Journal of Web Semantics (JWS) is an academic journal published by Elsevier. (ISSN 1570-8268, impact factor 3.1, CCF B).
The Journal of Web Semantics (JWS) is an interdisciplinary forum at the intersection of the Semantic Web, Knowledge Graphs (KGs), and Artificial Intelligence (AI), with a strong emphasis on both theoretical and applied research. Building on its foundation as a venue for exploring knowledge-intensive and intelligent Web technologies, JWS recognizes the pivotal role that KGs and Semantic Web (SW) technologies play in the evolving AI landscape, particularly amid recent breakthroughs in Generative AI, neuro-symbolic systems, and autonomous agents.
JWS seeks to capture the critical convergence between symbolic and statistical approaches to AI, focusing on the methods, architectures, and foundational theories that drive the integration of Semantic Web and KG technologies with machine learning, deep learning, Large Language Models (LLMs), and other AI techniques. The journal encourages contributions that not only demonstrate impactful applications but also advance the theoretical understanding of how structured, semantic knowledge can enhance intelligent systems.
We welcome high-quality submissions that include, but are not limited to, the following areas:
Theoretical Foundations and Methodological Advances
Formal Models and Representations: New theoretical frameworks and formalisms for KGs, ontologies, reasoning, and semantic data management, including studies on expressivity, consistency, change management, and evolution in complex or dynamic systems.
Hybrid and Neuro-Symbolic Architectures: Methodological insights into combining symbolic knowledge representation with sub-symbolic learning, including formal characterizations of neuro-symbolic systems and architectures.
KG-AI Integration Methods: Novel algorithms and frameworks that tightly couple KGs with AI methods in ways that yield results unattainable by either approach alone, including Logic Augmented Generation and reasoning-enhanced learning.
Evaluation and Benchmarking: Research on robust evaluation methodologies for KG-AI systems, with attention to correctness, scalability, data quality, reliability, interpretability, and accountability.
Applied and Interdisciplinary Research
Cross-Disciplinary Studies: Integrative work drawing from ontology engineering, databases, NLP, machine learning, human-computer interaction, and cognitive science, among others, with clear theoretical or methodological contributions.
Domain Applications: Real-world use cases showing how KGs and SW technologies enable or enhance AI in specific domains:
Healthcare and Life Sciences, Education, Legal Tech, Scientific Discovery, Smart Cities, Industry, Finance, Cultural Heritage, Art and Creativity, etc.
Engineering, Resources, and System Integration
KG Engineering Automation: AI-driven approaches to the (semi-)automatic creation, population, alignment, and refinement of KGs and ontologies, especially using LLMs and foundation models.
System Descriptions and Architectures: Descriptions of integrated KG-AI systems, with technical insights into issues such as hallucination mitigation, knowledge retrieval, cross-modal integration, and interaction design.
Auditing, Explanation, and Governance: Research on how KGs contribute to transparency, robustness, and auditability of AI systems, including formal representation of workflows, provenance, and ethical constraints.
Data and Knowledge Resources: Descriptions of high-impact ontologies, datasets, benchmarks, and tools that enable research or deployment in SW/AI integration.
JWS is especially interested in papers that address current and future challenges in the field, including:
Modelling expressivity for complex systems
Knowledge engineering automation
Integration of heterogeneous data and knowledge sources
Scalable, efficient reasoning with large-scale KGs
Accessibility and usability of semantic systems
Provenance, privacy, and interoperability in AI-KG ecosystems
Societal impacts, costs, risks, and sustainability of KG-based AI
Evaluation of semantic methods and systems
Finally, we value contributions that demonstrate real-world impact and uptake, including usability studies, deployment evaluations, and comparative analyses with alternative technologies.
By promoting both foundational insights and practical innovations, JWS aims to remain a leading venue for advancing the role of Semantic Web and Knowledge Graph technologies in shaping the future of Artificial Intelligence.