Collective entity linking via greedy search and Monte Carlo calculation. (22nd October 2019)

• Record Type: Journal Article
• Authors: Chen, Lei; Wu, Chong
• Is Part Of: International journal of computational science and engineering. Volume 20:Number 1(2019)
• Page Start: 59
• Page End: 68
• Publication Date: 2019-10-22
• Subjects: collective entity linking -- relationship calculation -- Monte Carlo calculation -- greedy search -- computational science -- engineering; Computer science -- Mathematics -- Periodicals; Computer simulation -- Mathematical aspects -- Periodicals; Computational intelligence -- Periodicals; 004.015105
• Languages: English
• ISSNs: 1742-7185

Abstract

Facing the large amount of entities appearing on the web, entity linking becomes popular recently. It assigns an entrance of a resource to one entity to help users grasp the meaning of this entity. Apparently, the entities that usually co-occur are related and can be considered together to find their best assignments. This approach is called collective entity linking and is often conducted based on entity graph. However, traditional collective entity linking methods either consume much time due to the large-scale of entity graph or obtain low accuracy due to simplifying graph to boost speed. To improve both accuracy and efficiency, this paper proposes a novel collective entity linking method based on greedy search and Monte Carlo calculation. Experimental results show that our linking algorithm can obtain both accurate results and low running time meanwhile.