Targeted Extraction of Temporal Facts from Textual Resources for Improved Temporal Question Answering over Knowledge Bases

TL;DR

This paper introduces a targeted method for extracting temporal facts from text to enhance the accuracy of temporal question answering systems that rely on knowledge bases, addressing retrieval gaps.

Contribution

It proposes a novel approach using logical representations and textual queries to retrieve missing temporal facts, improving KBQA performance on temporal questions.

Findings

Achieved approximately 30% relative improvement in answer accuracy.

Effectively retrieves temporal facts missed by KBs using textual resource querying.

Demonstrated effectiveness on Wikidata and Wikipedia datasets.

Abstract

Knowledge Base Question Answering (KBQA) systems have the goal of answering complex natural language questions by reasoning over relevant facts retrieved from Knowledge Bases (KB). One of the major challenges faced by these systems is their inability to retrieve all relevant facts due to factors such as incomplete KB and entity/relation linking errors. In this paper, we address this particular challenge for systems handling a specific category of questions called temporal questions, where answer derivation involve reasoning over facts asserting point/intervals of time for various events. We propose a novel approach where a targeted temporal fact extraction technique is used to assist KBQA whenever it fails to retrieve temporal facts from the KB. We use $\lambda$-expressions of the questions to logically represent the component facts and the reasoning steps needed to derive the answer. This allows us to spot those facts that failed to get retrieved from the KB and generate textual queries to extract them from the textual resources in an open-domain question answering fashion. We evaluated our approach on a benchmark temporal question answering dataset considering Wikidata and Wikipedia respectively as the KB and textual resource. Experimental results show a significant $\sim$30\% relative improvement in answer accuracy, demonstrating the effectiveness of our approach.