Scholarly Knowledge Graphs for Open Communication

Scholarly Knowledge Graphs (SKGs) are structured, queryable data models that capture the entities of the scholarly ecosystem, publications, research data, software, authors, organizations, funding, and more, alongside the relationships between them, such as citations and affiliations. They are built by aggregating heterogeneous data sources through deduplication and enrichment via full-text mining and AI, producing representations of global science that power discovery, research impact, and assessment applications.Prominent examples include the OpenAIRE Graph, OpenAlex, Crossref, DataCite, and Wikidata, as well as thematic graphs such as the eBrain KG and Graphia-OPERAS KG. These align with open science demands as identified by initiatives including CoARA, FAIR data, and FDOs. Together, they are becoming foundational to an open data layer supporting research intelligence, AI agents, and the full lifecycle of science. Amid surging digital scholarship, SKGs facilitate comparative studies, policy insights, and knowledge creation behaviors, advancing SDG 4 (Quality Education), SDG 9 (Industry, Innovation, and Infrastructure), and SDG 17 (Partnerships).This Research Topic examines scholarly knowledge graphs as infrastructure for enhanced communication, and challenges in their construction, from ecosystem issues, heterogeneity, disciplinary idiosyncrasies, and technological and behavioral solutions. It will also examine SKGs as tools to connect stakeholders through semantic technologies, standards metadata and protocols (e.g., SKG Interoperability Framework), data models that overcome fragmentation in data, metadata, and workflows, and support (AI powered) applications such as discovery, research impact, and open science monitoring. We therefore invite studies on KG construction (schema design, population pipelines), querying/enrichment (SPARQL, embeddings), applications (reuse analytics, provenance tracking), and challenges (scalability, ethics), with evaluations on real-world graphs like the OpenAIRE Graph, OpenAlex, Crossref, DataCite, etc. Outcomes will guide developers, funders, and publishers toward interoperable, AI-ready systems for sustainable knowledge sharing.This Research Topic explores scholarly knowledge graphs advancing traditional/emerging communication approaches, technologies, and networks for research creation/dissemination across domains. It connects diverse elements like metadata, collaboration, and open products, promoting SDG 4, 9, and 17. Suggested themes which may be explored in the Research Topic, include, but are not limited to:• KG construction from repositories, citations, and heterogeneous scholarly sources. • Semantic interoperability via RDF, ontologies, and entity resolution pipelines. • Querying and analytics for discovery, impact, reuse, and collaboration insights. • AI/ML integration for KG enrichment, recommendation, and provenance tracking. • Ethical frameworks, policies, and funding models for open KG ecosystems. • Comparative evaluations of national/global KGs and digital scholarship applications. • Researcher behaviors and adoption in KG-enabled workflows and social channels. We welcome Original Research (empirical KG studies), Methods (technical frameworks), Policy and Practice Reviews (deployment analyses), and Perspectives (future visions). Submissions must provide in-depth insights into processes/technologies/stakeholders with clear scholarly communication focus. Topic editors: Paolo Manghi Source: https://www.frontiersin.org/research-topics/79637/scholarly-knowledge-graphs-for-open-communication