Fine-grained entity typing for knowledge base completion

Yidong Jia, Weiran Xu, Pengda Qin, Zuyi Bao

Abstract: Most work on knowledge base completion focuses on relations between entities, while entity types are also important knowledge. This paper addresses the problem of fine-grained entity typing for knowledge base completion. Context information plays a vital role in fine-grained entity typing, hence there is an urgent need to find ideal context representations. This paper presents a new approach CNNJM (convolutional neural network joint model) to learn the embeddings of the entities and their contextual information using convolutional neural network and correctly categorize the entities into their fine-grained type classes. We show that CNNJM outperforms state-of-art methods on a fine-grained entity typing benchmark.