Representation Learning Based Relational Inference Algorithm with Semantical Aspect Awareness

Knowledge representation based relational inference algorithms is a crucial research issue in the field of statistical relational learning and knowledge graph population in recent years. In this work, we perform a comparative study of the prevalent knowledge representation based reasoning models, with detailed discussion of the general potential problems contained in their basic assumptions. The major problem of these representation based relational inference models is that they often ignore the semantical diversity of entities and relations, which will cause the lack of semantic resolution to distinguish them, especially when there exists more than one type of relation between two given entities. This paper proposes a new assumption for relation reasoning in knowledge graphs, which claims that each of the relations between any entity pairs reflects the semantical connection of some specific attention aspects of the corresponding entities, and could be modeled by selectively weighting on the constituent of the embeddings to help alleviating the semantic resolution problem. A semantical aspect aware relational inference algorithm is proposed to solve the semantic resolution problem, in which a nonlinear transformation mechanism is introduced to capture the effects of the different semantic aspects of the embeddings. Experimental results on public datasets show that the proposed algorithms have superior semantic discrimination capability for complex relation types and their associated entities, which can effectively improve the accuracy of relational inference on knowledge graphs, and the proposed algorithm significantly outperforms the state-of-the-art approaches.