Question Answering over Linked Data Using First-order Logic

Abstract

Question Answering over Linked Data (QALD) aims to evaluate a question answering system over structured data, the key objective of which is to translate questions posed using natural language into structured queries. This technique can help common users to directly access open-structured knowledge on the Web and, accordingly, has attracted much attention. To this end, we propose a novel method using first-order logic. We formulate the knowledge for resolving the ambiguities in the main three steps of QALD (phrase detection, phrase-tosemantic-item mapping and semantic item grouping) as first-order logic clauses in a Markov Logic Network. All clauses can then produce interacted effects in a unified framework and can jointly resolve all ambiguities. Moreover, our method adopts a pattern-learning strategy for semantic item grouping. In this way, our method can cover more text expressions and answer more questions than previous methods using manually designed patterns. The experimental results using open benchmarks demonstrate the effectiveness of the proposed method.