Challenges of Web Science: Adaptive Information Extraction Methods

1. Introduction to Web Science Harvesting the SW

2. Six challenges of the Knowledge Life Cycle • Acquire • Model • Reuse • Retrieve • Publish • Maintain

3. Information Extraction vs. Retrieval IR IE 

4. A couple of approaches … • Active learning to reduce annotation burden • Supervised learning • Adaptive IE • The Melita methodology • Automatic annotation of large repositories • Largely unsupervised • Armadillo

5. The Seminar Announcements Task • Created by Carnegie Mellon School of Computer Science • How to retrieve • Speaker • Location • Start Time • End Time • From seminar announcements received by email

6. Seminar Announcements Example Dr. Steals presents in Dean Hall at one am. becomes <speaker>Dr. Steals</speaker> presents in <location>Dean Hall</location> at <stime>one am</stime>.

7. Information Extraction Measures • How many documents out of the retrieved documents are relevant? • How many retrieved documents are relevant out of all the relevant documents? • Weighted harmonic mean of precision and recall

8. IE Measures Examples • If I ask the librarian to search for books on cars, there are 10 relevant books in the library and out of the 8 he found, only 4 seem to be relevant books. What is his precision, recall and f-measure?

9. IE Measures Answers • If I ask the librarian to search for books on cars, there are 10 relevant books in the library and out of the 8 he found, only 4 seem to be relevant books. What is his precision, recall and f-measure? • Precision = 4/8 = 50% • Recall = 4/10 = 40% • F =(2*50*40)/(50+40) = 44.4%

10. Adaptive IE • What is IE? • Automated ways of extracting unstructured or partially structured information from machine readable files • 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-language features • different text genres • Considers important the Usability and Accessibility of the system

11. Amilcare • Tool for adaptive IE from Web-related texts • Specifically designed for document annotation • Based on (LP)2 algorithm *Linguistic Patterns by Learning Patterns • 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

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

13. 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) • Co-reference Resolution • Multi lingual support • Protégé • WEKA • many more exist and can be added • http://www.gate.ac.uk

14. 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

15. Different Annotation Systems • SGML • TEX • Xanadu • CoNote • ComMentor • JotBot • Third Voice • Annotate.net • The Annotation Engine • Alembic • The Gate Annotation Tool • iMarkup, Yawas • MnM, S-CREAM

16. 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

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

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

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

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

21. 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

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

23. 60 30 Results

24. 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 !!

25. 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)

26. 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)

27. The Idea • 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)

28. 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

29. 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

30. Experimental Results II - Sheffield • People • discovering who works in the department • using Information Integration • Total present in site 139 • Using generic patterns + online repositories • 35 correct, 5 wrong • Precision 35 / 40 = 87.5 % • Recall 35 / 139 = 25.2 % • F-measure 39.1 % • Errors • A. Schriffin • Eugenio Moggi • Peter Gray

31. Experimental Results IE - Sheffield • People • using Information Extraction • Total present in site 139 • 116 correct, 8 wrong • Precision 116 / 124 = 93.5 % • Recall 116 / 139 = 83.5 % • F-measure 88.2 % • Errors • Speech and Hearing • European Network • Department Of • Enhancements – Lists, Postprocessor • Position Paper • The Network • To System

32. Experimental Results - Edinburgh • People • using Information Integration • Total present in site 216 • Using generic patterns + online repositories • 11 correct, 2 wrong • Precision 11 / 13 = 84.6 % • Recall 11 / 216 = 5.1 % • F-measure 9.6 % • using Information Extraction • 153 correct, 10 wrong • Precision 153 / 163 = 93.9 % • Recall 153 / 216 = 70.8 % • F-measure 80.7 %

33. Experimental Results - Aberdeen • People • using Information Integration • Total present in site 70 • Using generic patterns + online repositories • 21 correct, 1 wrong • Precision 21 / 22 = 95.5 % • Recall 21 / 70 = 30.0 % • F-measure 45.7 % • using Information Extraction • 63 correct, 2 wrong • Precision 63 / 65 = 96.9 % • Recall 63 / 70 = 90.0 % • F-measure 93.3 %

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

35. 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" }

36. 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!

37. Artists domain • Task • Given the name of an artist, find all the paintings of that artist. • Created for the ArtEquAKT project

38. Artists domain Evaluation

39. 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

40. 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

41. 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

42. Data Navigation (1)

43. Data Navigation (2)

44. Data Navigation (3)

45. 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

46. Questions?