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 in many 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. From a Web of linked documents

10. To a Web of linked data

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

12. 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

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

14. 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

15. RDF is the first SW language XML Encoding Graph stmt(docInst, rdf_type, Document) stmt(personInst, rdf_type, Person) stmt(inroomInst, rdf_type, InRoom) stmt(personInst, holding, docInst) stmt(inroomInst, person, personInst) Triples RDF Data Model Good for Machine processing Good for human viewing Good for storage and reasoning RDF is a simple language for graph based representations

16. 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/"> Intelligent Information … and in the Aether Tim Finin finin@umbc.edu http://umbc.edu/ ~finin/talks/idm02/ “Intelligent Information Systems on the Web and in the Aether” http://umbc.edu/ dc:Title dc:Creator bib:Aff “Tim Finin” “finin@umbc.edu” bib:name bib:email

17. 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/. 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/ “Intelligent Information Systems on the Web and in the Aether” http://umbc.edu/ dc:Title dc:Creator bib:Aff “Tim Finin” “finin@umbc.edu” bib:name bib:email

18. 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:. @prefix :. 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.

19. 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

20. 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

21. A Linked Data story Wikipedia as a source of knowledge – Wikis are a great ways to collaborate on 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

22. Populating Freebase KB

23. Underlying Powerset’s KB

24. Mined by TrueKnowledge

25. 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

26. 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.

27. Dbpedia : Wikipedia in RDF A community effort to extract structured information from Wikipedia and publish as RDF on 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

28. DBpedia: Linked Data lynchpin

29. http://lookup.dbpedia.org/

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

34. 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/http://dbpedia.org/ontology/ Place248,000 Person 214,000 Work 193,000 Species 90,000 Org. 76,000 Building 23,000

35. Person 56 properties

36. Organisation 50 properties

37. Place 110 properties

38. http://dbpedia.org/sparql/ PREFIX dbp: PREFIX dbpo: SELECT distinct ?Property ?Place WHERE {dbp:Barack_Obama ?Property ?Place. ?Place rdf:type dbpo:Place.}

39. DBpedia: Linked Data lynchpin

40. Consider Baltimore, MD

41. 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

42. Linked Data Cloud, March 2009

43. 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

44. 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

45. 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)

46. Wikitology 2.0 (2008) WordNet Yago Human input & editingDatabases Freebase KB RDF textgraphs

47. 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

48. Wikitology Entity Document & Tags Wikitology entity document ABC19980430.1830.0091.LDC2000T44-E2 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 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

49. Top Ten Features (by F1) Prec.RecallF1 Feature Description 90.8%76.6%83.1% some NAM mention has an exact match 92.9%71.6%80.9% Dice score of NAM strings (based on the intersection of NAM strings, not words or n-grams of NAM strings) 95.1%65.0%77.2% the/a longest NAM mention is an exact match 86.9%66.2%75.1% Similarity based on cosine similarity of Wikitology Article Medium article tag vector 86.1%65.4%74.3% Similarity based on cosine similarity of Wikitology Article Long article tag vector 64.8%82.9%72.8% Dice score of character bigrams from the 'longest' NAM string 95.9%56.2%70.9% all NAM mentions have an exact match in the other pair 85.3%52.5%65.0% Similarity based on a match of entities' top Wikitology article tag 85.3%52.3%64.8% Similarity based on a match of entities' top Wikitology article tag 85.7%32.9%47.5% Pair has a known alias

50. 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

51. KBs and IE are Symbiotic Knowledge Base Information Extraction from Text KB info helps interpret text IE helps populate KBs

52. Infobox Graph Infobox Graph IR collection Relational Database Relational Database Triple Store RDF reasoner Page Link Graph Category Links Graph Category Links Graph Articles Wikitology Code Application Specific Algorithms Application Specific Algorithms Application Specific Algorithms Application Specific Algorithms Application Specific Algorithms Application Specific Algorithms Wikitology 3.0 (2009) Linked Semantic Web data & ontologies Infobox Graph Infobox Graph

53. Wikipedia’s social network Wikipedia has an implicit ‘social network’ that can help disambiguate PER mentions Resolving PER mentions in a short document to KB people who are linked in the KB is good The same can be done for the network of ORG and GPE entities

54. WSN Data We extracted 213K people from the DBpedia’s Infobox dataset, ~30K of which participate in an infobox link to another person We extracted 875K people from Freebase, 616K of were linked to Wikipedia pages, 431K of which are in one of 4.8M person-person article links Consider a document that mentions two people: George Bush and Mr. Quayle

55. Which Bush & which Quayle? Six George BushesNine Male Quayles

56. A simple closeness metric Let Si = {two hop neighbors of Si} Cij = |intersection(Si,Sj)| / |union(Si,Sj) | Cij>0 for six of the 56 possible pairs 0.43 George_H._W._Bush -- Dan_Quayle 0.24 George_W._Bush -- Dan_Quayle 0.18 George_Bush_(biblical_scholar) -- Dan_Quayle 0.02 George_Bush_(biblical_scholar) -- James_C._Quayle 0.02 George_H._W._Bush -- Anthony_Quayle 0.01 George_H._W._Bush -- James_C._Quayle

57. Application to TAC KBP Using entity network data extracted from Dbpedia and Wikipedia provides evidence to support KBP tasks: – Mapping document mentions into infobox entities – Mapping potential slot fillers into infobox entities – Evaluating the coherence of entities as potential slot fillers

58. Conclusion The Semantic Web approach is a powerful approach for data interoperability and integration The research focus is shifting to a “Web of Data” perspective Many research issue remain: uncertainty, provenance, trust, parallel graph algorithms, reasoning over billions of triples, user-friendly tools, etc. Just as the Web enhances human intelligence, the Semantic Web will enhance machine intelligence The ideas and technology are still evolving

59. http://ebiquity.umbc.edu/