知识图谱是实现开放领域问答的关键技术之一，开放领域问答任务往往需要足够多的知识信息，而知识图谱的不完备性成为制约问答系统性能的一个重要因素。已有相关工作利用外部非结构化的文本与基于知识图谱的结构化知识相结合来填补缺失的信息。此时，检索外部文本的准确性和效率尤为关键，选取与问题相关度较高的文本可提升系统性能。相反，选取与问题相关性较弱的文本将会引入知识噪声，从而降低问答任务的准确性。因此，本文设计了一种融合文本与知识图谱的问答系统模型，其中的文本检索器可充分挖掘问题和文本的语义信息，以提高检索的质量和查询子图的准确性；其中的知识融合器将文本和知识库中的知识相结合以构建知识的融合表征。实验结果表明，对比基线方法，该模型在性能上存在一定的优势。

Knowledge graph is one of the key technologies to realize question answering in open domain. Open domain question answering tasks often require enough knowledge information, and the incompleteness of knowledge graph becomes an important factor restricting the performance of question answering system. Existing related work utilizes external unstructured text combined with knowledge graph-based structured knowledge to fill in the missing information. At this time, the accuracy and efficiency of retrieving external texts are particularly critical, and selecting texts that are highly relevant to the problem can improve system performance. Conversely, selecting texts that are less relevant to the question will introduce knowledge noise, thereby reducing the accuracy of question answering tasks. Therefore, this paper designs a question answering system model that integrates text and knowledge graphs, in which the text retriever can fully mine the semantic information of questions and texts to improve the quality of retrieval and the accuracy of query subgraphs; Combine knowledge from text and knowledge bases to build fusion representations of knowledge. The experimental results show that the model has certain advantages in performance compared with the baseline method.

TP183

科技创新2030“新一代人工智能”重大项目(2020AAA0109300)；国家自然科学基金青年基金(61802251).