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A natural language interface to a graph-based bibliographic information retrieval system
Journal article   Open access   Peer reviewed

A natural language interface to a graph-based bibliographic information retrieval system

Yongjun Zhu, Erjia Yan and Il-Yeol Song
Data & knowledge engineering, v 111, pp 73-89
Sep 2017
DOI: https://doi.org/10.1016/j.datak.2017.06.006
url
http://arxiv.org/abs/1612.03231View
SubmittedOpen Access (License Unspecified),  Open

Abstract

Natural language interface Data and knowledge visualization Information retrieval Graph database Digital libraries
With the ever-increasing volume of scientific literature, there is a need for a natural language interface to bibliographic information retrieval systems to retrieve relevant information effectively. In this paper, we propose one such interface, NLI-GIBIR, which allows users to search for a variety of bibliographic data through natural language. NLI-GIBIR makes use of a novel framework applicable to graph-based bibliographic information retrieval systems in general. This framework incorporates algorithms/heuristics for interpreting and analyzing natural language bibliographic queries via a series of text- and linguistic-based techniques, including tokenization, named entity recognition, and syntactic analysis. We find that our framework, as implemented in NLI-GIBIR, can effectively represent and address complex bibliographic information needs. Thus, the contributions of this paper are as follows: First, to our knowledge, it is the first attempt to propose a natural language interface for graph-based bibliographic information retrieval. Second, we propose a novel customized natural language processing framework that integrates a few original algorithms/heuristics for interpreting and analyzing bibliographic queries. Third, we show that the proposed framework and natural language interface provide a practical solution for building real-world bibliographic information retrieval systems. Our experimental results show that the presented system can correctly answer 39 out of 40 example natural language queries with varying lengths and complexities.

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10 citations in Web of Science
17 citations in Scopus

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Web of Science research areas
Computer Science, Artificial Intelligence
Computer Science, Information Systems
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