关键词: knowledge enhanced entity linking, entity linking, biography construction, Markov logic network, Knowledge

Abstract: Biography is a direct and extensive way to know the representation of well known peoples, however, for common people, there is poor knowledge for them to be recognized. In recent years, information extraction (IE) technologies have been used to automatically generate biography for any people with online information. One of the key challenges is the entity linking (EL) which can link biography sentence to corresponding entities. Currently the used general EL systems usually generate errors originated from entity name variation and ambiguity. Compared with general text, biography sentences possess unique yet rarely studied relational knowledge (RK) and temporal knowledge (TK), which could sufficiently distinguish entities. This article proposed a new statistical framework called the knowledge enhanced EL (KeEL) system for automated biography construction. It utilizes commonsense knowledge like PK and TK to enhance Entity Linking. The performance of KeEL on Wikipedia data was evaluated. It is shown that, compared with state-of-the-art method, KeEL significantly improves the precision and recall of Entity Linking.

Key words: knowledge enhanced entity linking, entity linking, biography construction, Markov logic network, Knowledge