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.

Special Issue on AI and Multimodal Knowledge Graphs
提出日: 2026-05-15

Recent advances in generative machine learning have transformed computationally creative systems, enabling high-quality text-to-image generators, video diffusion models, and music generation tools trained on large multimodal datasets. Foundational models now address diverse tasks like video scene detection, image segmentation, and chord recognition, with mainstream platforms hosting systems like MusicGen and Stable Diffusion. The Semantic Web community increasingly explores representing multimodality in knowledge graphs, which serve as both input constraints and output generators for creative AI models. Major conferences and funded research projects, including DOREMUS and Polifonia, focus on integrating knowledge graphs, music, and multimodal creativity.

This special issue builds on top of the outcomes of recent work and events on AI and multimodality with Knowledge Graphs. The scope of the present special issue is to provide an opportunity to publish novel work in the areas of AI, multimedia, multimodality and knowledge graphs.

Guest editors:

Albert Meroño-Peñuela, King’s College London, London, UK
Christophe Guillotel-Nothmann, CNRS, Paris, France
Andrea Poltronieri, Universitat Pompeu Fabra, Barcelona, Spain

Special issue information:

We encourage the submission of novel, previously unpublished research related, but not limited, to one or more of the following themes and topics:

● Use of AI models and multimodal generation for knowledge graphs
● Use of knowledge graphs for computational creativity and generative AI
● AI and knowledge graphs for multimodal feature representation (melody, harmony, rhythm, structure, timbre, genre, emotion, expression; image segmentation; video scene detection)
● Knowledge graphs as training data for models addressing multimedia information retrieval tasks
● Knowledge graphs and AI for MIR tasks
● Linking multimedia to their cultural context through knowledge graphs and AI
● Licensing issues and representation in generative AI and multimodal knowledge graphs
● Multimodal knowledge graphs, MMKG models, and MMKG completion
● Sonification, musicalisation, image generation of knowledge graphs
● Knowledge graph-based cross-modal translation (triples-to-music, image-to-triples, etc.)
● Evaluation and benchmarks for AI and multimodal knowledge graphs
● Bias, fairness, and cultural awareness in AI and knowledge graphs
● Large language models, knowledge graphs, and multimedia representation
● Knowledge extraction from and data integration of multimedia sources
● Multimedia metadata and ontologies
● Musical and image reasoning with knowledge graphs and AI
● Retrieval-Augmented Generation architectures involving AI models, knowledge graphs and multimedia

Manuscript submission information:

Important Dates:

    Submission Deadline: May 15, 2026
    Notification of Acceptance: August 31, 2026

Contributed papers must be submitted via the Journal of Web Semantics online submission system (https://www.editorialmanager.com/jows/default2.aspx): Please select the article type “VSI: AI and multimodal KG” when submitting the manuscript online.

All submissions deemed suitable to be sent for peer review will be reviewed by at least two independent reviewers. Once your manuscript is accepted, it will go into production and will be simultaneously published in the current regular issue and pulled into the online Special Issue. Articles from this Special Issue will appear in different regular issues of the journal, though they will be clearly marked and branded as Special Issue articles.

Please refer to the Guide for Authors to prepare your manuscript.

For any further information or questions, the authors may contact the Executive Guest Editor Dr. Albert Meroño-Peñuela (albert.merono@kcl.ac.uk).

Keywords:

multimodal knowledge graphs, generative ai and creativity, multimedia information retrieval, cross-modal translation, semantic representation