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

Special Issue on Ethics in Knowledge Engineering
提出日: 2026-07-31

As Knowledge Engineering (KE) transitions from a manual craft to an AI-augmented discipline, the ethical implications of how we construct, publish, and reuse knowledge have become paramount. The integration of Large Language Models (LLMs) into the KE lifecycle offers unprecedented efficiency but also increases risks of encoded bias, loss of provenance, and the potential for semantic hallucinations. Furthermore, as Knowledge Graphs (KGs) and ontologies increasingly underpin critical decision-making systems in healthcare, law, and finance, adhering to FAIR (Findable, Accessible, Interoperable, and Reusable) principles is no longer just a technical goal, it is an ethical imperative.

This special issue explores the intersection of moral philosophy and knowledge representation. We seek to define the ethical responsibilities of both "knowledge creators" (those who design and populate these structures) and "knowledge consumers" (those who build applications upon them). Beyond theoretical frameworks, we are interested in the operationalisation of ethics: how can we inject fairness, accountability, and transparency directly into our schemas, triplestores, and engineering tools?

Guest editors:

Prof. Valentina Presutti
University of Bologna, Bologna, Italy
Email: valentina.presutti@unibo.it 

Special issue information:

We encourage the submission of novel, previously unpublished research related to the following themes:

    Theoretical Ethical Frameworks: Defining "Ethical Knowledge Engineering"both in general or for specific domain of interest (Health, Cultural Heritage, Law, etc.) and responsibilities in decentralized and collaborative knowledge construction.
    Operationalising Ethics: Semantic patterns and ontology models for representing ethical constraints, values, and norms. Software development practices to ensure fairness, transparency, and accountability.
    AI-Aided KE Ethics: Addressing bias and transparency in AI-driven automation of ontology design and knowledge graph population.
    FAIR Principles as Ethical Duty: how FAIRness fosters social equity and scientific integrity.
    Legal & Regulatory Compliance: Representing GDPR, intellectual property, and data sovereignty within knowledge graphs.
    Knowledge Graphs for Ethical AI: Using symbolic representations to provide guardrails, explainability, and bias-detection for "black-box" AI models.
    Economic and Social Impact: Novel business models that support creative industries and public institutions while remaining ethically compliant.
    In-Use and Case Studies: Reports on the deployment of ethical knowledge engineering in real-world scenarios, particularly in knowledge curator institutions.

Manuscript submission information:

Important Dates:

Submission Deadline: July 31, 2026
Review notification: September 30, 2026
Publication: November 31, 2026

https://www.sciencedirect.com/special-issue/330354/ethics-in-knowledge-engineering

Special Issue on Knowledge Engineering Automation
提出日: 2026-07-31

The rapid evolution of Large Language Models (LLMs) and neuro-symbolic AI has ushered in a new era for Knowledge Engineering (KE). Traditionally, the development of ontologies and Knowledge Graphs (KGs) has been a labor-intensive, manual process requiring deep domain expertise and significant time investment. Today, advances in automated reasoning and generative AI are enabling the automation of core KE tasks: from the initial elicitation of requirements via competency questions to the complex processes of ontology learning, population, and formal validation. As the Semantic Web community shifts toward this more agile knowledge engineering process, tools that can autonomously generate, document, and evaluate knowledge structures are becoming essential for maintaining scalable and high-quality decentralized data architectures.

This special issue aims to bridge the gap between theoretical knowledge representation and practical, automated tooling. We seek to provide a platform for novel research that explores how automation can streamline the lifecycle of knowledge-based systems, reduce human bottleneck, and ensure the structural and semantic integrity of evolving Knowledge Graphs.

