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

Fatiha Saïs (1, 2) , Joana E Gonzales Malaverri (1, 2) , Gianluca Quercini (1, 2)

1 LaHDAK - LRI - Données et Connaissances Massives et Hétérogènes (LRI)
2 LRI - Laboratoire de Recherche en Informatique

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.

Mots clés: Knowledge Graphs; Knowledge Graph Enrichment; Temporal Information Inference; Fact Validity; Temporal Meta-Facts; Logical Rules; RDF

Domaines: Intelligence artificielle [cs.AI]

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⟩