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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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