A Multi-View–Based Collective Entity Linking Method

Abstract

Facing lots of name mentions appearing on the web, entity linking is essential for many information processing applications. To improve linking accuracy, the relations between entities are usually considered in the linking process. This kind of method is called collective entity linking and can obtain high-quality results. There are two kinds of information helpful to reveal the relations between entities, i.e., contextual information and structural information of entities. Most traditional collective entity linking methods consider them separately. In fact, these two kinds of information represent entities from specific and diverse views and can enhance each other, respectively. Besides, if we look into each view closely, it can be separated into sub-views that are more meaningful. For this reason, this article proposes a multi-view–based collective entity linking algorithm, which combines several views of entities into an objective function for entity linking. The importance of each view can be valued and the linking results can be obtained along with resolving this objective function. Experimental results demonstrate that our linking algorithm can acquire higher accuracy than many state-of-the-art entity linking methods. Besides, since we simplify the entity's structure and change the entity linking to a sub-matrix searching problem, our algorithm also obtains high efficiency.