Expanding Domain-Specific Knowledge Graphs with Unknown Facts

Abstract

Many knowledge graphs have been created to support intelligent applications, such as search engines and recommendation systems. Some domain-specific knowledge graphs contain similar contents in nature (e.g., the FreeBase contains information about actors and movies which are the core of the IMDB). Adding relevant facts or triples from one knowledge graph into another domain-specific knowledge graph is key to expanding the coverage of the knowledge graph. The facts from one knowledge graph may contain unknown entities or relations that do not occur in the existing knowledge graphs, but it doesn’t mean that these facts are not relevant and hence can not be added to an existing domain-specific knowledge graph. However, adding irrelevant facts will violate the inherent nature of the existing knowledge graph. In other words, the facts that conform to the subject matter of the existing domain-specific knowledge graph only can be added. Therefore, it is vital to filter out irrelevant facts in order to avoid such violations. This paper presents an embedding method called UFD to compute the relevance of the unknown facts to an existing domain-specific knowledge graph so that the relevant new facts from another knowledge graph can be added to the existing domain-specific knowledge graph. A new dataset, called UFD-303K, is created for evaluating unknown fact detection. The experiments show that our embedding method is very effective at distinguishing and adding relevant unknown facts to the existing knowledge graph. The code and datasets of this paper can be obtained from GitHub (https://github.com/MiaoHu-Pro/UFD ).