DiscoverPath: A Knowledge Refinement and Retrieval System for Interdisciplinarity on Biomedical Research

TL;DR

DiscoverPath is a knowledge graph-based search system designed to improve interdisciplinary biomedical research article retrieval by extracting key terminologies and relationships, providing focused visualizations, and enabling iterative query refinement.

Contribution

It introduces a novel knowledge graph system utilizing NER and POS tagging to enhance biomedical article search and interdisciplinary knowledge discovery.

Findings

Effective visualization of related research entities

Improved article retrieval accuracy in interdisciplinary contexts

Open-source implementation available for community use

Abstract

The exponential growth in scholarly publications necessitates advanced tools for efficient article retrieval, especially in interdisciplinary fields where diverse terminologies are used to describe similar research. Traditional keyword-based search engines often fall short in assisting users who may not be familiar with specific terminologies. To address this, we present a knowledge graph-based paper search engine for biomedical research to enhance the user experience in discovering relevant queries and articles. The system, dubbed DiscoverPath, employs Named Entity Recognition (NER) and part-of-speech (POS) tagging to extract terminologies and relationships from article abstracts to create a KG. To reduce information overload, DiscoverPath presents users with a focused subgraph containing the queried entity and its neighboring nodes and incorporates a query recommendation system, enabling users to iteratively refine their queries. The system is equipped with an accessible Graphical User Interface that provides an intuitive visualization of the KG, query recommendations, and detailed article information, enabling efficient article retrieval, thus fostering interdisciplinary knowledge exploration. DiscoverPath is open-sourced at https://github.com/ynchuang/DiscoverPath.