Entity Linking Technology For Enterprise Knowledge Graph

In this era of information explosion,there is a huge amount of valuable enterprise information on the Internet.However,most of the information is scattered on different websites,which leads to the lack of hierarchy and logic.It is difficult to achieve automatic association of data from different websites.Therefore,it is particularly important to deal with information intelligently and normatively.The entity linking technology is mainly to solve the problems of diversity,ambiguity and lack of entities.According to the three steps of the supervised entity linking algorithm: named entity recognition,candidate entity generation and candidate entity disambiguation,an entity linking system is designed and used to build an enterprise knowledge graph successfully.The specific work of the paper can be summarized as follows:(1)Build a multi-source knowledge base by selecting Wikipedia Chinese,Baidu Encyclopedia and Interactive Encyclopedia as background knowledge bases.Use the AttBiLSTM-CRF Chinese named entity recognition model to obtain entity referrals.An entity referential extension method combining context matching strategy and knowledge base information retrieval strategy is proposed.Finally,a set of candidate entities with high recall rate and accuracy rate is generated.(2)Two candidate entity ranking algorithms combining neural network and cosine similarity and an empty entity decision method are proposed.The comparison experiments of different scenes are designed.The results show that the obtained candidate entity disambiguation algorithm which selects candidate entity ranking algorithm combining CNN and cosine similarity and adds the empty entity judgment method is the best.(3)The above candidate entity generation algorithm and candidate entity ranking algorithm are redefined as the entity link algorithm herein.Design an entity linking system applied to the enterprise field,and then apply this system to the process of constructing the knowledge graph.Use Neo4 j to build an enterprise knowledge graph successfully.