基于多维信息融合的知识库问答实体链接

知识库问答实体链接任务需要将问句内容精准链接到知识库中实体.当前方法大多难以兼顾链接实体的召回率和精确率,并且仅能根据文本信息对实体进行区分筛选.因此,文中在合并子步骤的基础上,提出融合多维度特征的知识库问答实体链接模型(MDIIEL).通过表示学习方法,将文本符号、实体和问句类型、实体在知识库中语义结构表达等信息整合并引至实体链接任务中,加强对相似实体的区分,在提高准确率的同时降低候选集的大小.实验表明,MDIIEL模型在实体链接任务性能上具有整体性提升,在大部分指标上取得较优的链接结果.

Multi-dimensional Information Integration Based Entity Linking for Knowledge Base Question Answering

The entity linking task of knowledge base question answering(KBQA) is to accurately link the content of questions to the entities in the knowledge base. Recall rate and accuracy of linked entities cannot be balanced by most of the current methods, and only the text information is applied to distinguish and filter the entities. Therefore, a multi-dimensional information integration based entity linking for KBQA(MDIIEL) combining multi-dimensional features based on merging substeps is proposed in this paper. By representing learning methods, information such as text symbols, entities and question types, and semantic structure expressions of entities in the knowledge base are integrated and introduced into the entity linking task. The differentiation of similar entities is strengthened, and candidate sets are reduced while the accuracy is improved. The experiment proves that the MDIIEL model makes a holistic improvement on the entity linking task compared with the current methods, and it achieves the best current linking results on most indicators.