Knowledge Base Completion using Web-Based Question Answering and Multimodal Fusion

TL;DR

This paper presents a web-based question answering system that uses multimodal fusion of unstructured web data and structured knowledge base information to effectively complete and expand large, incomplete knowledge bases.

Contribution

It introduces a novel multimodal fusion approach combining unstructured web data and structured knowledge for knowledge base completion.

Findings

Achieves effective knowledge base completion with minimal questions.

Utilizes structured information to improve extraction accuracy.

Demonstrates the effectiveness of multimodal fusion in KB completion.

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. 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.