Abstract
Techniques that map the entities and relations of the knowledge graph (KG) into a low-dimensional continuous space are called KG embedding or knowledge representation learning. However, most existing techniques learn the embeddings based on the facts in KG alone, suffering from the issues of imperfection and spareness of KG. Recently, the research on textual information in KG embedding has attracted much attention due to the rich semantic information supplied by the texts. Thus, in this paper, a survey of techniques for textual information based KG embedding is proposed. Firstly, we introduce the techniques for encoding the textual information to represent the entities and relations from perspectives of encoding models and scoring functions, respectively. Secondly, methods for incorporating the textual information in the existing embedding techniques are summarized. Thirdly, we discuss the training procedure of textual information based KG embedding techniques. Finally, applications of KG embedding with textual information in the specific tasks such as KG completion in zero-shot scenario, multilingual entity alignment, relation extraction and recommender system are explored. We hope that this survey will give insights to researchers into textual information based KG embedding.
Highlights
Recent years, knowledge graph (KG) has experienced rapid development
Tang et al [43] propose Multi-source Knowledge Representation Learning (MKRL), introducing the position embedding and attention mechanism [52] in Convolutional neural network (CNN) to encode the lexicalized dependency paths extracted from the textual mentions
DIRECTIONS Much work has been done to handle the sparseness of KG and enhance the performance of embedding with the textual information
Summary
KG has experienced rapid development. Some typical achievement have been constructed and published, e.g., YAGO [1], Freebase [2] and DBpedia [3]. Methods that utilize the textual information in KG embedding start to get attention [25]–[29] We category these works according to if the representation is built from the textual informaiton: (i) Text-based KG embedding: The textual information are encoded to represent the entities and relations. In this paper, we propose a comprehensive review on techniques that utilize the textual information in KG embedding, including state-of-the-art and latest trends. They are classified into two catogories based on whether the work builds the representation form the texts.
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