1. March 2000 Gio XIT 1 Increasing the Precision of Semantic Interoperation Gio Wiederhold Stanford University March 2000 report: www-db.stanford.edu/pub/gio/1999/miti.htm Stanford Computer Forum Supported by AFOSR- New World Vistas Program Jan Jannink, Shrish Agarwal, Prasenjit Mitra, Stefan Decker.

2. March 2000 Gio XIT 2 Heterogeneity among Domains If interoperation involves distinct domains mismatch ensues Autonomy conflicts with consistency, – Local Needs have Priority, – Outside uses are a Byproduct Heterogeneity must be addressed Platform and Operating Systems 4 4 Representation and Access Conventions 4 Naming and Ontology :

3. March 2000 Gio XIT 3 Semantic Mismatches Information comes from many autonomous sources Differing viewpoints (by source) –differing terms for similar items { lorry, truck } –same terms for dissimilar items trunk(luggage, car) –differing coverage vehicles (DMV, AIA) –differing granularity trucks (shipper, manuf.) –different scope student museum fee, Stanford Hinders use of information from disjoint sources –missed linkages loss of information, opportunities –irrelevant linkages overload on user or application program Poor precision when merged ok for web browsing, poor for business

4. March 2000 Gio XIT 4 Solutions Specify and standardize terminology usage: ontology Globally all interacting sources –wonderful for users and their programs –long time to achieve, 2 sources (UAL, BA), 3 (+ trucks), 4, … all ? –costly maintenance, since all sources evolve –who has the authority to dictate conformance Domain-specific XML DTD assumption –Small, focused, cooperating groups –high quality, some examples - genomics, arthritis, shakespeare plays –allows sharable, formal tools –ongoing, local maintenance affecting users - annual updates –poor interoperation, users still face inter-domain mismatches solves only part of the problem

5. March 2000 Gio XIT 5 Domains and Consistency. a domain will contain many objects the object configuration is consistent within a domain all terms are consistent & relationships among objects are consistent context is implicit No committee is needed to forge compromises * within a domain  Compromises hide valuable details Domain Ontology

6. March 2000 Gio XIT 6 Objective S calable K nowledge C omposition Provide for Maintainable Ontologies devolve maintenance onto many domain-specific experts / authorities provide an algebra to compute composed ontologies that are limited to their articulation terms enable interpretation within the source contexts SKC

7. March 2000 Gio XIT 7 Sample Operation: INTERSECTION Source Domain 1: Owned and maintained by Store Result contains shared terms, useful for purchasing Source Domain 2: Owned and maintained by Factory Articulation

8. March 2000 Gio XIT 8 Tools to create articulations Graph matcher for Articulation- creating Expert Vehicle ontology Transport ontology Suggestions for articulations

9. March 2000 Gio XIT 9 continue from initial point Also suggest similar terms for further articulation: by spelling similarity, by graph position by term match repository Expert response: 1. Okay 2. False 3. Irrelevant to this articulation All results are recorded Okay ’s are converted into articulation rules

10. March 2000 Gio XIT 10 Candidate Match Repository Term linkages automatically extracted from 1912 Webster’s dictionary * * free, other sources. being processed. Based on processing headwords ý definitions using algebra primitives Notice presence of 2 domains: chemistry, transport

11. March 2000 Gio XIT 11 Using the Match Repository

12. March 2000 Gio XIT 12 Using the Match Repository

13. March 2000 Gio XIT 13 An Ontology Algebra A knowledge-based algebra for ontologies The Articulation Ontology (AO) consists of matching rules that link domain ontologies Intersection create a subset ontology keep sharable entries Union create a joint ontology merge entries Difference create a distinct ontology remove shared entries

14. March 2000 Gio XIT 14 INTERSECTION support Store Ontology Articulation ontology Matching rules that use terms from the 2 source domains Factory Ontology Terms useful for purchasing

15. March 2000 Gio XIT 15 Other Basic Operations typically prior intersections UNION: merging entire ontologies DIFFERENCE: material fully under local control Arti- culation ontology

16. March 2000 Gio XIT 16 Features of an algebra Operations can be composed Operations can be rearranged Alternate arrangements can be evaluated Optimization is enabled The record of past operations can be kept and reused when sources change

17. March 2000 Gio XIT 17 Knowledge Composition Articulation knowledge for UUUU U (A(AB)B) U (B(BC)C) U (C(CE)E) Knowledge resource B Knowledge resource A Knowledge resource C Knowledge resource D U (C(CD)D) U (B(BC)C) Articulation knowledge Composed knowledge for applications using A,B,C,E Knowledge resource E U (C(CE)E) Legend: U : union U : intersection Articulation knowledge for (A(A B)B) U

18. March 2000 Gio XIT 18 Primitive Operations Unary Summarize -- abstract Glossarize - list terms Filter - reduce instances Extract - move into context Binary Match - data corrobaration Difference - distance measure Intersect - use of articulation Union - search broadening Constructors create object create set Connectors match object match set Editors insert value edit value move value delete value Converters object - value object indirection reference indirection Model and Instance

19. March 2000 Gio XIT 19 Exploiting the result. Processing & query evaluation is best performed within Source Domains & by their engines Result has links to source Avoid n 2 problem of interpreter mapping [Swartout HPKB year 1]

20. March 2000 Gio XIT 20 Domain Specialization. Knowledge Acquisition (20% effort) & Knowledge Maintenance (80% effort *) to be performed Domain specialists Professional organizations Field teams of modest size Empowerment automously maintainable * based on experience with software

21. March 2000 Gio XIT 21 Summary To sustain the trend 1. The value of the results has to keep increasing precision, relevance not volume 2. Value is provided by experts, encoded as models of diverse resources, customers Problems to be addressed mismatches quality temporal extensions maintenance } Clear models Thanks to Jan Jannink, Shrish Agarwal, Prasenjit Mitra, Stefan Decker.