Fine-grained type inference in knowledge graphs via probabilistic and tensor factorization methods

Abstract

Knowledge Graphs (KGs) have been proven to be incredibly useful for enriching semantic Web search results and allowing queries with a well-defined result set. In recent years much attention has been given to the task of inferring missing facts based on existing facts in a KG. Approaches have also been proposed for inferring types of entities, however these are successful in common types such as'Person','Movie', or'Actor'. There is still a large gap, however, in the inference of fine-grained types which are highly important for exploring specific lists and collections within web search. Generally there are also relatively fewer observed instances of fine-grained types present to train in KGs, and this poses challenges for the development of effective approaches. In order to address the issue, this paper proposes a new approach to the fine-grained type inference problem. This new approach is explicitly modeled for leveraging domain knowledge and utilizing additional data outside KG, that improves performance in fine-grained type inference. Further improvements in efficiency are achieved by extending the model to probabilistic inference based on entity similarity and typed class classification. We conduct extensive experiments on type triple classification and entity prediction tasks on Freebase FB15K benchmark dataset. The experiment results show that the proposed model outperforms the state-of-the-art approaches for type inference in KG, and achieves high performance results in many-to-one relation in predicting tail for KG completion task.