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

Yongjun Zhu, Erjia Yan, Il Yeol Song

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)73-89
Number of pages17
JournalData and Knowledge Engineering
Publication statusPublished - 2017 Sep

Bibliographical note

Funding Information:
This project was made possible in part by the Institute of Museum and Library Services (Grant Award Number: RE-07–15-0060-15), for the project titled “Building an entity-based research framework to enhance digital services on knowledge discovery and delivery”.

Publisher Copyright:
© 2017 Elsevier B.V.

All Science Journal Classification (ASJC) codes

  • Information Systems and Management


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