Query-Driven Knowledge Base Completion using Multimodal Path Fusion over Multimodal Knowledge Graph
Yang Peng, Daisy Zhe Wang

TL;DR
This paper presents a query-driven system that fuses unstructured web data and structured knowledge base information through multimodal path fusion, significantly improving knowledge base completion accuracy and response speed.
Contribution
It introduces a novel multimodal path fusion algorithm and query-driven techniques for efficient knowledge base completion using multimodal knowledge graphs.
Findings
Outperforms baseline fusion algorithms in accuracy
Achieves faster response times for user queries
Demonstrates effectiveness through extensive experiments
Abstract
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…
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Taxonomy
TopicsSemantic Web and Ontologies · Advanced Graph Neural Networks · Topic Modeling
MethodsBalanced Selection
