Ontology driven Annotation
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  • Ontologies today are available in many different forms: as artifacts of a tedious knowledge-engineering process, as information that was extracted automatically from informal electronic sources, or as simple “light-weight” ontologies

Ontology driven Annotation Presentation Transcript

  • 1. Ontology driven Annotation
  • 2. What is Semantic web?
  • 3. The Semantic Web vision”A web of data that can be processed directly and indirectly bymachines”
  • 4. What do we need?Annotated documents with Ontology with metadata aboutcanonical references to entitiesmentioned entities
  • 5. Wikipedia Annotation
  • 6. About WikipediaLarge knowledge base with 3.9Marticle pages in English
  • 7. Automatic Annotation Spotting DisambiguationHeading into the LosAngeles Lakers Tuesdaynight tilt with the GoldenState Warriors, KobeBryant needed 25 pointsto pass Michael Jordan forthe second-most pointsscored by a player for asingle NBA franchise. Hegot 30. We now have adefinitive answer; finally,the debate can end.
  • 8. Existing Wikipedia Annotators Wikify DBPedia Spotlight Wikipedia Miner Zemanta Extractiv Opencalais Wikifier
  • 9. Sample from Wikipedia Miner
  • 10. Trie based Spotter
  • 11. Academic and Technical Ontology, Annotators
  • 12. 1212 Motivating Vision Next-Generation Search will be Information Extraction + Ontology + Inference Object 1 … Albert Einstein was a German-born theoretical physicist …h German Scientists Taught at US Universities? … Object 3 New Jersey is a state in the Northeastern region … Object 2 … Einstein was a guest lecturer at the Institute for Advanced Study in New Jersey …
  • 13. Academic and Technical Ontology
  • 14. AQL script for Techpedia Project Titlecreate view ProjectName asextract regex /Project Title/ with flags CASE_INSENSITIVE on D.text as titlefrom DetaggedDoc D;create view ProjectTitle asselect RightContextTok(P.title,10) as ProjectTitlefrom ProjectName P;output view ProjectTitle;
  • 15. AQL script for course prerequisitecreate view PrerequisiteWord as extract regex /(Prerequisitesns*)(.*)/ on D.text return group 1 as prereqWord and group 2 as prereqfrom DetaggedDoc D;create view prereq asextract P.prereq as match, regex /n/ on P.prereq as boundaryfrom PrerequisiteWord P;create view CoursePrerequisite asextract split using P.boundaryretain left split point on P.matchas CoursePrerequisitefrom prereq Plimit 1;output view CoursePrerequisite;
  • 16. AQL script for Professor Namecreate view ProfessorName as(select CombineSpans(F.fname, L.lname) as Pnamefrom FirstName F, LastName Lwhere FollowsTok(F.fname, L.lname,0,0)consolidate on F.fname using ContainedWithinorder by GetText(F.fname)limit 1)union all(select CombineSpans(F.Nameinitial, L.lname) as Pnamefrom FNameInitial F, LastName Lwhere FollowsTok(F.Nameinitial, L.lname,0,0)consolidate on F.Nameinitial using ContainedWithinorder by GetText(F.Nameinitial)limit 1); 16
  • 17. Input Page
  • 18. Output view for Project TitleOutput View ProjectTitle: [Document.text[17-73]: : DESIGN & ANALYSIS OF PRESSUREVES...(1 fields)] ProjectTitle: : DESIGN & ANALYSIS OF PRESSURE VESSEL College : U [Document.text[276-349]: : Design and Analysis ofElectrical...(1 fields)] ProjectTitle: : Design and Analysis of Electrical OverheadTraveling Crane College [Document.text[566-623]: : Blungers: For Slip and GlazePrep...(1 fields)] ProjectTitle: : Blungers: For Slip and Glaze Preparation College : [Document.text[833-880]: : DESIGN OF STEAM CONDENSERn Col...(1 fields)] ProjectTitle: : DESIGN OF STEAM CONDENSER
