Over the past few years, large knowledge bases have been constructed to store massive amounts of knowledge. However, these knowledge bases are highly incomplete, for example, over 70% of people in Freebase have no known place of birth. To solve this problem, we propose a query-driven knowledge base completion system with multimodal fusion of unstructured and structured information. To effectively fuse unstructured information from the Web and structured information in knowledge bases to achieve good performance, our system builds multimodal knowledge graphs based on question answering and rule inference. We propose a multimodal path fusion algorithm to rank candidate answers based on different paths in the multimodal knowledge graphs, achieving much better performance than question answering, rule inference and a baseline fusion algorithm. To improve system efficiency, query-driven techniques are utilized to reduce the runtime of our system, providing fast responses to user queries. Extensive experiments have been conducted to demonstrate the effectiveness and efficiency of our system.

翻译：近年来，大规模知识库被构建用于存储海量知识。然而，这些知识库存在高度不完整性，例如，Freebase中超过70%的人物缺乏已知出生地信息。为解决这一问题，我们提出了一种融合非结构化与结构化信息的查询驱动式知识库补全系统。为有效融合来自互联网的非结构化信息与知识库中的结构化信息以取得良好性能，本系统基于问答与规则推理构建了多模态知识图谱。我们提出了一种多模态路径融合算法，通过排序多模态知识图谱中不同路径的候选答案，其性能显著优于问答系统、规则推理以及基线融合算法。为提升系统效率，我们采用查询驱动技术来缩短系统运行时间，从而为用户查询提供快速响应。大量实验验证了本系统的有效性与高效性。