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September 2001

C. K. S. Scalable Knowledge Composition. September 2001 Gio Wiederhold, Shrish Agarwal, Stefan Decker, Jan Janninck, Prasenjit Mitra, et al. Stanford University, CSD. Data + Knowledge  Information. Apply relevant Knowledge to relevant Data. Analyses.

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September 2001

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  1. C K S Scalable Knowledge Composition September 2001 Gio Wiederhold, Shrish Agarwal, Stefan Decker, Jan Janninck, Prasenjit Mitra, et al. Stanford University, CSD SKC Synopsis

  2. Data + KnowledgeInformation • Apply relevant Knowledge to relevant Data Analyses Composition of source information SKC focus Aggregation of instances Selection Observations Quality Filters • to obtain: Information for decision-making SKC Synopsis

  3. Many sources, disciplines, people Extraction of actionable information, so that future benefits can accrue, requires broad-based knowledge Areas make deep progress in isolation Benefits are possible when solid results are available the results are Integrated or Composed Broad base leads to heterogeneity and inconsistency of terminologies SKC Synopsis

  4. Language differences inhibit integration • An essential feature of science • autonomy of fields • differing granularity and scope of focus • growth of fields requires new terms • A feature of technological process • standards require stability • yesterday’s innovations are today’s infrastructure • today’s innovations are tomorrow’s infrastructure • Must be dealt with explicitly • sharing, integration, and aggregation are essential • large quantities of data require precision SKC Synopsis

  5. Semantic Mismatches Autonomous sources in all domains have • Differing viewpoints ( by source ) • differing terms for similar items { lorry, truck } • same terms for dissimilar items trunk( luggage, car) • differing coverage vehicles ( DMV, police, AIA ) • differing granularity trucks ( shipper, manuf. ) • different scope student ( museum fee, Stanford ) • different hierarchical structures supplier vs. usage  • 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 & science SKC Synopsis

  6. Heterogeneity among Domains is natural Interoperation creates mismatch • Autonomy conflicts with consistency, • Local Needs have Priority, • Outside uses are a Byproduct Heterogeneity must be addressed • Platform and Operating Systems  • Data Representation and Access Conventions  • Metadata: Naming and Ontology  • needed to share data from distinct sources SKC Synopsis

  7. Two Mismatch Solutions • A Single, Globally consistent Ontology ( Your Hope ) • wonderful for users and their programs • too many interacting sources • long time to achieve,2 sources ( UAL, LH ), 3 (+ trucks), 4, … all ? • costly maintenance, since all sources evolve • no world-wide authority to dictate conformance • Domain-specific ontologies ( XML DTD assumption ) • Small, focused, cooperating groups • high quality, some examples - arthritis, Shakespeare plays • allows sharable, formal tools • ongoing, local maintenance affecting users - annual updates • poor interoperation, users still face inter-domain mismatches SKC Synopsis

  8. Our approach(SKC project) 1. Define Terminology in a domain precisely • Schemas, XML DTDs  Ontologies • Develop methods to permit interoperation among differing domains (not integration) • Articulation --- support the limited interoperation needed to solve problems in an application domain • Ontology Algebra --- enable scalability to as many sources as are needed to support applications • Develop tools to support the methods • Ontology matching SKC Synopsis

  9. Intersection create a subset ontology • keep sharable entries • Union create a joint ontology • merge entries • Difference create a distinct ontology • remove shared entries An Ontology Algebra The glue that holds the bricks together A knowledge-based algebra for ontologies The Articulation Ontology (AO) consists of matching rules that link domain ontologies SKC Synopsis

  10. Sample Operation: INTERSECTION Result contains shared terms Articulates the two domains Terms useful for purchasing Source Domain 1: Owned and maintained by Store Source Domain 2: Owned and maintained by Factory SKC Synopsis

  11. Shoe Factory • Material inventory {...} • Employees { . . . } • Machinery { . . . } • Processes { . . . } • Shoes { . . . } Shoe Store • Shoes { . . . } • Customers { . . . } • Employees { . . . } Sample Intersections Articulation ontology matching rules : size = size color =table(colcode) style = style Ana- tomy {. . . } Hard- ware foot = foot Employees Employees Nail (toe, foot) Nail (fastener) . . . . . . Department Store SKC Synopsis

  12. No committee is needed to forge compromises * within a domain Within a Domain Terms have clear Meanings • 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 Domain Ontology • Compromises hide valuable details SKC Synopsis

  13. SKC grounded definition. • Ontology: a set of terms and their relationships • Term: a reference to real-world and abstract objects • Relationship: a named and typed set of links between objects • Reference: a label that names objects • Abstract object: a concept which refers to other objects • Real-world object: an entity instance with a physical manifestation (or its representation in a factual database) SKC Synopsis

