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Erschienen in:
2019 | OriginalPaper | Buchkapitel
COMET: A Contextualized Molecule-Based Matching Technique
verfasst von : Mayesha Tasnim, Diego Collarana, Damien Graux, Mikhail Galkin, Maria-Esther Vidal
Erschienen in: Database and Expert Systems Applications
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Abstract
Context-specific description of entities –expressed in RDF– poses challenges during data-driven tasks, e.g., data integration, and context-aware entity matching represents a building-block for these tasks. However, existing approaches only consider inter-schema mapping of data sources, and are not able to manage several contexts during entity matching. We devise COMET, an entity matching technique that relies on both the knowledge stated in RDF vocabularies and context-based similarity metrics to match contextually equivalent entities. COMET executes a novel 1-1 perfect matching algorithm for matching contextually equivalent entities based on the combined scores of semantic similarity and context similarity. COMET employs the Formal Concept Analysis algorithm in order to compute the context similarity of RDF entities. We empirically evaluate the performance of COMET on a testbed from DBpedia. The experimental results suggest that COMET is able to accurately match equivalent RDF graphs in a context-dependent manner.