Research Knowledge Graphs: The Shifting Paradigm of Scholarly Information Representation

2025-06-08

Extended Semantic Web Conference

AI Summary

Reproducibility, reuse of research data and methods, and discovery of heterogeneous scholarly resources remain severely hindered by high heterogeneity in both resources and metadata, compounded by the prevalence of unstructured, literature-based information. Method: This paper introduces the “Research Knowledge Graph” (RKG) paradigm—the first systematic formalization of its kind—comprising a conceptual framework, taxonomy, and core architectural modules. It integrates persistent identifier (PID) management, RDF/OWL-based semantic modeling, cross-source vocabulary alignment, and trustworthy data integration to enable structured representation and interoperability of multi-source research assets. Contribution/Results: Through a comprehensive survey spanning scale, modeling paradigms, ontologies, data sources, and trustworthiness criteria, we characterize empirical RKG implementations, map their methodological landscape, identify application pathways, and pinpoint critical technical and organizational challenges—thereby providing foundational theoretical insights and actionable engineering guidance for RKG development and deployment.

Technology Category

• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Knowledge Representation and Reasoning: Ontologies
• Reasoning under Uncertainty: Graphical Models

Application Category

• 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: Graph embeddings and representation learning for Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Sharing and reusing research artifacts, such as datasets, publications, or methods is a fundamental part of scientific activity, where heterogeneity of resources and metadata and the common practice of capturing information in unstructured publications pose crucial challenges. Reproducibility of research and finding state-of-the-art methods or data have become increasingly challenging. In this context, the concept of Research Knowledge Graphs (RKGs) has emerged, aiming at providing an easy to use and machine-actionable representation of research artifacts and their relations. That is facilitated through the use of established principles for data representation, the consistent adoption of globally unique persistent identifiers and the reuse and linking of vocabularies and data. This paper provides the first conceptualisation of the RKG vision, a categorisation of in-use RKGs together with a description of RKG building blocks and principles. We also survey real-world RKG implementations differing with respect to scale, schema, data, used vocabulary, and reliability of the contained data. We also characterise different RKG construction methodologies and provide a forward-looking perspective on the diverse applications, opportunities, and challenges associated with the RKG vision.

Problem

• Addressing challenges in sharing and reusing heterogeneous research artifacts
• Improving reproducibility and discovery of state-of-the-art research methods
• Establishing machine-actionable knowledge graphs for structured scholarly information

Innovation

• Research Knowledge Graphs for scholarly representation
• Machine-actionable research artifacts with unique identifiers
• Linking vocabularies and data for reproducibility