  • 19. Output view for College NameOutput View CollegeName: [Document.text[68-113]: : U.V.PATEL COLLEGE OFENGINEERING...(1 fields)] CollegeName: : U.V.PATEL COLLEGE OF ENGINEERING Guide [Document.text[349-394]: : U.V.PATEL COLLEGE OFENGINEERING...(1 fields)] CollegeName: : U.V.PATEL COLLEGE OF ENGINEERING Guide [Document.text[621-666]: : U.V.PATEL COLLEGE OFENGINEERING...(1 fields)] CollegeName: : U.V.PATEL COLLEGE OF ENGINEERING Guide [Document.text[873-918]: : U.V.PATEL COLLEGE OFENGINEERING...(1 fields)] CollegeName: : U.V.PATEL COLLEGE OF ENGINEERING
  • 20. Output view for Guide NameOutput View GuideName: [Document.text[116-150]: MR. Bhavesh Pateln TeamMembers(1 fields)] Guide: MR. Bhavesh Patel Team Members [Document.text[397-418]: Mr. Bhavesh P. Patel(1 fields)] Guide: Mr. Bhavesh P. Patel [Document.text[669-680]: PROF. V.B.(1 fields)] Guide: PROF. V.B. [Document.text[921-941]: A.R. ISRANIn Team(1 fields)] Guide: A.R. ISRANI
  • 21. Output view for Team MembersOutput View TeamMembers: [Document.text[153-219]: Jimit Vyas & Mahavir SolankinAbs...(1 fields)] members: Jimit Vyas & Mahavir Solanki Abstract : The significance read [Document.text[437-471]: Vishal A. Patel,Bhavik H. Khamar,(1fields)] members: Vishal A. Patel,Bhavik H. Khamar, [Document.text[704-758]: PATEL JAYRAM ,PATEL KETUL ,PATELTUS...(1 fields)] members: PATEL JAYRAM ,PATEL KETUL ,PATEL TUSHAR Abstract : [Document.text[952-1007]: HARSHAD PATEL,NITIN NAHAR,SANDEEPPA...(1 fields)] members: HARSHAD PATEL,NITIN NAHAR,SANDEEP PARMAR
  • 22. Input CSE Course Page
  • 23. Output view for Course Details CourseId: CS 717 CourseName: Statistical Relational Learning CoursePrerequisite: N/A CourseHomepage: Not Available CourseContent:* What is Relational Learning? Need for RL.* Discussion on thethree elements of relational models: (a) logic for representingtypes, relations and complexdependencies between them, (b)uncertainty, and (c) learning and inferencing* Basics of 0-order and First order logic - includes types of clauses, syntaxand semantics, .... CourseReferences: 1.011 Inductive Logic Programming:Techniques and Applications, N. Lavrac and S. Dzeroski. Ellis ProfessorName: G.Ramakrishnan
  • 24. Thank You
  • 25. What is annotation? Short description of page/document Explicit v/s Implicit Metadata and not content Type of Annotation Concise description Abbreviation Opinion Web URL annotation like wiki linksearch” by Dmitriev, Pavel A. and Eiron, Nadav and Fontoura, Marcus and Shekita, Eugene In Proceedings of the 15th internatio
  • 26. Flow of annotations in search“Using Annotations in Enterprise Search” by Dmitriev, Pavel A. and Eiron, Nadav and Fontoura, Marcus and Shekita, Eugene In Proceedings of the 15th international conference on World Wide Web 2006
  • 27. Definition of Ontology‘A formal, explicit specification of a shared conceptualization’ Gruber (1993) must be machine understandable not private to some individual, but accepted by a groupan abstract model of some phenomenon in the world formed by identifying th types of concepts and constraints must be clearly defined 27 27
  • 28. Processes to create a DomainOntologyOntology acquisitionAutomatic extraction of ontological knowledge from base vocabulary and domain specific text sourcesMerging into one ontologyRefinement and ExtensionEvaluation and Assessment 28 28