Distributed representation learning for knowledge graphs with entity descriptions

Recent studies of knowledge representation attempt to project both entities and relations, which originally compose a high-dimensional and sparse knowledge graph, into a continuous low-dimensional space. One canonical approach TransE [2] which represents entities and relations with vectors (embeddings), achieves leading performances solely with triplets, i.e. (head_entity, relation, tail_entity), in a knowledge base. The cutting-edge method DKRL [23] extends TransE via enhancing the embeddings with entity descriptions by means of deep neural network models. However, DKRL requires extra space to store parameters of inner layers, and relies on more hyperparameters to be tuned. Therefore, we create a single-layer model which requests much fewer parameters. The model measures the probability of each triplet along with corresponding entity descriptions, and learns contextual embeddings of entities, relations and words in descriptions simultaneously, via maximizing the loglikelihood of the observed knowledge. We evaluate our model in the tasks of knowledge graph completion and entity type classification with two benchmark datasets: FB500K and EN15K, respectively. Experimental results demonstrate that the proposed model outperforms both TransE and DKRL, indicating that it is both efficient and effective in learning better distributed representations for knowledge bases.