DiscoverPath: A Knowledge Refinement and Retrieval System for Interdisciplinarity on Biomedical Research
This addresses the challenge for biomedical researchers who struggle with diverse terminologies in interdisciplinary searches, though it is incremental as it builds on existing knowledge graph and NLP methods.
The authors tackled the problem of inefficient article retrieval in interdisciplinary biomedical research by developing DiscoverPath, a knowledge graph-based search engine that uses NER and POS tagging to extract terminologies and relationships from abstracts, resulting in a system that provides focused subgraphs and query recommendations to enhance user experience.
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.