  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.
