论文标题
通过多模式融合在多模式知识图上使用多模式路径融合的查询驱动的知识库完成
Query-Driven Knowledge Base Completion using Multimodal Path Fusion over Multimodal Knowledge Graph
论文作者
论文摘要
在过去的几年中，已经建立了大量知识库来存储大量知识。但是，这些知识库是高度不完整的，例如，自由基础的70％以上的人没有已知的出生地。为了解决这个问题，我们提出了一个通过非结构化和结构化信息的多模式融合的查询驱动的知识基础完成系统。为了有效地从Web和知识库中的结构化信息融合，以实现良好的性能，我们的系统根据问题答案和规则推理构建了多模式知识图。我们提出了一种多模式路径融合算法，以根据多模式知识图中的不同路径对候选答案进行排名，这比问答答案，规则推理和基线融合算法要比质量回答要好得多。为了提高系统效率，使用查询驱动的技术来减少系统的运行时，从而快速响应用户查询。已经进行了广泛的实验，以证明我们系统的有效性和效率。
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.