Guest editors:

Assoc. Professor Valentina V. Presutti, University of Bologna, Bologna, Italy
Email: valentina.presutti@unibo.it

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:

    Automated Requirement Elicitation: Generation of competency questions (CQs) from natural language or legacy data.
    Ontology Generation and Learning: Automated extraction of classes, properties, and axioms from unstructured and semi-structured sources.
    Knowledge Graph Construction: Pipelines for automated entity linking, relation extraction, and KG population.
    Automated Validation and Evaluation: Tools for structural, logical, and semantic consistency checking, including automated unit testing for ontologies.
    Documentation Automation: automatic generation of human-readable documentation and diagrams from formal ontologies (e.g., LODE, Graffoo).
    LLMs in the KE Loop: The role of Prompt Engineering and Retrieval-Augmented Generation (RAG) in assisting knowledge engineers.
    Ontology Alignment and Mapping: Automated discovery and maintenance of links between disparate schemas and data stores.
    Quality Assurance: Automated detection of "anti-patterns," biases, and technical debt in knowledge models.
    Refinement and Evolution: Systems for automated version control, change impact analysis, and ontology evolution.
    Benchmark and Tooling: New datasets, frameworks, and evaluation metrics specifically designed for automated KE tasks.
    Human-in-the-loop Automation: Interfaces and workflows that balance machine efficiency with expert oversight.

Manuscript submission information:

Important Dates:

    Submission deadline: 31 July 2026
    Review Notification: by 30 November 2026

Contributed papers must be submitted via the Journal of Web Semantics online submission system (https://submit.elsevier.com/JOWS): Please select the article type “VSI: WEBSEM_KE Automation” when submitting the manuscript online.

https://www.sciencedirect.com/special-issue/330427/knowledge-engineering-automation

Special Issue on Multi-dimensional Knowledge Graphs and Multi-perspective Ontologies
提出日: 2026-07-31

The increasing complexity of modern data ecosystems requires Knowledge Graphs (KGs) to move beyond static, flat representations of facts. Real-world information is inherently multi-dimensional, spanning different modalities like text, images, and sensor data, and often subject to multiple perspectives depending on the observer, the temporal context, the intended use, or the cultural framework. Representing this "contextualized truth" remains a significant challenge for the Semantic Web community. Traditional RDF triples often struggle to capture the nuances of provenance, validity intervals, and conflicting viewpoints without incurring significant computational overhead.

This special issue focuses on the next generation of knowledge representation: infrastructures capable of handling multi-perspective ontologies and multi-dimensional data. We aim to explore the full lifecycle of these systems, from the foundational ontology patterns needed to model perspectival data to the underlying storage engines and indexing strategies required to keep multi-layered querying performant at scale.

Guest editors:

Prof. Valentina Presutti
University of Bologna, Bologna, Italy
Email: valentina.presutti@unibo.it 

Special issue information:

We invite submissions of original research, applied case studies, and comprehensive surveys. Topics of interest include, but are not limited to:

Modeling Multi-perspectivity: Ontology design patterns for context, facets, and viewpoints; representing disagreement and conflicting claims in KGs.

Multimodal Infrastructure: Systems for the integrated storage and retrieval of cross-modal knowledge graphs (text, audio, visual, and structured data).

Layered Querying: Query languages or extensions to SPARQL or querying strategies for traversing multi-dimensional layers and filtering by perspective.

Scalability and Performance: Indexing techniques and distributed architectures for high-volume, multi-dimensional knowledge graphs.

Contextualized Fact Construction: Automated pipelines for extracting multi-perspective facts from heterogeneous sources.

Maintenance of Evolving KGs: Versioning, truth maintenance systems, and belief revision in multi-perspective environments.

Applied Research & Pilots: Real-world deployments and validation in domains e.g. digital humanities, medical diagnosis, legal reasoning, or news verification.

Evaluation Metrics: Frameworks for assessing the richness, accuracy, and utility of multi-dimensional knowledge graphs.

State-of-the-Art Surveys: Critical reviews of theoretical approaches and existing tool support, identifying gaps for the 2026–2030 research agenda.

Manuscript submission information:

Important Dates:

    Submission deadline: 31 July 2026
    Review Notification: by 15 September 2026
    Publication: by 15 December 2026

Contributed papers must be submitted via the Journal of Web Semantics online submission system (https://submit.elsevier.com/JOWS): Please select the article type “VSI: Multi-dimensional KG” when submitting the manuscript online.

https://www.sciencedirect.com/special-issue/330352/multi-dimensional-knowledge-graphs-and-multi-perspective-ontologies