Paper Info
Title
An empirical study of representing adjectives over knowledge bases: Approach, lexicon and application
Abstract
Adjectives are common in natural language, and their usage and semantics have been studied broadly. In recent years, with the rapid growth of knowledge bases (KBs), many knowledge-based question answering (KBQA) systems are developed to answer users’ natural language questions over KBs. A fundamental task of such systems is to transform natural language questions into structural queries, e.g., SPARQL queries. Thus, such systems require knowledge about how natural language expressions are represented in KBs, including adjectives. In this paper, we specifically address the problem of representing adjectives over KBs. We propose a novel approach, called Adj2SP, to represent adjectives as SPARQL query patterns. Adj2SP contains a statistic-based approach and a neural network-based approach, both of them can effectively reduce the search space for adjective representations and overcome the lexical gap between input adjectives and their target representations. Two adjective representation datasets are built for evaluation, with adjectives used in QALD and Yahoo! Answers, as well as their representations over DBpedia. Experimental results show that Adj2SP can generate representations of high quality and significantly outperform several alternative approaches in F1-score. Furthermore, we publish Lark, a lexicon for adjective representations over KBs. Current KBQA systems show an improvement of over 24% in F1-score by integrating Adj2SP.
Year
DOI
Venue
2022
10.1016/j.websem.2021.100681
Journal of Web Semantics
Keywords
DocType
Volume
Adjective representation,Question answering,Knowledge base,SPARQL
Journal
72
ISSN
Citations 
PageRank 
1570-8268
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Jiwei Ding102.03
Yuzhong Qu272662.49
Yuzhong Qu372662.49
Xin Yu400.34