Triple-as-Node Knowledge Graph and Its Embeddings

Xin Lv, Jiaxin Shi, Shulin Cao, Lei Hou, Juanzi Li

Abstract: Knowledge Graphs (KGs) aim at semantically representing the world’s knowledge in the form of machine-readable graphs composed of subject-relation-object triples (facts). However, most previous KGs only consider the relationship between individual entities, ignoring connections between facts and entities, which are commonly used to depict useful information about the properties of facts. To this end, we formally introduce FactKG, a new KG form which incorporates fact nodes and extends relations from entity-level to fact-level. This new structure challenges some previous KG techniques. One of the key challenges to FactKG is how to learn compatible representation of entities and facts. In this paper, we mainly focus on the embedding task of FactKG. We contribute a benchmark WD16K with additional fact-relevant relations, and a framework FactE, which can represent facts, entities and relations in the same space via attention. Experiments demonstrate that FactE not only significantly outperforms state-of-the-art models but also brings remarkable benefits for disambiguation of 1-N relations, revealing its potential usefulness.