关键词: 实体关系, 谓词概念模型（PCM）, 概念相关度, 概念连通度

Abstract: Chinese entities relation extraction task is a research focus of text retrieval and knowledge discovery in the open corpus. In the traditional extraction strategies, there exist some problems such as heavy workload of manual annotating, poor pattern versatility and relatively fixed relational granularity, etc. All these restrict the extraction effect in open corpus especially. This paper builds the predicate concept model (PCM) relying on hierarchical structure and relational connectivity of concept, proposes the predicate concept acquisition strategy for incremental concept learning (PCIA), achieves the extraction strategy based on predicate concept connectivity (PCCS), and carries out the untight, long-distant relation extraction ultimately. The construction of the formal concepts is relatively independent, and the combination of concept granularities is more flexible. Therefore, the description approach of the relationship has a better versatility and interpretability, and provides an effective means for unknown relationship identifying and extracting in the open corpus. The experimental results show that PCCS improves the effect of entities identification and entities connectivity path choice, and obtains good entities relation extracting performance.

Key words: entities relationship, predicate concept model (PCM), concept association degree, concept connectivity