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COMET: A Contextualized Molecule-Based Matching Technique

: Tasnim, Mayesha; Collarana, Diego; Graux, Damien; Galkin, Mikhail; Vidal, Maria-Esther


Hartmann, S.:
Database and expert systems applications. 30th International Conference, DEXA 2019. Proceedings. Pt.1 : Linz, Austria, August 26-29, 2019
Cham: Springer Nature, 2019 (Lecture Notes in Computer Science 11706)
ISBN: 978-3-030-27614-0 (Print)
ISBN: 978-3-030-27615-7 (Online)
International Conference on Database and Expert Systems Applications (DEXA) <30, 2019, Linz>
European Commission EC
H2020; 822404; QualiChain
Decentralised Qualifications' Verification and Management for Learner Empowerment, Education Reengineering and Public Sector Transformation
Conference Paper
Fraunhofer IAIS ()
data integration; RDF knowledge graphs; RDF entities

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.