Question Answering over Linked Data Using First-order Logic
Shizhu He, Kang Liu, Yuanzhe Zhang, Liheng Xu, Jun Zhao · 2014
Question Answering over Linked Data (QALD) aims to evaluate a question an-swering 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 ac-cess 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-to-semantic-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 am-biguities. 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 us-ing manually designed patterns. The ex-perimental results using open benchmarks demonstrate the effectiveness of the pro-posed method. 1