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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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Prasenjit Mitra SKC 2
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Road Map
• SKC Project Overview
– The Problem» The Approach
» Issues
» Example: NATO Web
– The Algebra & Its Application
–Conclusion & Future Directions
Prasenjit Mitra SKC 3
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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
Prasenjit Mitra SKC 4
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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
Prasenjit Mitra SKC 5
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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
Prasenjit Mitra SKC 6
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Austria
Prasenjit Mitra SKC 7
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England
Prasenjit Mitra SKC 8
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Finland
Prasenjit Mitra SKC 9
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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
Prasenjit Mitra SKC 10
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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
Prasenjit Mitra SKC 11
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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
Prasenjit Mitra SKC 12
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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
Prasenjit Mitra SKC 15
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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
Prasenjit Mitra SKC 16
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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.
Prasenjit Mitra SKC 18
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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