2011 03 11 (upm) emadrid lsanchez uc3m anotación semántica de texto

1. Semanticannotation of text: techniques and applications Prof. Luis Sanchez-Fernandez Web Technologies LaboratoryUniversity Carlos III of Madrid http://webtlab.it.uc3m.es 1 http://webtlab.it.uc3m.es

2. Semantic Web Techniquesforsemanticannotation of text AnapproachtonamedentitydisambiguationusingWikipedia 2 Outline http://webtlab.it.uc3m.es

3. Short history of the Web 1990: Creation of WorldWide Web infraestructure at CERN by Tim Berners-Lee HTTP, HTML, first Web client, first Web server 1993: Mosaic, firstgraphic Web client 1994: Netscape Navigator 1996: Commercial use of WWWisgeneralized 1999: Tim Berners-Lee proposestheSemantic Web 2002: Weblogs and RSS  Web 2.0 6thOctober 2009: at least 8 billionindexable Web pages 23rdSeptember 2010: at least 15 billionindexable Web pages accordingtohttp://www.worldwidewebsize.com/

4. The problem of information overload The great success of the web has lead to one of its current problems: information overload Difficult and time costly to find and update relevant information for people and companies Ex.: keep an updated state of the art Company employees can use up to 20% of their working time searching in the Web (Outsell Inc, 2002)

5. Thegoal of theSemantic Web istoautomate web tasksbyenrichingthecurrent Web contentwith formal representationsthatenablebettercooperationbetweenhumans and computers http://webtlab.it.uc3m.es 5 TheSemantic Web proposal

6. http://webtlab.it.uc3m.es 6 Semantic Web Stack

7. Máster interuniversitario en Ingeniería Telemática 7 RDF “ResourceDescription Framework” (RDF) Goal of RDF (alternativeviews): Languageforresourcedescription in the Web Languagefor formal representation of (parts of) informationavailable in a Web document (metadata) Formal => machine readable Vocabularydefinedwithontologies Whatis a resource? Web content: Web pages, images, e-mails, files, … Resourcesmentioned in Web content: Persons, locations, organizations, …

8. Máster interuniversitario en Ingeniería Telemática 8 RDF basic principles We want to represent a piece of information available in the Web describing a resource Each metadata states a property that can be modelled as a (formal) statement, composed of: subject: resource being described predicate: property of the resource object: value of the property for the resource being described “http://www.example.org has a creator whose value is John Smith”

9. Máster interuniversitario en Ingeniería Telemática 9 RDF Model An RDF model (set of RDF statements) can be represented by means of a graf For each statement: subject is a node predicate is an arc object is a node Subject and predicate are resources Object can be either a resource or a literal

10. Máster interuniversitario en Ingeniería Telemática 10 Example “http://www.example.org has a creator whose value is John Smith”.

11. Máster interuniversitario en Ingeniería Telemática 11 Textual notation (triples) <http://www.example.org/index.html> <http://purl.org/dc/elements/1.1/creator> <http://www.example.org/staffid/85740> . <http://www.example.org/index.html> <http://www.example.org/terms/creation-date> "August 16, 1999" . <http://www.example.org/index.html> <http://www.example.org/terms/language> "English“ .

12. Máster interuniversitario en Ingeniería Telemática 12 Ontologies: goal An ontology is a formal, explicit specification of a shared conceptualization An ontology defines the basic terms and relations comprising the vocabulary of a topic area, as well as rules that should be fulfilled by such terms and relations

13. Máster interuniversitario en Ingeniería Telemática 13 RDF Schema RDF vocabulary Properties definition and description of properties Classes definition and description Can be used to define simple ontologies

14. Máster interuniversitario en Ingeniería Telemática 14 Properties in RDF Schema rdfs:subPropertyOf rdfs:range rdfs:domain rdfs:subClassOf

15. http://webtlab.it.uc3m.es 15 Sampletaxonomy pictureby IanRuotsala

16. Ontologylanguage More powerfulthanRDF-Schema Examples: Existence/cardinalityconstraints allinstancesof personhave a motherthatisalso a person, orthatpersonshaveexactly 2 parents Transitive, inverseorsymmetricalproperties isPartOfis a transitiveproperty, hasPartistheinverse of isPartOf, touchesissymmetrical http://webtlab.it.uc3m.es 16 OWL

17. Semantic Web and TechnologyEnhancedLearning http://webtlab.it.uc3m.es 17

18. Modelling (ontologies) learningprocesses learningcontent learning output (competences) learningagents (students, teachers) Addingmetadata (annotations) accordingtothemodels Use themodels and themetadata in toolstomakedecissions example: personalized, adaptivecontent and/orproblems http://webtlab.it.uc3m.es 18 Typicalapplications

19. Semanticannotation of text http://webtlab.it.uc3m.es 19

20. Generalities Goal: extract semantic annotations from free text Natural language is complex and ambiguous Language dependent Domain dependent applications News Literature E-mail Transcriptions of spoken dialogues Some useful results can be achieved nowadays

