关键词: 关系抽取, 关系推理, 远程监督, 规则推理引擎

Abstract: Entity relation extraction refers to extract semantic relationships between entities from unstructured natural language text and express in a structured form. Traditional entity relation extraction methods only focus on a particular type of data source, and label large numbers of training data by humans to train extraction model. Manually labeling training data are labor-intensive and time consuming. So this paper proposes a method integrating diversity data sources, and combines rule-based inference engine to discover relation triples. More precisely, integrating structured and unstructured data sources, and in the case of having small amount of seeds provided by structured data, a large number of entity relationships are reasoned by rule-based inference engine. The newly entity relationships are fed as seeds to distantly supervise the learning process to extract entity relationships from unstructured text. The final entity relationships are obtained through multiple iterations. The experimental results show the effectiveness of the proposed method.

Key words: relation extraction, relation reasoning, distant supervision, rule-based inference engine