Mapping Factoid Adjective Constraints to Existential Restrictions over Knowledge Bases

Abstract

The rapid progress of question answering (QA) systems over knowledge bases (KBs) enables end users to acquire knowledge with natural language questions. While mapping proper nouns and relational phrases to semantic constructs in KBs has been extensively studied, little attention has been devoted to adjectives, most of which play the role of factoid constraints on the modified nouns. In this paper, we study the problem of finding appropriate representations for adjectives over KBs. We propose a novel approach, called Adj2ER, to automatically map an adjective to several existential restrictions or their negation forms. Specifically, we leverage statistic measures for generating candidate existential restrictions and supervised learning for filtering the candidates, which largely reduce the search space and overcome the lexical gap. We create two question sets with adjectives from QALD and Yahoo! Answers, and conduct experiments over DBpedia. Our experimental results show that Adj2ER can generate high-quality mappings for most adjectives and significantly outperform several alternative approaches. Furthermore, current QA systems can gain a promising improvement when integrating our adjective mapping approach.