Research progress of large-scale knowledge graph completion technology

Abstract

With the continued growth of various knowledge graphs, such as Google Knowledge Map, DBpedia, Microsoft Concept Graph, and YAGO, the knowledge representation system, constructed based on RDF, has become more well-known. The RDF triple format has become the basic description of knowledge in the real world. Due to its simple structure and clear logic, it is easy to understand and implement. Nevertheless, when faced with extremely complicated knowledge and common sense, complete knowledge can become difficult to describe. The construction process of knowledge graphs is bound to lead to incomplete knowledge contained in the graphs. At this point, the knowledge-based completion technology is particularly important for managing such situations. Any existing knowledge graph must be improved continuously through completion technology and newly inferred knowledge. Beginning with the construction of a knowledge graph, this paper divides the problem of knowledge graph completion into two levels: concept completion and instance completion. (1) The concept completion level primarily focuses on the completion of entity types. It is described in terms of three development stages: a logical reasoning mechanism, based on description logic, a type inference mechanism, based on traditional machine learning, and a type inference mechanism, based on representation learning. (2) The instance completion level can be further divided into an RDF triple completion and new instance discovery. This paper focuses on RDF triples completion learning, which includes entity completion or relationship completion and is described in three development stages, such as statistical relational learning, probability learning based on random walks, and knowledge representation learning. Through the review and discussion of the research process, the development status, and the latest progress in the above-mentioned large-scale knowledge graph completion, we present the challenges that the technology will face and the development prospects of future work.