#5535. Simple Question Answering over Knowledge Graph Enhanced by Question Pattern Classification

August 2026publication date
Proposal available till 21-05-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Information Systems;
Human-Computer Interaction;
Hardware and Architecture;
Software;
Artificial Intelligence;
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More details about the manuscript: Science Citation Index Expanded or/and Social Sciences Citation Index
Abstract:
Question answering over knowledge graph (KGQA), which automatically answers natural language questions by querying the facts in knowledge graph (KG), has drawn significant attention in recent years. In this paper, we focus on single-relation questions, which can be answered through a single fact in KG. This task is a non-trivial problem since capturing the meaning of questions and selecting the golden fact from billions of facts in KG are both challengeable. We propose a pipeline framework for KGQA, which consists of three cascaded components: (1) an entity detection model, which can label the entity mention in the question; (2) a novel entity linking model, which considers the contextual information of candidate entities in KG and builds a question pattern classifier according to the correlations between question patterns and relation types to mitigate entity ambiguity problem; and (3) a simple yet effective relation detection model, which is used to match the semantic similarity between the question and relation candidates.
Keywords:
Entity detection; Entity linking; Knowledge graph; Question answering; Relation detection; Simple questions

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