21. Taxonomy of semanticannotations Content basedannotations Documentcategorization Namedentities Ontologybaseddomainannotations Concepts and instancesidentification Relationsextraction isGovernor(GaryLocke,WST) Named Entity (Washington, location) <rdf:Description rdf:about=‘WST'> <rdf:type rdf:resource=‘State'/> </rdf:Description> <rdf:Description rdf:about=‘WDC'> <rdf:type rdf:resource=‘City'/> </rdf:Description>

22. basic techniques (i) Semantic Analysis S  NP NP*(X) VBT(Elect) NN(Y) Parsing S  NP NP* VBT NN S hasFunction(X, Y) NP VBT NP NN Symbolic NLP Based on the use of lexicons and grammar rules to process text Example: “Barack Obama Elected President” Lexical Analysis NP  Barack NP  Obama VBT  Elect VBT  VBT + ‘ed’ NN  President hasFunction(BarackObama, President)

23. Basic techniques (ii) Statistical NLP Based on counting: finding frequent patterns that make likely the occurrence of certain text feature Use of extensive corpora Example: “Washington” when appearing in the same document with “Hollywood” is likely to represent (Denzel Washington, actor) while Washington” when appearing in the same document with “Obama” is likely to represent (Washington D.C., American capital) We can count the frequency of different meanings of “Washington” when appearing in different contexts

24. AnapproachtonamedentitydisambiguationwithWikipedia http://webtlab.it.uc3m.es 24

25. Instance: a particular person, location (GPE), organization, ... http://webtlab.it.uc3m.es 25 Introduction Entity: text + type

26. http://webtlab.it.uc3m.es 26 Strategy I

27. Approach Findentities in document Foreachentity, identifycandidateinstancesthat are compatible withtheentityname Assign a ranking valuetoeachcandidateinstance: 0 ≤ r ≤ 1 Greater ranking valuesindicategreaterlikelihood of occurrence http://webtlab.it.uc3m.es 27 Strategy II

28. Semanticcoherence (in terms of ranking) “Aninstancewouldhave a high ranking valueiftheinstancesthattypicallyco-occurwithitalsohavehigh ranking values” http://webtlab.it.uc3m.es 28 Strategy III

29. We can add a vector Ethataccountsforothercontextinformation Equation similar to Google PageRank http://webtlab.it.uc3m.es 29 Strategy IV

30. Alternativeinstancenamesextractedbyprocessing a Wikipediadump Page titles, redirects, disambiguationpages, anchors IndexedbyLucene Candidateinstances are obtainedbyqueryingLucene CandidateinstancesweightedbycombiningLucene scores and PageRankvalues Filteringlimitsthemaximumnumber of candidates http://webtlab.it.uc3m.es 30 Instancefinder & filter

31. http://webtlab.it.uc3m.es 31 Instanceranker AL: basedondirect links E: candidateinstanceweightspassedbytheinstancefilter AC: basedoninstanceco-occurrence in Wikipediapages

32. http://webtlab.it.uc3m.es 32 Results I

33. http://webtlab.it.uc3m.es 33 Results II

34. Approachbasedoninstanceco-occurrence TextfromWikipediarestrictedto: titles, anchors Resultsconsideredpromising ShouldimproveforGPE http://webtlab.it.uc3m.es 34 Conclusions

35. http://webtlab.it.uc3m.es 35 ThankYou! Questions?

36. Differentiateaccordingtoentitytype Improveselection of candidateinstances Responsiblebyitself of errors in 12,7% of non-nilqueries http://webtlab.it.uc3m.es 36 Futurework

37. AC: basedoninstancecooccurrence in Wikipediapages AL: basedondirect links E: candidateinstanceweightspassedbytheinstancefilter http://webtlab.it.uc3m.es 37 Instanceranker

38. aCij≈ P(Ii|Ij) Basedoncountingcooccurrence of Ii and Ij in Wikipediapages Ex.: P(PauGasol|Lakers)= #pageswhereboth Pau Gasol and Los AngelesLakers are mentioneddividedby #total pageswhere Los AngelesLakers are mentioned http://webtlab.it.uc3m.es 38 ACComputation

39. Basedondirect links Ex.: TheWikipedia page of Pau Gasol links totheWikipedia page of Los AngelesLakers Initial idea IfIj links many times toIi and Ijislikelytooccur (it has a high ranking), thenIiisalsolikelytooccur Lucene score isusedto compute αLij http://webtlab.it.uc3m.es 39 ALComputation

40. Target entity: 200 or 30 Otherentities: 15 http://webtlab.it.uc3m.es 40 Instancefilter

41. Semanticcoherenceprinciple “Aninstanceusuallycooccurtypicallywithotherrelatedinstances” Ex.: (Pau Gasol, Los AngelesLakers); (Athens, Georgia); (Athens; Greece); (Hillary Clinton, BarackObama) Requirestodisambiguatealldocumententities Slightlydifferenttoentitycooccurrenceapproaches http://webtlab.it.uc3m.es 41 DisambiguationStrategy I

42. http://webtlab.it.uc3m.es 42 Results IV: ablationtests