Applying Semantic Parsing to Question Answering Over Linked Data: Addressing the Lexical Gap

Question answering over linked data has emerged in the past years as an important topic of research in order to provide natural language access to a growing body of linked open data on the Web. In this paper we focus on analyzing the lexical gap that arises as a challenge for any such question answering system. The lexical gap refers to the mismatch between the vocabulary used in a user question and the vocabulary used in the relevant dataset. We implement a semantic parsing approach and evaluate it on the QALD-4 benchmark, showing that the performance of such an approach su↵ers from training data sparseness. Its performance can, however, be substantially improved if the right lexical knowledge is available. To show this, we model a set of lexical entries by hand to quantify the number of entries that would be needed. Further, we analyze if a state-of-the-art tool for inducing ontology lexica from corpora can derive these lexical entries automatically. We conclude that further resear...

• Type: Journal
• Year: 2015
• Where: NLDB
• Authors: Sherzod Hakimov, Christina Unger, Sebastian Walter, Philipp Cimiano
• Keywords: Natural Language Processing, NLDB 2015