EmpiRE-Compass：一个用于可持续和动态知识探索、综合与重用的神经符号仪表盘
EmpiRE-Compass: A Neuro-Symbolic Dashboard for Sustainable and Dynamic Knowledge Exploration, Synthesis, and Reuse
AI总结：
EmpiRE-Compass通过神经符号仪表盘实现可持续的文献综述，提供可视化分析、神经符号综合和可重用知识，促进透明和可重复性。
摘要：
软件工程（SE）和需求工程（RE）面临二次研究显著增加的问题，特别是文献综述（LRs），这主要是由于科学出版物数量持续增长所致。生成式人工智能（GenAI）加剧了这一趋势，因为它能快速生成LRs，但往往以牺牲质量和严谨性为代价。同时，二次研究往往无法分享底层数据和 artifacts，限制了复制和重用。本文介绍了EmpiRE-Compass，这是一个神经符号仪表盘，旨在降低访问、复制和重用LR数据的门槛。其总体目标是展示如何通过在研究知识图（RKGs）中语义结构化其底层数据，以及利用大语言模型（LLMs）实现轻松和动态的访问、复制和重用，使LRs更加可持续。基于两个RE使用案例，我们开发了EmpiRE-Compass，采用模块化系统设计和工作流，用于定制和自定义能力问题。该仪表盘已在线上免费提供，配有演示视频。为了管理运营成本，默认LLM（GPT-4o mini）每天每个IP地址最多25次请求限制。所有源代码和文档均已发布为开源项目，以促进重用、采用和扩展。EmpiRE-Compass提供了三个核心功能：（1）针对定制能力问题的探索性可视化分析；（2）针对定制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。通过在神经符号仪表盘中统一RKGs和LLMs，EmpiRE-Compass推动了RE、SE及其他领域的可持续LRs发展。它降低了技术门槛，促进了透明性和可重复性，并使协作、持续更新和可重用的LRs成为可能。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
