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Conference Papers Year : 2020

MOMENT: temporal meta-fact generation and propagation in knowledge graphs

Abstract

This paper deals with the problem of temporal meta-fact generation in RDF knowledge graphs (KGs). These temporal meta-facts represent the time validity of facts, for instance, is valid for the period [2008..2016]. We propose an approach called MOMENT that combines two methods, the first uses a set of specified rules to generate meta-facts in knowledge bases where no temporal meta-fact exist. The second method exploits existing temporal meta-facts and a set of Horn rules generated by AMIE [9] to propagate the meta-facts and thus expand the set of temporal meta-facts. An experimental evaluation has been conducted using Yago, DBpedia and Wikidata datasets. The obtained results are promising and showed the relevance of such an approach for temporal meta-fact generation in Knowledge Graphs.
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hal-04420928 , version 1 (27-01-2024)

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Fatiha Saïs, Joana E Gonzales Malaverri, Gianluca Quercini. MOMENT: temporal meta-fact generation and propagation in knowledge graphs. Proceedings of the 35th Annual ACM Symposium on Applied Computing, Mar 2020, Brno, Czech Republic. ⟨10.1145/3341105.3374021⟩. ⟨hal-04420928⟩
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