Representation Learning of Knowledge Graph with Semantic Vectors

Knowledge graph (KG) is a structured semantic knowledge base, which is widely used in the fields of semantic search, such as intelligent Q&A and intelligent recommendation. Representation learning, as a key issue of KG, aims to vectorize entities and relations in KG to reduce data sparseness and improve computational efficiency. Translation-based representation learning model shows great knowledge representation ability, but there also are limitations in complex relations modeling and representation accuracy. To address these problems, this paper proposes a novel representation learning model with semantic vectors, called TransV, which makes full use of external text corpus and KG's context to accurately represent entities and complex relations. Entity semantic vectors and relation semantic vectors are constructed, which can not only deeply extend semantic structure of KG, but also transform complex relations into precise simple relations from a semantic perspective. Link prediction and triple classification tasks are performed on TransV with public datasets. Experimental results show that TransV can outperform other translation-based models. Mean Rank is reduced by 66 and Hits@10 is increased by 20% on average for link prediction task on FB15K.