The concept information of graph granule with application to knowledge graph embedding

Abstract

Knowledge graph embedding (KGE) has become one of the most effective methods for the numerical representation of entities and their relations in knowledge graphs. Traditional methods primarily utilise triple facts, structured as (head entity, relation, tail entity), as the basic knowledge units in the learning process and use additional external information to improve the performance of models. Since triples are sometimes less than adequate and external information is not always available, obtaining structured internal knowledge from knowledge graphs (KGs) naturally becomes a feasible method for KGE learning. Motivated by this, this paper employs formal concept analysis (FCA) to mine deterministic concept knowledge in KGs and proposes a novel KGE model by taking the concept information into account. More specifically, triples sharing the same head entity are organised into knowledge structures named graph granules, and then were transformed into concept lattices, based on which a novel lattice-based KGE model (TransGr) is proposed for knowledge graph completion. TransGr assumes that entities and relations exist in different granules and uses a matrix (obtained by fusing concepts from concept lattice) for quantitatively depicting the graph granule. Afterwards, it forces entities and relations to meet graph granule constraints when learning vector representations of KGs. Experiments on link prediction and triple classification demonstrated that the proposed TransGr is effective on the datasets with relatively complete graph granules.