Knowledge Base Completion using Web-Based Question Answering and Multimodal Fusion
This work addresses knowledge base completion for AI and data management applications, but appears incremental as it builds on existing multimodal and question answering techniques.
The paper tackles the problem of incomplete knowledge bases by proposing a web-based question answering system with multimodal fusion to extract missing facts, achieving good performance with very few questions.
Over the past few years, large knowledge bases have been constructed to store massive amounts of knowledge. However, these knowledge bases are highly incomplete. To solve this problem, we propose a web-based question answering system system with multimodal fusion of unstructured and structured information, to fill in missing information for knowledge bases. To utilize unstructured information from the Web for knowledge base completion, we design a web-based question answering system using multimodal features and question templates to extract missing facts, which can achieve good performance with very few questions. To help improve extraction quality, the question answering system employs structured information from knowledge bases, such as entity types and entity-to-entity relatedness.