Knowledge Base Entity Typing From Text via Entity-Aware Heterogeneous Graph Attention Network

Knowledge base entity typing from the description text (KBET-X) has become an important research direction, which takes semantically richer descriptive text as input to obtain better typing results. However, existing approaches either consider all sentences in the text but ignore the entities themselves or consider only the sentences in which the entities are mentioned without considering the other sentences in the text. To address these issues, we propose a novel framework for KBET-X based on an entity-aware heterogeneous graph attention network that makes full use of all sentences in the description text and considers the entities themselves. Specifically, we construct a heterogeneous graph for the description text with the node being a word or sentence. Node embeddings are initialized with an entity-aware encoder. Then we use a context encoder to obtain a contextual node representation of each word and sentence, consisting of a heterogeneous graph attention network and a gated recurrent unit (GRU) network. Finally, we use a type decoder based on a multilayer perceptron (MLP) network to obtain the types of each entity. Experiments conducted on the DBpedia dataset show that our method achieves the new state-of-the-art performance. We also conduct an ablation study to demonstrate that each component plays an essential role in our framework.