A Graph Traversal Based Approach to Answer Non-Aggregation Questions Over DBpedia

TL;DR

This paper introduces a graph traversal approach for answering non-aggregation questions over DBpedia, improving accuracy by joint entity disambiguation and answer path ranking.

Contribution

It presents a novel method that simplifies query understanding and enhances answer ranking for non-aggregation questions in knowledge base question answering.

Findings

Achieves state-of-the-art performance on non-aggregation question datasets.

Effectively handles semantic item mapping and disambiguation.

Improves answer accuracy through focus on answer path ranking.

Abstract

We present a question answering system over DBpedia, filling the gap between user information needs expressed in natural language and a structured query interface expressed in SPARQL over the underlying knowledge base (KB). Given the KB, our goal is to comprehend a natural language query and provide corresponding accurate answers. Focusing on solving the non-aggregation questions, in this paper, we construct a subgraph of the knowledge base from the detected entities and propose a graph traversal method to solve both the semantic item mapping problem and the disambiguation problem in a joint way. Compared with existing work, we simplify the process of query intention understanding and pay more attention to the answer path ranking. We evaluate our method on a non-aggregation question dataset and further on a complete dataset. Experimental results show that our method achieves best performance compared with several state-of-the-art systems.