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