Ontology matching is the process of automatically determining the semantic equivalences between the concepts of two ontologies. Most ontology matching algorithms are based on two types of strategies: terminology-based strategies, which align concepts based on their names or descriptions, and structure-based strategies, which exploit concept hierarchies to find the alignment. In many domains, there is additional information about the relationships of concepts represented in various ways, such as Bayesian networks, decision trees, and association rules. We propose to use the similarities between these relationships to find more accurate alignments. We accomplish this by defining soft constraints that prefer alignments where corresponding concepts have the same local relationships encoded as knowledge rules. We use a probabilistic framework to integrate this new knowledge-based strategy with standard terminology-based and structure-based strategies. Furthermore, our method is particularly effective in identifying correspondences between complex concepts. Our method achieves better F-score than the state-of-the-art on three ontology matching domains.
Ontology Matching with Knowledge Rules
Shangpu Jiang,Daniel Lowd,D. Dou
Published 2015 in Trans. Large Scale Data Knowl. Centered Syst.
ABSTRACT
PUBLICATION RECORD
- Publication year
2015
- Venue
Trans. Large Scale Data Knowl. Centered Syst.
- Publication date
2015-07-11
- Fields of study
Computer Science
- Identifiers
- External record
- Source metadata
Semantic Scholar
CITATION MAP
EXTRACTION MAP
CLAIMS
CONCEPTS
- complex concepts
Ontology concepts with richer internal structure or multiple parts that make correspondence harder to identify.
- f-score
The evaluation metric used to summarize precision and recall for the matching results.
Aliases: F1 score
- knowledge rules
Explicit rules encoding relationships among concepts that are used as additional matching knowledge in this approach.
Aliases: knowledge-based rules
- ontology matching
The task of automatically determining semantic equivalences between concepts in two ontologies.
- ontology matching domains
The three application settings or datasets used to evaluate the matching method.
Aliases: domains
- probabilistic framework
A probabilistic integration layer that combines multiple ontology-matching strategies in one model.
- soft constraints
Preferences in the matching objective that encourage alignments with desired relational properties without enforcing them absolutely.
- structure-based strategies
Matching strategies that exploit ontology hierarchies and structural relations to infer correspondences.
- terminology-based strategies
Matching strategies that rely on concept names or descriptions to find correspondences.
REFERENCES
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CITED BY
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