S calable K nowledge C omposition Ontology Interoperation January 19, 1999 Jan Jannink, Prasenjit...

Scalable

Knowledge

Composition

Ontology Interoperation

January 19, 1999

Jan Jannink, Prasenjit Mitra, Srinivasan Pichai,

Danladi Verheijen, Gio Wiederhold

Database Group (Infolab), Stanford University

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Road Map

• SKC Project Overview

– The Problem» The Approach

» Issues

» Example: NATO Web

– The Algebra & Its Application

–Conclusion & Future Directions

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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

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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

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Example: NATO Web

• URLshttp://www.nato.int/family/countries.htm

http://www.nato.int/php/partners.htm

• Partial Contentslegislature (parliament, house, senate)governmentstate head prime minister

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Austria

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England

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Finland

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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

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Road Map

• SKC Project Overview

– The Problem

– The Algebra & Its Application» Unary Operators

» Binary Operators

» Rule Primitives

» Application: Intersection

–Conclusion & Future Directions

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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

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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

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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

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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

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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

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Road Map

• SKC Project Overview

– The Problem

– The Algebra & Its Application

–Conclusion & Future Directions» Future Work

» Summary

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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.

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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