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Ontology Matching Basics - PL, CS 652 1
Ontology Matching Basics
Ontology Matchingby Jerome Euzenat and Pavel Shvaiko
Parts I and II
11/6/2012
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1 - Applications
1.1 Ontology engineering1.2 Information integration1.3 Peer-to-peer information sharing1.4 Web service composition1.5 Autonomous communication systems1.6 Navigation and query answering on the web
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2 – The matching problem
2.1 Vocabularies, schemas and ontologies2.2 Ontology language2.3 Types of heterogeneity2.4 Terminology2.5 The ontology matching problem
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2.1 Vocabularies, schemas and ontologies
• Tags and folksonomies• Directories• Relational database schemas• XML schemas• Conceptual models• Ontologies – model-theoretic semantics,
“ontologies are logic theories”
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2.2 Ontology language (OWL)
• Entities:– Classes– Individuals– Relations– Datatypes– Data values
• Entity relations– Specialization– Exclusion– Instantiation
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2.3 – Types of heterogeneity
• Syntactic heterogeneity– Not expressed in the same ontology language
• Terminological heterogeneity– Variation in names for the same entity
• Conceptual heterogeneity– Differences in coverage, granularity, or perspective
• Semiotic (pragmatic) heterogeneity– How entities are interpreted by people
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3 – Classification of ontology matching techniques
3.1 Matching dimensions- Input dimensions- Process dimensions- Output dimensions
3.2 Classification of matching approaches- Exhaustivity- Disjointedness- Homogeneity- Saturation
3.3 Other classifications- Horizontal: data, ontology, and context layers- Vertical: syntactic, pragmatic, conceptual
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Element-level techniques
• String-based techniques• Language-based techniques• Constraint-based techniques• Linguistic resources• Alignment reuse• Upper level and domain specific formal
ontologies
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Structure-level techniques
• Graph-based techniques• Taxonomy-based techniques• Repository of structures• Model-based techniques• Data analysis and statistical techniques
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4 – Basic techniques
4.1 Similarity, distances and other measures4.2 Name-based techniques4.3 Structure-based techniques4.4 Extensional techniques4.5 Semantic-based techniques
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4.2 – Name-based techniques
• Problem: synonyms and homonyms (polysemy)• String-based methods– Normalization– String equality– Substring test– Edit, token-based, and path distances
• Language-based methods– Intrinsic methods– Extrinsic methods
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4.3 – Structure-based techniques
• Internal structure– Property comparison– Datatype comparison– Domain comparison– Comparing multiplicities
and properties– Other features
• Relational structure– Maximum common
directed subgraph problem
– Taxonomic structure– Mereologic structure– Relation similarities
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4.4 – Extensional techniques
• Common extension comparison– Hamming distance– Jaccard similarity– Formal concept analysis – intent and extent
• Instance identification techniques• Disjoint extension comparison– Statistical approach– Similarity-based extension comparison– Matching-based comparison
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4.5 – Semantic-based techniques
• Model-theoretic, deductive methods• Act to amplify seeding alignments• Techniques based on external ontologies• Deductive techniques– Propositional satisfiability– Modal satisfiability– Description logic techniques
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5 – Matching strategies
5.1 Matcher composition5.2 Similarity aggregation5.3 Global similarity computation5.4 Learning methods5.5 Probabilistic methods5.6 User involvement and dynamic composition5.7 Alignment extraction
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5.4 – Learning methods
• Bayes learning• WHIRL learner• Neural networks• Decision trees• Stacked generalization
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5.6 – User involvement and dynamic composition
• Providing input– Ontologies, parameters, initial alignment
• Manual matcher composition– Assemble from libraries– Examine results and iterate– Apply to application
• Relevance feedback
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5.7 – Alignment extraction
• Select on similarity, extract, and filter
• Thresholds• Strengthening and weakening• Optimizing the result
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Fig. 5.14 displays a fictitious example involving several of the methods. It (i)runs several basic matchers in parallel, (ii) aggregates their results, (iii) selects somecorrespondences on the basis of their (dis)similarity, (iv) extracts an alignment, (v) uses a semantic algorithm to amplify the selected alignment, and (vi) reiterate thisprocess if necessary.