DBpedia-Based Entity Linking via Greedy Search and Adjusted Monte Carlo Random Walk

Abstract

Facing a large amount of entities appearing on the web, entity linking has recently become useful. It assigns an entity from a resource to one name mention to help users grasp the meaning of this name mention. Unfortunately, many possible entities can be assigned to one name mention. Apparently, the usually co-occurring name mentions are related and can be considered together to determine 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 improve both accuracy and efficiency, this article proposes a novel collective entity linking algorithm. It first constructs an entity graph by connecting any two related entities, and then a probability-based objective function is proposed on this graph to ensure the high accuracy of the linking result. Via this function, we convert entity linking to the process of finding the nodes with the highest PageRank Values. Greedy search and an adjusted Monte Carlo random walk are proposed to fulfill this work. Experimental results demonstrate that our algorithm performs much better than traditional linking methods.