Title: Collective entity linking via greedy search and Monte Carlo calculation
Authors: Lei Chen; Chong Wu
Addresses: International Business Faculty, Beijing Normal University, Zhuhai, Guangdong Province, China; School of Management, Harbin Institute of Technology, Harbin, Heilongjiang Province, China ' School of Management, Harbin Institute of Technology, Harbin, Heilongjiang Province, China
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.
Keywords: collective entity linking; relationship calculation; Monte Carlo calculation; greedy search; computational science; engineering.
International Journal of Computational Science and Engineering, 2019 Vol.20 No.1, pp.59 - 68
Received: 18 Oct 2016
Accepted: 07 May 2017
Published online: 23 Oct 2019 *