Entity Linking (EL) consists of determinating the entities that best represent the mentions in a document. Mentions can be very ambiguous and can refer to different entities in different contexts. In this paper, we present ABACO, a semantic annotation system for Entity Linking (EL) which addresses name ambiguity assuming that the entity that annotates a mention should be coherent with the main topics of the document. ABACO extracts a sub-graph from a knowledge base which interconnects all the candidate entities to annotate each mention in the document. Candidate entities are scored according to their degree of centrality in the knowledge graph and their textual similarity with the topics of the document, and worst candidates are pruned from the sub-graph. The approach has been validated with 13 datasets and compared with other 11 annotation systems using the GERBIL platform. Results show that ABACO outperforms the other systems for medium/large documents.

实体链接（EL）包括确定最能代表文档中提及内容的实体。提及可能非常模棱两可，并且可以在不同的上下文中引用不同的实体。在本文中，我们提出了ABACO，这是一种用于实体链接（EL）的语义注释系统，该系统解决了名称歧义的问题，假定注释注释的实体应与文档的主要主题保持一致。ABACO从知识库中提取一个子图，该子图将所有候选实体互连起来以注释文档中的每个提及。根据候选实体在知识图中的中心程度以及它们与文档主题的文本相似性来对其评分，并且从子图中删除最差的候选对象。该方法已通过13个数据集进行了验证，并与使用GERBIL平台的其他11个注释系统进行了比较。结果表明，ABACO在大中型文档方面优于其他系统。