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.

翻译：学术出版物数量呈指数级增长，亟需开发高效的文章检索工具，尤其在跨学科领域中，不同术语常被用于描述相似研究。传统基于关键词的搜索引擎在协助不熟悉特定术语的用户时往往效果有限。为解决该问题，我们提出了一种基于知识图谱的生物医学论文搜索引擎，旨在提升用户发现相关查询与文章的体验。该系统命名为DiscoverPath，采用命名实体识别（NER）与词性标注（POS）技术，从文章摘要中提取术语及关系以构建知识图谱。为减少信息过载，DiscoverPath向用户展示包含查询实体及其相邻节点的聚焦子图，并集成查询推荐系统，使用户能够迭代优化查询。系统配备易于使用的图形用户界面，直观呈现知识图谱可视化、查询推荐及详细文章信息，从而提升文章检索效率，促进跨学科知识探索。DiscoverPath已开源，代码地址：https://github.com/ynchuang/DiscoverPath。