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Knowledge-Based Matching of $n$-ary Tuples

Pierre Monnin 1 Miguel Couceiro 1 Amedeo Napoli 1 Adrien Coulet 1
1 ORPAILLEUR - Knowledge representation, reasonning
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Abstract : An increasing number of data and knowledge sources are accessible by human and software agents in the expanding Semantic Web. Sources may differ in granularity or completeness, and thus be complementary. Consequently, they should be reconciled in order to unlock the full potential of their conjoint knowledge. In particular, units should be matched within and across sources, and their level of relatedness should be classified into equivalent, more specific, or similar. This task is challenging since knowledge units can be heterogeneously represented in sources (e.g., in terms of vocabularies). In this paper, we focus on matching $n$-ary tuples in a knowledge base with a rule-based methodology. To alleviate heterogeneity issues, we rely on domain knowledge expressed by ontologies. We tested our method on the biomedical domain of pharmacogenomics by searching alignments among 50,435 n-ary tuples from four different real-world sources. Results highlight noteworthy agreements and particularities within and across sources.
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https://hal.inria.fr/hal-02493067
Contributor : Pierre Monnin <>
Submitted on : Tuesday, February 23, 2021 - 8:34:52 PM
Last modification on : Thursday, March 11, 2021 - 2:26:02 PM
Long-term archiving on: : Monday, May 24, 2021 - 9:05:50 PM

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Pierre Monnin, Miguel Couceiro, Amedeo Napoli, Adrien Coulet. Knowledge-Based Matching of $n$-ary Tuples. ICCS 2020 - 25th International Conference on Conceptual Structures, Sep 2020, Bolzano / Virtual, Italy. pp.48-56, ⟨10.1007/978-3-030-57855-8_4⟩. ⟨hal-02493067⟩

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