Abstract
Knowledge graphs (KGs) have been widely used in the field of artificial intelligence, such as in information retrieval, natural language processing, recommendation systems, etc. However, the open nature of KGs often implies that they are incomplete, having self-defects. This creates the need to build a more complete knowledge graph for enhancing the practical utilization of KGs. Link prediction is a fundamental task in knowledge graph completion that utilizes existing relations to infer new relations so as to build a more complete knowledge graph. Numerous methods have been proposed to perform the link-prediction task based on various representation techniques. Among them, KG-embedding models have significantly advanced the state of the art in the past few years. In this paper, we provide a comprehensive survey on KG-embedding models for link prediction in knowledge graphs. We first provide a theoretical analysis and comparison of existing methods proposed to date for generating KG embedding. Then, we investigate several representative models that are classified into five categories. Finally, we conducted experiments on two benchmark datasets to report comprehensive findings and provide some new insights into the strengths and weaknesses of existing models.
Highlights
Symmetry 2021, 13, 485. https://Knowledge graphs (KGs) have been widely used to store structured semantic information for tasks of artificial intelligence
We synthesized the previous surveys’ ideas; we summarize these models of KG embedding proposed over nearly three years into a classification table, which is intuitive, and we analyze the correlations among these models from a more fine-grained perspective, which involves our five main lines
Knowledge graphs (KGs) are semantic networks that reveal the correlations between entities, which have the abilities of analysis and reasoning like human beings
Summary
Knowledge graphs (KGs) have been widely used to store structured semantic information for tasks of artificial intelligence. KGE models embed entities and relations into a low-dimensional vector space while preserving the structure of the KG and its underlying semantic information These models can be effectively applied to link prediction [14]. The second group of multiplicative models includes DistMult [20] and Complex [21], which can outperform the additive models by capturing more semantic information [19] These models first embed entities and relations into a unified continuous vector space and define a scoring function to measure its authenticity. This paper provides a theoretical analysis and comparison of existing KGE methods for generating KG embeddings for link prediction in KGs. Several representative models in each category are analyzed and compared along five main lines. The main contents of the rest of the article are as follows: Section 2 introduces the concept of the knowledge graph and knowledge graph embedding, as well as the definition of the link-prediction task; Section 3 mainly presents the two types of categories of models and a detailed introduction on representative models; Section 4 presents the experiment and comparative analysis of representative models; Section 5 is the conclusion
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
