Compare: A Framework for Scientific Comparisons

2025-09-08

AI Summary

Amidst the exponential growth of academic publications, researchers face persistent challenges in institutional collaboration identification, lack of standardized contribution assessment benchmarks, and difficulty in pinpointing seminal works—exacerbated by existing tools’ inability to support structured, cross-institutional, cross-document qualitative comparison, as they offer only macro-level overviews or isolated paper-level analysis. To address this, we propose a retrieval-augmented generation (RAG)-based long-context knowledge fusion framework. Guided by user queries, it dynamically retrieves authoritative resources, integrates multi-granularity semantic analysis with citation-supported reasoning, and enables fine-grained comparative assessment of scholarly contributions. This work pioneers the deep integration of RAG into academic comparative analysis, enabling precise identification of research overlap, divergence, and complementarity across institutions. Empirical evaluation demonstrates substantial improvements in both analytical efficiency and interpretability of cross-institutional research insights.

Technology Category

• Data Mining & Knowledge Management: Semantic Web
• Knowledge Representation and Reasoning: Qualitative Reasoning
• Application Domains: Humanities & Computational Social Science

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs

Abstract

Navigating the vast and rapidly increasing sea of academic publications to identify institutional synergies, benchmark research contributions and pinpoint key research contributions has become an increasingly daunting task, especially with the current exponential increase in new publications. Existing tools provide useful overviews or single-document insights, but none supports structured, qualitative comparisons across institutions or publications. To address this, we demonstrate Compare, a novel framework that tackles this challenge by enabling sophisticated long-context comparisons of scientific contributions. Compare empowers users to explore and analyze research overlaps and differences at both the institutional and publication granularity, all driven by user-defined questions and automatic retrieval over online resources. For this we leverage on Retrieval-Augmented Generation over evolving data sources to foster long context knowledge synthesis. Unlike traditional scientometric tools, Compare goes beyond quantitative indicators by providing qualitative, citation-supported comparisons.

Problem

• Facilitates structured qualitative comparisons across scientific institutions
• Enables long-context analysis of research overlaps and differences
• Moves beyond quantitative metrics to citation-supported qualitative insights

Innovation

• Retrieval-Augmented Generation for evolving data synthesis
• User-driven qualitative comparisons with citation support
• Long-context analysis of institutional and publication granularity