Home // International Journal On Advances in Intelligent Systems, volume 13, numbers 1 and 2, 2020 // View article

Domain Specific Knowledge Graph Embedding for Analogical Link Discovery

Authors:
Nada Mimouni
Jean-Claude Moissinac
Anh-Tuan Vu

Keywords: Domain knowledge base; Context graph; Entity embedding; Neural language model; Analogy structure; Facts discovery.

Abstract:
General purpose knowledge bases such as DBpedia and Wikidata are valuable resources for various AI tasks. They describe real-world facts as entities and relations between them and they are typically incomplete. Knowledge base completion refers to the task of adding new missing links between entities to build new triples. In this work, we propose an approach for discovering implicit triples using observed ones in the incomplete graph leveraging analogy structures deducted from a knowledge graph embedding model. We use a neural language modelling approach where semantic regularities between words are preserved, which we adapt to entities and relations. We consider domain specific views from large input graphs as the basis for the training, which we call context graphs, as a reduced and meaningful context for a set of entities from a given domain. Results show that analogical inferences in the projected vector space is relevant to a link prediction task in domain knowledge bases.

Pages: 140 to 150

Copyright: Copyright (c) to authors, 2020. Used with permission.

Publication date: June 30, 2020

Published in: journal

ISSN: 1942-2679