EmpiRE-Compass: A Neuro-Symbolic Dashboard for Sustainable and Dynamic Knowledge Exploration, Synthesis, and Reuse

2026-02-25

AI Summary

This work addresses the challenges posed by the rapid growth of literature reviews—particularly in software and requirements engineering—where limited data sharing hinders reproducibility, transparency, and reuse. To overcome these limitations, we propose the first neuro-symbolic interactive dashboard that integrates Research Knowledge Graphs (RKGs) with large language models (e.g., GPT-4o mini). The system features a modular architecture enabling dynamic querying through both predefined and user-defined questions, interactive visual exploration, and neuro-symbolic reasoning. All queries and results are fully open for reuse, fostering a collaborative and transparent approach to literature synthesis. The project is open-sourced and publicly deployed, establishing a sustainable, transparent, and collaborative paradigm for next-generation literature reviews.

Technology Category

• Machine Learning: Neuro-Symbolic Learning
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Knowledge Representation and Reasoning: Applications

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Large language models for search

Abstract

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

Problem

• literature reviews
• knowledge reuse
• reproducibility
• research knowledge graphs
• generative AI

Innovation

• neuro-symbolic
• research knowledge graphs
• large language models
• sustainable literature reviews
• competency questions