  14. Grounding enables implementation • We use many abstract terms in our work • Needed because we are dealing with many objects • Human thinking is limited to short-term memory • Someone must be able to translate them into code reliably • Each abstract term must have a path to reality • One must provide that path for • students and • coders • Without a clear path that is not possible • Not automatically at all – machines need specs • Not reliably by human programmers – failures occur • Without implementation there is no benefit SKC Synopsis

  15. INTERSECTION support Articulation ontology Matching rules that use terms from the 2 source domains Terms useful for purchasing Store Ontology Factory Ontology SKC Synopsis

  16. Arti- culation ontology Other Basic Operations DIFFERENCE: material fully under local control UNION: merging entire ontologies typically prior intersections SKC Synopsis

  17. Features of an algebra • The record of past operations can be • kept and reused • (experience: 3 months  1 week for Webster's annual update, •  2 weeks for OED (6 x size) [Jannink:01]) • Maintenance is enabled by using • remote, deep domain expertise • rapid recomposition for application domain • Expect also that • Operations can be composed • Operations can be rearranged • Alternate arrangements can be evaluated • Optimization is enabled SKC Synopsis

  18. What is the most recent year an OPEC member nation was on the UN security council (SC)? SKC resolves 3 Sources CIA Factbook ‘96 (nation) OPEC (members, dates) UN (SC members, years) SKC obtains the Correct Answer 1996 (Indonesia) Other groups obtained more, but factually wrong answers; they relied on one global source, the CIA factbook. Problems resolved by SKC Factbook – a secondary source -- has out of date OPEC & UN SC lists Indonesia not listed Gabon (left OPEC 1994) different country names Gambia => The Gambia historical country names Yugoslavia UN lists future security council members Gabon 1999 needed ancillary data Sample Processingin the DARPA HPKB challenge SKC Synopsis

  19. Interoperation via Articulation Process phases:  At application definition time • Match relevant ontologies where needed • Establish articulation rules among them. • Record the process  At execution time • Perform query rewriting to get to sources • Optimize based on the ontology algebra.  For maintenance • Regenerate rules using the stored formulation SKC Synopsis

  20. Generation of the articulation rules Provide library of automatic match heuristics • Lexical Methods – spelling similarity --- commonly used by others • Structural Methods -- relative graph position • Reasoning-based Methods • Nexus – a graph we derive from the OED / Websters • links terms based on definitions, not lexical similarity • Hybrid Methods • Iteratively, with an expert in control GUI tool to • - display matches and • - verify generated matches using the human expert • - expert can also supply matching rules SKC Synopsis

  21. Articulation Generator Being built by Prasenjit Mitra Thesaurus OntA Context-based Word Relator Phrase Relator Driver Semantic Network (Nexus) Structural Matcher Ont1 Ont2 Human Expert SKC Synopsis

  22. Articulation knowledge for U (A B) U U U (B C) Legend: U (C E) U : union U (C E) U : intersection B) (A U U (B C) (C D) Principle of Knowledge Composition Composed knowledge for applications using A,B,C,E Articulation knowledge Knowledge resource E Articulation knowledge for Knowledge resource C U Knowledge resource A Knowledge resource B Knowledge resource D SKC Synopsis

  23. Exploiting the result (future plans) Avoid n2 problem of interpreter mapping [Swartout HPKB year 1] Result has links to source Processing & query evaluation is best performed within Source Domains & by their engines SKC Synopsis

  24. Empowerment Support Domain Specialization • Knowledge Acquisition (20% effort) & • Knowledge Maintenance (80% effort *) to be performed • Domain specialists (SMEs) • Professional organizations • Field teams of modest size automously maintainable * based on experience with software SKC Synopsis

  25. Domain-specific Expertise. Knowledge needed is huge • Partition into natural domains • Determine domain responsibility and authority • Empower domain owners • Exploit domain-specific expertise • Provide computer-science tools Consider interaction Our Ontology Society of specialists SKC Synopsis

  26. SKC Project Synopsis • Research Objective: • Precise information for applications from heterogeneous, imperfect, scalably many data sources • Sources for Ontologies used currently: • General: CIA World Factbook ‘96, www.UN, www.OPEC Webster’s Dictionary, Thesaurus, Oxford English Dictionary • Topical: NATO, BattleSpace Sensors, Logistics Servers • Theory: • Domain autonomy and exploitation • Rule-based algebra over ontologies • Translation & Composition primitives • Sponsors and collaboration • AFOSR; DARPA DAML program; W3C; Stanford KSL and SMI; Univ. of Karlsruhe, Germany; others. SKC Synopsis

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