Applying Referent Tracking in Websites

1. InterOntology 2009Applying Referent Tracking to the Use and Evolution of Websites. Keio University, Tokyo, Japan - February 28, 2009 Werner Ceusters & Shahid Manzoor Ontology Research Group Center of Excellence in Bioinformatics & Life Sciences SUNY at Buffalo, NY

2. Presentation overview • Foundations of referent tracking • Referent Tracking Systems • Referent Tracking enabled websites

3. Foundations of Referent Tracking

4. ‘Referent Tracking’ • In (computational) linguistics: • Identifying which words or phrases denote the same entity throughout a discourse. • In the newspaper: • Obama gave another speech yesterday. The President said hard times are coming. But he was confident to come up with solutions.

5. ‘Referent Tracking’ • In (computational) linguistics: • Identifying which words or phrases denote the same entity throughout a discourse. • In the newspaper: • Obama gave another speech yesterday. The President said hard times are coming. But he was confident to come up with solutions.

6. concept object term Origin: the semantic / semiotic triangle reference referent

7. How can we know what co-refers? • prior mention • mutual knowledge from shared experience • frames/scripts/schemata: culturally established scenes with certain expectable parts • the handlebars from a mention of a bicycle; • the waiter from a mention of a restaurant • uniqueness in the “universe of discourse” (e.g. ‘the sun’)

8. Not fail proof • Important local negotiation aspect during human communication • Requests for comprehension • ‘you know ?’, ‘you remember ?’ • Requests for clarification • ‘who do you mean?’ • Explanations • These tools are not available in isolated descriptions

9. From two recipes • For meringue: • Take an egg, separate the yolk from the white, add sugar and start beating it gently • For sabayon: • Take an egg, separate the yolk from the white, add sugar and white wine and start beating it gently over a low flame Yolk or white in which case ?

10. PtID Date ObsCode Narrative 5572 5572 5572 298 5572 5572 298 2309 47804 5572 5572 12/07/1990 01/04/1997 12/07/1990 17/05/1993 22/08/1993 21/03/1992 22/08/1993 04/07/1990 01/04/1997 04/07/1990 03/04/1993 81134009 9001224 26442006 9001224 79001 79001 9001224 26442006 2909872 58298795 26442006 Essential hypertension Accident in public building (supermarket) Closed fracture of radial head closed fracture of shaft of femur Essential hypertension Accident in public building (supermarket) Other lesion on other specified region closed fracture of shaft of femur Fracture, closed, spiral closed fracture of shaft of femur Accident in public building (supermarket) 5572 04/07/1990 79001 Essential hypertension 0939 24/12/1991 255174002 benign polyp of biliary tract 2309 21/03/1992 26442006 closed fracture of shaft of femur 0939 20/12/1998 255087006 malignant polyp of biliary tract A medical example: morbidity reporting

11. The problem • Generic terms used to denote specific entities do not have enough referential capacity • Usually enough to convey that some specific entity is denoted, • Not enough to be clear about which one in particular. • For many ‘important’ entities, unique identifiers are used: • UPS parcels • Patients in hospitals • VINs on cars • …

12. Fundamental goals of ‘our’ Referent Tracking • explicitreference to the concrete individual entities relevant to the accurate description of some portion of reality, ... Ceusters W, Smith B. Strategies for Referent Tracking in Electronic Health Records. J Biomed Inform. 2006 Jun;39(3):362-78.

13. 78 235 5678 321 322 666 427 Method: numbers instead of words • Introduce an Instance Unique Identifier(IUI) for each relevant particular (individual) entity Ceusters W, Smith B. Strategies for Referent Tracking in Electronic Health Records. J Biomed Inform. 2006 Jun;39(3):362-78.

