labelled arc in a graph • Statements describe properties of resources • A resource is any object that can be referenced or denoted by a URI • Properties themselves are also resources (URIs) • Dereferencing a URI produces useful additional information, e.g., a definition or additional facts

  13. RDF is the first SW language Graph XML Encoding RDF Data Model <rdf:RDF ……..> <….> <….> </rdf:RDF> Good for human viewing Good for Machineprocessing Triples stmt(docInst, rdf_type, Document) stmt(personInst, rdf_type, Person) stmt(inroomInst, rdf_type, InRoom) stmt(personInst, holding, docInst) stmt(inroomInst, person, personInst) RDF is a simple language for graph based representations Good for storage and reasoning

  14. XML encoding for RDF <rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:bib="http://daml.umbc.edu/ontologies/bib/"> <description about="http://umbc.edu/~finin/talks/idm02/"> <dc:title>Intelligent Information … and in the Aether</dc:Title> <dc:creator> <description> <bib:Name>Tim Finin</bib:Name> <bib:Email>finin@umbc.edu</bib:Email> <bib:Aff resource="http://umbc.edu/" /> </description> </dc:Creator> </description> </rdf:RDF> http://umbc.edu/~finin/talks/idm02/ dc:Title “Intelligent Information Systemson the Web and in the Aether” dc:Creator bib:Aff bib:email http://umbc.edu/ bib:name “finin@umbc.edu” “Tim Finin”

  15. N3 is a friendlier encoding @prefix rdf: http://www.w3.org/1999/02/22-rdf-syntax-ns# . @prefix dc: http://purl.org/dc/elements/1.1/ . @prefix bib: http://daml.umbc.edu/ontologies/bib/ . <http://umbc.edu/~finin/talks/idm02/> dc:title "Intelligent ... and in the Aether" ; dc:creator [ bib:Name "Tim Finin"; bib:Email "finin@umbc.edu" bib:Aff: "http://umbc.edu/" ] . http://umbc.edu/~finin/talks/idm02/ dc:Title “Intelligent Information Systemson the Web and in the Aether” dc:Creator bib:Aff bib:email http://umbc.edu/ bib:name “finin@umbc.edu” “Tim Finin”

  16. RDFS supports simple inferences • RDF Schema adds vocabulary for classes, properties & constraints • An RDF ontology plus some RDF statements may imply additional RDF statements (not possible in XML) • Note that this is part of the data model and not of the accessing or processing code. • @prefix rdfs: <http://www.....>. • @prefix : <genesis.n3>. • parent a rdf: property; • rdfs:domain person; • rdfs:range person. • mother rdfs:subProperty parent; • rdfs:domain woman; • rdfs:range person. • eve mother cain. person a class. woman subClass person. mother a property. eve a person; a woman; parent cain. cain a person.

  17. OWL adds further richness OWL adds richer representational vocabulary, e.g. • parentOf is the inverse of childOf • Every person has exactly one mother • Every person is a man or a woman but not both • A man is the equivalent of a person with a sex property with value “male” OWL is based on ‘description logic’ – a logic subset with efficient reasoners that are complete • Good algorithms for reasoning about descriptions

  18. That was then, this is now • 1996-2000: focus on RDF and data • 2000-2007: focus on OWL, developing ontologies, sophisticated reasoning • 2008-…: Integrating and exploiting large RDF data collections backed by lightweight ontologies

  19. A Linked Data story • Wikipedia as a source of knowledge • Wikis are a great ways to collaborateon building up knowledge resources • Wikipedia as an ontology • Every Wikipedia page is a concept or object • Wikipedia as RDF data • Map this ontology into RDF • DBpedia as the lynchpin for Linked Data • Exploit its breadth of coverage to integrate things

  20. Populating Freebase KB

  21. Underlying Powerset’s KB

  22. Mined by TrueKnowledge

  23. Wikipedia as an ontology • Using Wikipedia as an ontology • each article (~3M) is an ontology concept or instance • terms linked via category system (~200k), infobox template use, inter-article links, infobox links • Article history contains metadata for trust, provenance, etc. • It’s a consensus ontology with broad coverage • Created and maintained by a diverse community for free! • Multilingual • Very current • Overall content quality is high

  24. Wikipedia as an ontology • Uncategorized and miscategorized articles • Many ‘administrative’ categories: articles needing revision; useless ones: 1949 births • Multiple infobox templates for the same class • Multiple infobox attribute names for same property • No datatypes or domains for infobox attribute values • etc.

  25. Dbpedia : Wikipedia in RDF • A community effort to extractstructured information fromWikipedia and publish as RDFon the Web • Effort started in 2006 with EU funding • Data and software open sourced • DBpedia doesn’t extract information from Wikipedia’s text, but from the its structured information, e.g., links, categories, infoboxes

  26. DBpedia: Linked Data lynchpin

  27. http://lookup.dbpedia.org/

  28. Dbpedia uses WP structured data DBpedia extracts structured data from Wikipedia, especially from Infoboxes

  29. Dbpedia ontology • Dbpedia 3.2 (Nov 2008) added a manually constructed ontology with • 170 classes in a subsumption hierarchy • 880K instances • 940 properties with domain and range • A partial, manual mapping was constructed from infobox attributes to these term • Current domain and range constraints are “loose” • Namespace: http://dbpedia.org/ontology/ Place 248,000 Person 214,000 Work 193,000 Species 90,000 Org. 76,000 Building 23,000

