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SKC: Scalable Knowledge Composition Ontology Interoperation

This project overview discusses the problem of integrating knowledge from multiple sources and presents the approach of using the algebra to compose ontologies. It addresses issues such as semantic mismatch, incomplete specifications, and inconsistent data. An example of applying the approach to NATO web sources is provided. The future directions include estimating maintenance costs and enabling inference within the source of contexts.

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SKC: Scalable Knowledge Composition Ontology Interoperation

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  1. S K C Scalable Knowledge Composition Ontology Interoperation January 19, 1999 Jan Jannink, Prasenjit Mitra, Srinivasan Pichai, Danladi Verheijen, Gio Wiederhold Database Group (Infolab), Stanford University

  2. Road Map • SKC Project Overview • The Problem • The Approach • Issues • Example: NATO Web • The Algebra & Its Application • Conclusion & Future Directions Prasenjit Mitra SKC 2

  3. The Approach • Integration of Knowledge from Multiple Sources • Preserve the autonomy of sources • Compose ontologies using the algebra • Spreads the maintenance cost • Scales smoothly to more complex inferences • Reuse existing sources and knowledge for new applications Prasenjit Mitra SKC 3

  4. Issues • Semantic Mismatch • mismatch in terms • automatic discovery and resolution expensive • difficulty in processing and matching terms • Incomplete Specifications • full semantics not specified • Inconsistent Data • data from multiple sources inconsistent Prasenjit Mitra SKC 4

  5. Example: NATO Web • URLs http://www.nato.int/family/countries.htm http://www.nato.int/php/partners.htm • Partial Contents • legislature (parliament, house, senate) • government • state head • prime minister Prasenjit Mitra SKC 5

  6. Austria Prasenjit Mitra SKC 6

  7. England Prasenjit Mitra SKC 7

  8. Finland Prasenjit Mitra SKC 8

  9. SKC methodology • Construct an embedding for a frame-like object in terms of semistructured data as in the OEM data model • A rule language for explicitly resolving semantic mismatches and for restructured views • Contexts over semistructured data using the rules to circumscribe areas of interest (similar to views over relations) • Unary and Binary operations on these contexts Prasenjit Mitra SKC 9

  10. Road Map • SKC Project Overview • The Problem • The Algebra & Its Application • Unary Operators • Binary Operators • Rule Primitives • Application: Intersection • Conclusion & Future Directions Prasenjit Mitra SKC 10

  11. Unary operators • Flatten : Build a glossary of terms from an ontology • Circumscribe : Induce a restricted ontology which is of interest for a specific application. The articulation rules work only on the circumscribed ontology. • Filter : Select the instance objects satisfying a specific condition Prasenjit Mitra SKC 11

  12. Binary Operators • A knowledge based algebra for contexts. • Binary operations • Intersection : Find the common schema and instances between contexts • Union : Compose contexts to enrich information • Difference : Determine the transform between contexts Prasenjit Mitra SKC 12

  13. Rule Primitives • Provide articulation primitives for matching concepts between ontologies and restructuring objects. • Match nodes, Add a Child, Merge nodes, Block nodes etc. • Extraction rules allow us to create contexts from information sources • Create Nodes, Sequence a list • Create explicit nodes to accommodate implicit assumptions • Conversion between instances and schema items permitted Prasenjit Mitra SKC 13

  14. Application: Intersection • Restructuring of two NATO graphs • 1: Extract the two labeled graphs from the NATO web sources • 2: Match the two graphs to identify corresponding nodes • 3: Filter out only matching nodes and restructure one graph to match the structure of the other Prasenjit Mitra SKC 14

  15. Application: Intersection • Matching of Nodes • Content Based Matching • Construct list of labels describing each node • Preprocess labels (if required, to root words) • Rule-based matching • Type checking • Generate heuristic estimates of extent of match • Accept or reject match based on threshold • Structure Based Matching Prasenjit Mitra SKC 15

  16. Road Map • SKC Project Overview • The Problem • The Algebra & Its Application • Conclusion & Future Directions • Future Work • Summary Prasenjit Mitra SKC 16

  17. Future Work • Estimate maintenance costs to validate our claims • n sources of size s ; m articulation agents • Is n * maint[s] + m * maint[agent] < maint[n * s] • Enable inference within the source of contexts • Proofs on properties of the operators and rewriting expressions. Prasenjit Mitra SKC 17

  18. Summary • Algebra enables interoperation by • dealing explicitly with differences using rulesets • keeping source domains autonomous • Assumes domain has a common ontology • composing domain ontologies requires the algebra to manage the linkages where articulation occurs • Articulation knowledge is distributed • allows specialists to work independently • supports multiple intersections and views • Maintenance is structured and partitioned Prasenjit Mitra SKC 18

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