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

TL;DR

This paper introduces NLI-GIBIR, a novel natural language interface for graph-based bibliographic retrieval systems, using advanced NLP techniques to interpret complex queries and demonstrate high accuracy in real-world scenarios.

Contribution

It is the first to propose a natural language interface for graph-based bibliographic retrieval, with a new NLP framework and heuristics for query interpretation.

Findings

Correctly answered 39 out of 40 queries

Effective handling of complex bibliographic queries

Practical solution for real-world applications

Abstract

With the ever-increasing scientific literature, there is a need on a natural language interface to bibliographic information retrieval systems to retrieve related information effectively. In this paper, we propose a natural language interface, NLI-GIBIR, to a graph-based bibliographic information retrieval system. In designing NLI-GIBIR, we developed a novel framework that can be applicable to graph-based bibliographic information retrieval systems. Our framework integrates algorithms/heuristics for interpreting and analyzing natural language bibliographic queries. NLI-GIBIR allows users to search for a variety of bibliographic data through natural language. A series of text- and linguistic-based techniques are used to analyze and answer natural language queries, including tokenization, named entity recognition, and syntactic analysis. We find that our framework can effectively represents and addresses 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 to 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 natural language bibliographic queries. Third, we show that the proposed framework and natural language interface provide a practical solution in building real-world natural language interface-based 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.