An unsupervised learning method for named entity relation extraction of space knowledge graph

Abstract

Knowledge graph is a kind of typical artificial intelligence technology, which can improve cognitive ability of machines. Space is a booming domain for human beings. In this paper, space knowledge graph is proposed in order to improve intelligence of space information system. A major obstacle of space knowledge graph construction is to identify and extract core named entities and relations in space domain. An unsupervised learning method to extract core named entities and relations is proposed in this paper. Firstly, confidence level index used to measure importance of entities and conditional confidence level index used to measure credibility of relations between entities are defined and investigated. Then, an entity and relation extraction procedure based on maximum likelihood estimation and apriori algorithm is designed. Finally, a case study is carried out and influence of super parameter setting on the extraction result is discussed. The method proposed in this paper can be used to construct space knowledge graph at a lower cost compared to supervised method, and can be extended to other domain knowledge graph construction.