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：学术出版物数量的指数级增长，迫切需要先进的工具来实现高效的文章检索，尤其是在使用不同术语描述相似研究的跨学科领域。传统的基于关键词的搜索引擎往往难以帮助那些不熟悉特定术语的用户。为解决这一问题，我们提出了一个面向生物医学研究的知识图谱论文搜索引擎，以提升用户在发现相关查询和文章方面的体验。该系统名为DiscoverPath，采用命名实体识别（NER）和词性标注（POS）技术，从文章摘要中提取术语及其关系，从而构建知识图谱（KG）。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，使用户能够迭代地优化其查询。该系统配备了一个易用的图形用户界面，可直观展示知识图谱、提供查询推荐及详细的文章信息，从而实现高效的文章检索，进而促进跨学科知识探索。DiscoverPath已在https://github.com/ynchuang/DiscoverPath 开源。