Populating Ontologies for the Semantic Web

1. Populating Ontologies for the Semantic Web Alexiei Dingli

2. What’s the problem?

3. Towards a solution … (1) Ask intelligent agents to do the job for us!! But they don’t understand the WWW !!!

4. (W3C Web Guru) Towards a solution … (2) For the Web to reach its full potential, it must evolve into a Semantic Web, providing a universally accessible platform that allows data to be shared and processed by automated tools as well as by people. • But there’s another way in which this can be achieved, by supplying the missing semantic information Creating the Semantic Web !!

5. It requires lots of time and effort • It needs lots of people willing to do it • Not everyone can do it Towards a solution … (3) • Why do many believe this solution will fail?

6. Our approaches • Active learning to reduce annotation burden • Supervised learning • Adaptive IE • The Melita methodology • Automatic annotation of large repositories • Largely unsupervised • Armadillo

7. Adaptive IE • What is AIE? • Performs tasks of traditional IE • Exploits the power of Machine Learning in order to adapt to • complex domains having large amounts of domain dependent data • different sub-languages features • different text genres • Considers important the Usability and Accessibility of the system

8. Amilcare • Tool for adaptive IE from Web-related texts • Specifically designed for document annotation • Based on (LP)2 algorithm • Covering algorithm based on Lazy NLP • Trains with a limited amount of examples • Effective on different text types • free texts • semi-structured texts • structured texts • Uses Gate and Annie for preprocessing

9. CMU: detailed results • Best overall accuracy • Best result on speaker field • No results below 75%

10. Gate • General Architecture for Text Engineering • provides a software infrastructure for researchers and developers working in NLP • Contains • Tokeniser • Gazetteers • Sentence Splitter • POS Tagger • Semantic Tagger (ANNIE) • Orthographic Coreference • http://www.gate.ac.uk • Pronominal Coreference • Multi lingual support • Protégé • WEKA • many more exist and can be added

11. is complex is time consuming needs annotation by experts Annotation Current practice of annotation for knowledge identification and extraction Reduce burden of text annotation for Knowledge Management

12. SGML TEX Xanadu CoNote ComMentor JotBot Third Voice Annotate.net The Annotation Engine Visual Text Alembic Annotea CritLink The Gate Annotation Tool iMarkup MnM S-CREAM Yawas Different Annotation Systems

13. Melita • Tool for assisted automatic annotation • Uses an Adaptive IE engine to learn how to annotate (no use of rule writing for adapting the system) • Users: annotates document samples • IE System: • Trains while users annotate • Generalizes over seen cases • Provides preliminary annotation for new documents • Performs smart ordering of documents • Advantages • Annotates trivial or previously seen cases • Focuses slow/expensive user activity on unseen cases • User mainly validates extracted information • Simpler & less error prone / Speeds up corpus annotation • The system learns how to improve its capabilities

14. Amilcare Learns in background Bare Text User Annotates Methodology: Melita Bootstrap Phase

15. User Annotates Amilcare Annotates Learning in background from missing tags, mistakes Bare Text Methodology: Melita Checking Phase

16. Corrections used to retrain Bare Text Amilcare Annotates Methodology: Melita Support Phase User Corrects

17. Intrusivity • An evolving system is difficult to control • Goal: • Avoiding unwelcome/unreliable suggestions • Adapting proactivity to user’s needs • Method: • Allow users to tune proactivity • Monitor user reactions to suggestions

18. User Annotates Bare Text Learns annotations Smart ordering of Documents Tries to annotate all the documents and selects the document with partial annotations

19. Methodology: Melita Control Panel Ontology defining concepts Document Panel

20. 60 30 Results

21. Future Work • Research better ways of annotating concepts in documents • Optimise document ordering to maximise the discovery of new tags • Allow users to edit the rules • Learn to discover relationships !! • Not only suggest but also corrects user annotations !!

22. Annotation for the Semantic Web • Semantic Web requires document annotation • Current approaches • Manual (e.g. Ontomat)or semi-automatic (MnM, S-Cream, Melita) • BUT: • Manual/Semi-automatic annotation of • Large diverse repositories • Containing different and sparse information is unfeasible • E.g. a Web site (So: 1,600 pages)

23. Redundancy • Informationon the Web (or large repositories) is Redundant • Information repeated in different superficial formats • Databases/ontologies • Structured pages (e.g. produced by databases) • Largely structured pages (bibliography pages) • Unstructured pages (free texts)

24. Our Proposal • Largely unsupervised annotation of documents • Based on Adaptive Information Extraction • Bootstrapped using redundancyof information • Method • Use the structured information (easier to extract) to bootstrap learning on less structured sources (more difficult to extract)

