Abstract: Knowledge bases (KBs) store entities,entity attributes and relations between entities in a structured manner.Because the knowledge in the KBs can be easily processed by computers,KBs play a vital role in many natural language processing (NLP) tasks.Although current KBs contain massive triple knowledge from the perspective of absolute quantity,they are far less than the knowledge existing in real world.Therefore,many researches focus on how to enrich the knowledge base with more high-quality knowledge.Internal reasoning and extracting from external resources are two main kinds of methods for knowledge base completion,but they still need to be improved.On the one hand,since the knowledge in KBs are not perfect and some errors exist,reasoning on such error knowledge will cause error propagation.On the other hand,existing extracting methods usually focus on limited relations and properties and thus cannot find comprehensive knowledge from external resources such as texts.In light of this,this paper proposed a knowledge reasoning method based on unstructured text-enhanced association rules.In this method,the text representation pattern is abstracted from the unstructured text firstly,then it is represented in the form of a bag of distribution,and the associa-tion rules can be mined through combining the knowledge of KBs.The difference from the traditional association rules is that the association rules obtained by the proposed method can directly match unstructured texts for knowledge reasoning.Experimental results show that the proposed method can efficiently infer triple knowledge from unstructured text with higher quality and larger quantity compared with traditional methods.

Key words: Association rules, Knowledge bases completion, Knowledge reasoning, Text-enhanced, Triple knowledge