Software engineering (SE) and requirements engineering (RE) face a significant increase in secondary studies, particularly literature reviews (LRs), due to the ever-growing number of scientific publications. Generative artificial intelligence (GenAI) exacerbates this trend by producing LRs rapidly but often at the expense of quality, rigor, and transparency. At the same time, secondary studies often fail to share underlying data and artifacts, limiting replication and reuse. This paper introduces EmpiRE-Compass, a neuro-symbolic dashboard designed to lower barriers for accessing, replicating, and reusing LR data. Its overarching goal is to demonstrate how LRs can become more sustainable by semantically structuring their underlying data in research knowledge graphs (RKGs) and by leveraging large language models (LLMs) for easy and dynamic access, replication, and reuse. Building on two RE use cases, we developed EmpiRE-Compass with a modular system design and workflows for curated and custom competency questions. The dashboard is freely available online, accompanied by a demonstration video. To manage operational costs, a limit of 25 requests per IP address per day applies to the default LLM (GPT-4o mini). All source code and documentation are released as an open-source project to foster reuse, adoption, and extension. EmpiRE-Compass provides three core capabilities: (1) Exploratory visual analytics for curated competency questions; (2) Neuro-symbolic synthesis for custom competency questions; and (3) Reusable knowledge with all queries, analyses, and results openly available. By unifying RKGs and LLMs in a neuro-symbolic dashboard, EmpiRE-Compass advances sustainable LRs in RE, SE, and beyond. It lowers technical barriers, fosters transparency and reproducibility, and enables collaborative, continuously updated, and reusable LRs

翻译：由于科学出版物数量的持续增长，软件工程（SE）和需求工程（RE）领域的二次研究，特别是文献综述（LRs）的数量显著增加。生成式人工智能（GenAI）加剧了这一趋势，它能够快速生成LRs，但往往以牺牲质量、严谨性和透明度为代价。与此同时，二次研究常常未能共享底层数据和制品，限制了其复现和重用。本文介绍了EmpiRE-Compass，一个旨在降低访问、复现和重用LR数据门槛的神经符号仪表板。其总体目标是展示如何通过将LR的底层数据在科研知识图谱（RKGs）中进行语义结构化，并利用大语言模型（LLMs）实现便捷、动态的访问、复现和重用，从而使LRs变得更加可持续。基于两个RE用例，我们开发了EmpiRE-Compass，它采用模块化系统设计，并支持针对策划和自定义能力问题的工作流。该仪表板可在线免费获取，并附有演示视频。为管理运营成本，默认LLM（GPT-4o mini）对每个IP地址每天限制25次请求。所有源代码和文档均作为开源项目发布，以促进重用、采纳和扩展。EmpiRE-Compass提供三项核心功能：（1）针对策划能力问题的探索性可视分析；（2）针对自定义能力问题的神经符号综合；（3）可重用的知识，所有查询、分析和结果均公开可用。通过将RKGs和LLMs统一在一个神经符号仪表板中，EmpiRE-Compass推动了RE、SE及其他领域可持续LRs的发展。它降低了技术门槛，促进了透明度和可复现性，并支持协作、持续更新和可重用的LRs。