Faculty, Staff and Student Publications
Publication Date
10-1-2023
Journal
Proceedings of the 33rd ACM International Conference on Information and Knowledge Management
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 with a demo video at Youtube.
Keywords
Healthcare, Biomedical, Knowledge Graph, Recommender System, Information Retrieval System
DOI
10.1145/3583780.3614739
PMID
38832084
PMCID
PMC11145658
PubMedCentral® Posted Date
6-3-2024
PubMedCentral® Full Text Version
Author MSS
Published Open-Access
yes
Included in
Bioinformatics Commons, Biomedical Informatics Commons, Data Science Commons, Health Services Research Commons