25. Example: Extracting Bibliographies • Mines web-sites to extract biblios from personal pages Tasks: • Finding people’s names • Finding home pages • Finding personal biblio pages • Extract biblio references • Sources • NE Recognition (Gate’s Annie) • Citeseer/Unitrier (largely incomplete biblios) • Google • Homepagesearch

26. AKT Reference Ontology • Developed by the AKT partners • Represent the knowledge used in the CS AKTive Portal testbed • Consists of several sub-ontologies • Available in several flavours … • DAML+OIL • OWL • Has 9,000,000 RDF triples !! • Available at • Ontology http://www.aktors.org/publications/ontology/ • RDF Triples http://triplestore.aktors.org/

27. Annotates known names • Trains on annotations to discover the HTML structure of the page • Recovers all names and hyperlinks Mining Web sites (1) • Mines the site looking for People’s names • Uses • Generic patterns (NER) • Citeseer for likely bigrams • Looks for structured lists of names

28. Experimental Results (1) • People • discovering who works in the department • using Information Integration • Total present in site 129 • Using generic patterns + online repositories • 48 correct, 3 wrong • Precision 48 / 51 = 94 % • Recall 48 / 129 = 37 % • F-measure 51 % • Errors • A. Schriffin • Eugenio Moggi • Peter Gray

29. Experimental Results (2) • People • using Information Extraction • Total present in site 129 • 96 correct, 9 wrong • Precision 96 / 105 = 91 % • Recall 96 / 129 = 74 % • F-measure 87 % • Errors • Speech and Hearing • European Network • Department Of • Position Paper • The Network • To System

30. Mining Web sites (2) • Annotates known papers • Trains on annotations to discover the HTML structure • Recovers co-authoring information

31. Experimental Results (1) • Papers • discovering publications in the department • using Information Integration • Total present in site 320 • Using generic patterns + online repositories • 151 correct, 1 wrong • Precision 151 / 152 = 99 % • Recall 151 / 320 = 47 % • F-measure 64 % • Errors - Garbage in database!! @misc{ computer-mining, author = "Department Of Computer", title = "Mining Web Sites Using Adaptive Information Extraction Alexiei Dingli and Fabio Ciravegna and David Guthrie and Yorick Wilks", url = "citeseer.nj.nec.com/582939.html" }

32. Experimental Results (2) • Papers • using Information Extraction • Total present in site 320 • 214 correct, 3 wrong • Precision 214 / 217 = 99 % • Recall 214 / 320 = 67 % • F-measure 80 % • Errors • Wrong boundaries in detection of paper names! • Names of workshops mistaken as paper names!

33. User Role • Providing … • A URL • List of services • Already wrapped (e.g. Google is in default library) • Train wrappers using examples • Examples of fillers (e.g. project names) • In case … • Correcting intermediate results • Reactivating Armadillo when paused

34. Armadillo • Library of known services (e.g. Google, Citeseer) • Tools for training learners for other structured sources • Tools for bootstrapping learning • From un/structured sources • No user annotation • Multi-strategy acquisition of information using redundancy • User-driven revision of results • With re-learning after user correction

35. Rationale • Armadillo learns how to extract information • From large repositories By integrating information • from diverse and distributed resources • Use: • Ontology population • Information highlighting • Document enrichment • Enhancing user experience

36. Data Navigation (1)

37. Data Navigation (2)

38. Data Navigation (3)

39. What’s so new about Armadillo? • In other systems … • User defined examples are used • Generic patters are used that work independently of the site • In our system … • We also make use of • generic patterns & some user defined examples • We learn page specific patterns • And we integrate information from different sources

40. IE for SW: The Vision • Automatic annotation services • For a specific ontology • Constantly re-indexing/re-annotating documents • Semanticsearch engine • Effects: • No annotation in the document • As today’s indexes are not stored in the documents • No legacy with the past • Annotation with the latest version of the ontology • Multiple annotations for a single document • Simplifies maintenance • Page changed but not re-annotated

41. Links • Melita • http://nlp.shef.ac.uk/melita/ • Armadillo • http://nlp.shef.ac.uk/armadillo/ • Amilcare • http://nlp.shef.ac.uk/amilcare/ • Gate • http://www.gate.ac.uk • AKT Reference Ontology • http://www.aktors.org/publications/ontology/ • AKT 3Store • http://triplestore.aktors.org/ • More than 40 semantic web technologies • http://www.aktors.org/technologies/ • Most of them can be freely downloaded • Range from IE tools, semantic portals, annotation tools, semantic web services, dialogue systems, etc