Scientific literature searches are often exploratory, whereby users are not yet familiar with a particular field or concept but are interested in learning more about it. However, existing systems for scientific literature search are typically tailored to keyword-based lookup searches, limiting the possibilities for exploration. We propose NLP-KG, a feature-rich system designed to support the exploration of research literature in unfamiliar natural language processing (NLP) fields. In addition to a semantic search, NLP-KG allows users to easily find survey papers that provide a quick introduction to a field of interest. Further, a Fields of Study hierarchy graph enables users to familiarize themselves with a field and its related areas. Finally, a chat interface allows users to ask questions about unfamiliar concepts or specific articles in NLP and obtain answers grounded in knowledge retrieved from scientific publications. Our system provides users with comprehensive exploration possibilities, supporting them in investigating the relationships between different fields, understanding unfamiliar concepts in NLP, and finding relevant research literature. Demo, video, and code are available at: https://github.com/NLP-Knowledge-Graph/NLP-KG-WebApp.

翻译：科学文献检索通常是探索性的，即用户尚未熟悉特定领域或概念，但希望进一步了解相关内容。然而，现有的科学文献检索系统通常针对基于关键词的查找式搜索而设计，限制了探索的可能性。我们提出了NLP-KG，这是一个功能丰富的系统，旨在支持对不熟悉的自然语言处理（NLP）领域研究文献的探索。除了语义搜索功能外，NLP-KG还允许用户轻松找到可快速介绍感兴趣领域的综述论文。此外，通过“研究领域”层次结构图，用户可以熟悉某个领域及其相关领域。最后，聊天界面允许用户就NLP中不熟悉的概念或特定文章提出问题，并获得基于科学出版物知识检索的答案。我们的系统为用户提供了全面的探索可能性，支持他们研究不同领域之间的关系、理解NLP中的陌生概念以及查找相关研究文献。演示、视频和代码可在以下网址获取：https://github.com/NLP-Knowledge-Graph/NLP-KG-WebApp。