  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.