14. Fundamental goals of ‘our’ Referent Tracking • Use these identifiers in expressions using a language that acknowledges the structure of reality e.g.: a yellow ball: #1: the ball #2: #1’s yellow Then not: ball(#1) and yellow(#2) and hascolor(#1, #2) But: instance-of(#1, ball, since t) instance-of(#2, yellow, since t) inheres-in(#1, #2, since t) • Strong foundations in realism-based ontology

15. PtID Date ObsCode Narrative IUI-001 5572 5572 5572 5572 298 5572 2309 298 47804 5572 5572 12/07/1990 01/04/1997 22/08/1993 22/08/1993 01/04/1997 04/07/1990 21/03/1992 03/04/1993 04/07/1990 17/05/1993 12/07/1990 81134009 26442006 9001224 79001 9001224 26442006 58298795 26442006 9001224 79001 2909872 Accident in public building (supermarket) closed fracture of shaft of femur Accident in public building (supermarket) Fracture, closed, spiral closed fracture of shaft of femur Closed fracture of radial head Essential hypertension Accident in public building (supermarket) closed fracture of shaft of femur Other lesion on other specified region Essential hypertension IUI-001 IUI-001 IUI-007 5572 04/07/1990 79001 IUI-005 Essential hypertension 0939 24/12/1991 255174002 IUI-004 benign polyp of biliary tract 2309 21/03/1992 26442006 IUI-002 closed fracture of shaft of femur IUI-007 IUI-006 IUI-005 IUI-003 IUI-007 IUI-012 IUI-005 0939 20/12/1998 255087006 IUI-004 malignant polyp of biliary tract Codes for types AND identifiers for instances

16. Representation and Reference terms concepts about First Order Reality The semantic triangle revisited concepts objects terms

17. representational units universals particulars Terminology Realist Ontology Representation and Reference terms concepts about objects First Order Reality

18. Terminology Realist Ontology Representation and Reference terms concepts representational units about objects universals particulars First Order Reality

19. Terminology Realist Ontology Representation and Reference representational units terms concepts cognitive units communicative units about objects universals particulars First Order Reality

20. Representational units in various • forms about (1), (2) or (3) (2) Cognitive entities which are our beliefs about (1) (1) Entities with objective existence which are not about anything Three levels of reality in Realist Ontology Representation and Reference representational units cognitive units communicative units universals particulars First Order Reality

21. Representation and the three levels Level 1, 2 or 3 Level 2 or 3 Level 3 Level 1

22. Possible mismatches reality / representations

23. Referent Tracking Systems

24. Referent Tracking System Environment

25. Information System A Information System C Referent Tracking System A Referent Tracking System C Referent Tracking Server A1 Referent Tracking Server C1 RTS Proxy Peer RTS Proxy Peer Referent Tracking Server A2 Referent Tracking Server C2 RTS Server Proxy Peer RTS Server Proxy Peer Referent Tracking Server A3 Referent Tracking Server C3 … … Information System B Referent Tracking System B RTS Server Proxy Peer RTS Proxy Peer Referent Tracking Server B1 Referent Tracking Server B2 Referent Tracking Server B3 … RTS farms

26. Referent Tracking enabled Websites

28. Architecture

29. Some central ideas • Informative websites are about portions of reality. If the latter change, so should the former. • Synchronization should be auditable. • Enforce responsibility of information providers and consumers, yet protect their integrity. • Cross-fertilization with Information Artifact Ontology.

30. Some key insights • Static versus dynamic pages; • Web pages usually keep their name (URL), yet undergo changes; • ‘page’ versus ‘file’ • Server file never ‘changes’: always replaced by a new file with the same name • Changes to a file do not always involve changes to the propositional content; • Requests to view a page do not lead the file on the server to be transmitted, but a new copy of it in each single case;

31. Entities to assign IUIs to • The content file of each page • The content of each content file • The propositional content of each content • Each browser page • Each checksum • Each ontology and terminology used in RT-tuples • Each RT-tuple (except D-tuples) • The middleware component

32. Use of the CEN Time Standard for HIT

33. Tuple generations when adding a page

34. Tuple generations when updating a page

35. Tuple insertions: generating a browser page

36. You see this is ontology! I got a graph!

40. Challenges for the Information Artifact Ontology • Ontological basis for various relationships that are currently too much CS-ish • MainContentCopyOf • InstigatorOf • DerivesFrom (applicable in this context?) • … • Ontological nature of files, pages, content, propositional content

41. Future work • Better automatisation • Integration in popular web-design softwares • RT-enabled vita-publisher • Expansion to hard-copies