  30. Person 56 properties

  31. Organisation 50 properties

  32. Place 110 properties

  33. PREFIX dbp: <http://dbpedia.org/resource/> PREFIX dbpo: <http://dbpedia.org/ontology/> SELECT distinct ?Property ?Place WHERE {dbp:Barack_Obama ?Property ?Place . ?Place rdf:type dbpo:Place .} http://dbpedia.org/sparql/

  34. DBpedia: Linked Data lynchpin

  35. Consider Baltimore, MD

  36. Looking at the RDF description We find assertions equating DBpedia's object for Baltimore with those in other LOD datasets: dbpedia:Baltimore%2C_Maryland owl:sameAs census:us/md/counties/baltimore/baltimore; owl:sameAs cyc:concept/Mx4rvVin-5wpEbGdrcN5Y29ycA; owl:sameAs freebase:guid.9202a8c04000641f800000000004921a; owl:sameAs geonames:4347778/ . Since owl:sameAs is defined as an equivalence relation, the mapping works both ways

  37. Linked Data Cloud, March 2009

  38. Four principles for linked data • Use URIs to identify things that you expose to the Web as resources • Use HTTP URIs so that people can locate and look up (dereference) these things. • When someone looks up a URI, provide useful information • Include links to other, related URIs in the exposed data as a means of improving information discovery on the Web -- Tim Berners-Lee, 2006

  39. 4.5 billion triples for free • The full public LOD dataset has about 4.5 billion triples as of March 2009 • Linking assertions are spotty, but probably include order 10M equivalences • Availability: • download the data in RDF • Query it via a public SPARQL servers • load it as an Amazon EC2 public dataset • Launch it and required software as an Amazon public AMI image

  40. Wikitology We’ve been exploring a different approach to derive an ontology from Wikipedia through a series of use cases: • Identifying user context in a collaboration system from documents viewed (2006) • Improve IR accuracy by adding Wikitology tags to documents (2007) • ACE: cross document co-reference resolution for named entities in text (2008) • TAC KBP: Knowledge Base population from text (2009) • Improve Web search engine by tagging documents and queries (2009)

  41. Wikitology 2.0 (2008) RDF RDF graphs text Freebase KB Yago WordNet Databases Human input & editing

  42. Wikitology tagging • Using Serif’s output, we produced an entity document for each entity. Included the entity’s name, nominal and pronominal mentions, APF type and subtype, and words in a window around the mentions • We tagged entity documents using Wiki-tology producing vectors of (1) terms and (2) categories for the entity • We used the vectors to compute features measuring entity pair similarity/dissimilarity

  43. Wikitology Entity Document & Tags Wikitology article tag vector Webster_Hubbell 1.000 Hubbell_Trading_Post National Historic Site 0.379 United_States_v._Hubbell 0.377 Hubbell_Center 0.226 Whitewater_controversy 0.222 Wikitology category tag vector Clinton_administration_controversies 0.204 American_political_scandals 0.204 Living_people 0.201 1949_births 0.167 People_from_Arkansas 0.167 Arkansas_politicians 0.167 American_tax_evaders 0.167 Arkansas_lawyers 0.167 Name Type & subtype Mention heads Words surrounding mentions Wikitology entity document <DOC> <DOCNO>ABC19980430.1830.0091.LDC2000T44-E2 <DOCNO> <TEXT> Webb Hubbell PER Individual NAM: "Hubbell” "Hubbells” "Webb Hubbell” "Webb_Hubbell" PRO: "he” "him” "his" abc's accountant after again ago all alleges alone also and arranged attorney avoid been before being betray but came can cat charges cheating circle clearly close concluded conspiracy cooperate counsel counsel's department did disgrace do dog dollars earned eightynine enough evasion feel financial firm first four friend friends going got grand happening has he help him hi s hope house hubbell hubbells hundred hush income increase independent indict indicted indictment inner investigating jackie jackie_judd jail jordan judd jury justice kantor ken knew lady late law left lie little make many mickey mid money mr my nineteen nineties ninetyfour not nothing now office other others paying peter_jennings president's pressure pressured probe prosecutors questions reported reveal rock saddened said schemed seen seven since starr statement such tax taxes tell them they thousand time today ultimately vernon washington webb webb_hubbell were what's whether which white whitewater why wife years </TEXT> </DOC>

  44. Top Ten Features (by F1)

  45. Knowledge Base Population • The 2009 NIST Text Analysis Conference (TAC) will include a new Knowledge Base Population track • Goal: discover information about named entities (people, organizations, places) and incorporate it into a KB • TAC KBP has two related tasks: • Entity linking: doc. entity mention -> KB entity • Slot filling: given a document entity mention, find missing slot values in large corpus

  46. KBs and IE are Symbiotic KB info helps interpret text KnowledgeBase Information Extraction from Text IE helps populate KBs

  47. Wikitology 3.0 (2009) Articles IRcollection Application Specific Algorithms CategoryLinks Graph Infobox Graph WikitologyCode Application Specific Algorithms Infobox Graph Page LinkGraph RDFreasoner Application Specific Algorithms Relational Database TripleStore LinkedSemanticWeb data &ontologies

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