Query-Driven Knowledge Base Completion using Multimodal Path Fusion over Multimodal Knowledge Graph
This work addresses knowledge base completion for applications like question answering, but it appears incremental as it builds on existing multimodal fusion techniques.
The paper tackles the problem of incomplete knowledge bases, such as Freebase where over 70% of people lack known birthplaces, by proposing a query-driven system that fuses unstructured and structured information using multimodal knowledge graphs and a path fusion algorithm, achieving much better performance than baseline methods.
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.