基于知识库的开放领域问答系统

问答系统能够理解用户问题，并直接返回答案。现有问答系统大多是面向领域的，仅能回答特定领域的问题。文中提出了基于大规模知识库的开放领域问答系统实现方法。该系统首先采用自定义词典分词和CRF模型相结合的方法识别问句中的主体；其次，采用模糊匹配方法将问句中的主体与知识库中实体建立链接；然后，通过相似度计算以及规则匹配等多种方法识别问句中的谓词并与知识库实体的属性建立关联；最后，进行实体消歧和答案获取。该系统平均F-Measure值为0.695 6，表明所提方法在基于知识库的开放领域问答上具有可行性。

Open-domain question-answering system based on large-scale knowledge base

Question-answering (QA) systems can understand user questions and return answers directly. Currently, most QA systems can only answer questions pertaining to specific domains. In this paper, we propose a method for constructing an open-domain QA system based on a large-scale knowledge base. First, we present an approach based on a visual dictionary and a conditional random field (CRF) model to identify the subject in question. Next, we use a fuzzy matching method to link the entity in question to that in the knowledge base, and apply similarity computation and rule matching methods to recognize the question predicates and link them to the attributes of the knowledge entity. Lastly, we implement entity disambiguation and answer retrieval. The mean F-measure value of the proposed system is 0.695 6, which indicates the feasibility of the proposed method for an open-domain QA system for a large-scale knowledge base.