Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.9
2.4
Journals
Information
Special Issues
Knowledge Management and Semantic Web Technologies for Explainable Artificial Intelligence
share announcement

Knowledge Management and Semantic Web Technologies for Explainable Artificial Intelligence

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Applications".

Deadline for manuscript submissions: 30 April 2025 | Viewed by 3551

Special Issue Editors

Prof. Dr. Cecilia Zanni-Merk

E-Mail Website
Guest Editor
INSA Rouen Normandie, Normandie Université, LITIS (UR 4108/FR CNRS 3638), F-76000 Rouen, France
Interests: knowledge engineering; conceptualisation; ontologies and knowledge graphs; rule-based reasoning (crisp, fuzzy, probabilistic, spatio-temporal); case-based reasoning; knowledge and experience capitalisation; semantic web tech-nologies; explainability
Special Issues, Collections and Topics in MDPI journals
Dr. Franco Giustozzi

E-Mail Website
Guest Editor
INSA Strasbourg, University of Strasbourg, ICube Laboratory, CNRS (UMR 7357), 67000 Strasbourg, France
Interests: knowledge representation and reasoning; ontologies and knowledge graphs; semantic web technologies; stream reasoning; explainable AI
Dr. Ali Ayadi

E-Mail Website
Guest Editor
Institut National des Sciences Appliquées (INSA Strasbourg), Université de Strasbourg, CNRS, ICube Laboratory UMR 7357, 67000 Strasbourg, France
Interests: knowledge engineering; conceptual representation and domain knowledge modelling; semantic web; neu-ro-symbolic AI and explainable AI (XAI); inference processes and qualitative reasoning; discrete event simulation

Special Issue Information

Dear Colleagues,

At this point, considering that data are ubiquitous, their efficient is crucial. The convergence of knowledge management (KM), semantic web technologies (SWT), and explainable artificial intelligence (XAI) presents fertile ground for both research and practical applications. This Special Issue aims to address the latest advancements, challenges, and opportunities in leveraging semantic web technologies to improve knowledge management processes by enhancing decision-making, fostering innovation, and encouraging user trust in AI systems.

Topics of interest include, but are not limited to, the following:

  • Semantic web technologies for KM and XAI: Ontologies, linked data, RDF, OWL, SPARQL, and other semantic technologies with which to increase our ability to explain the abilities of AI.
  • Integration of KM and semantic web: Case studies and frameworks demonstrating how semantic web technologies can be integrated with KM practices to boost transparency and decision-making effectiveness.
  • Semantic search and retrieval: Techniques for improving the search for and retrieval of knowledge assets using semantic technologies, focusing on the clarity and interpretability of results.
  • Knowledge discovery and data mining: Using semantic web technologies for knowledge discovery and data mining in large or heterogeneous data sources, enhancing the explainability of the derived insights.
  • Intelligent KM systems: Development of intelligent systems that leverage AI, machine learning, and semantic technologies, focusing on systems that provide clear explanations of their reasoning processes.
  • Integration of semantic web technologies with XAI for enhanced knowledge representation and reasoning in KM.
    • Knowledge management frameworks leveraging XAI for better decision support.
    • Semantic reasoning and inference mechanisms to improve AI explainability in KM systems.
  • Applications and case studies: Practical applications and case studies showcasing the implementation of semantic web technologies in KM across various domains, with an emphasis on systems that offer clear and understandable outcomes.
  • Interoperability and standards: Standards and frameworks that ensure interoperability between KM systems and semantic web technologies for a unified approach to transparent and interpretable AI.
  • Challenges and future directions: Current challenges, unresolved issues, and future research directions in the integration of KM, semantic web technologies, and XAI.

Prof. Dr. Cecilia Zanni-Merk
Dr. Franco Giustozzi
Dr. Ali Ayadi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • knowledge management
  • knowledge representation and reasoning
  • semantic web technologies
  • explainability
  • Interpretable AI models
  • ontologies
  • knowledge graphs
  • linked data
  • knowledge discovery
  • intelligent systems
  • smart systems
  • knowledge-based systems

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

25 pages, 10756 KiB  
Article
Explainable Recommender Systems Through Reinforcement Learning and Knowledge Distillation on Knowledge Graphs
by Alexandra Vultureanu-Albişi, Ionuţ Murareţu and Costin Bădică
Information 2025, 16(4), 282; https://doi.org/10.3390/info16040282 - 30 Mar 2025
Viewed by 277
Abstract
Recommender systems have evolved significantly in recent years, using advanced techniques such as explainable artificial intelligence, reinforcement learning, and graph neural networks to enhance both efficiency and transparency. This study presents a novel framework, XR2K2G (X for explainability, [...] Read more.
Recommender systems have evolved significantly in recent years, using advanced techniques such as explainable artificial intelligence, reinforcement learning, and graph neural networks to enhance both efficiency and transparency. This study presents a novel framework, XR2K2G (X for explainability, first R for recommender systems, the second R for reinforcement learning, first K for knowledge graph, the second K stands for knowledge distillation, and G for graph-based techniques), with the goal of developing a next-generation recommender system with a focus on careers empowerment. To optimize recommendations while ensuring sustainability and transparency, the proposed method integrates reinforcement learning with graph-based representations of career trajectories. Additionally, it incorporates knowledge distillation techniques to further refine the model’s performance by transferring knowledge from a larger model to a more efficient one. Our approach employs reinforcement learning algorithms, graph embeddings, and knowledge distillation to enhance recommendations by providing clear and comprehensible explanations for the recommendations. In this work, we discuss the technical foundations of the framework, deployment strategies, and its practical applicability in real-world career scenarios. The effectiveness and interpretability of our approach are demonstrated through experimental results. Full article
(This article belongs to the Special Issue Knowledge Management and Semantic Web Technologies for Explainable Artificial Intelligence)
►▼ Show Figures

Figure 1

26 pages, 8715 KiB  
Article
Interpretable Deep Learning for Pneumonia Detection Using Chest X-Ray Images
by Jovito Colin and Nico Surantha
Information 2025, 16(1), 53; https://doi.org/10.3390/info16010053 - 15 Jan 2025
Viewed by 1848
Abstract
Pneumonia remains a global health issue, creating the need for accurate detection methods for effective treatment. Deep learning models like ResNet50 show promise in detecting pneumonia from chest X-rays; however, their black-box nature limits the transparency, which fails to meet that needed for [...] Read more.
Pneumonia remains a global health issue, creating the need for accurate detection methods for effective treatment. Deep learning models like ResNet50 show promise in detecting pneumonia from chest X-rays; however, their black-box nature limits the transparency, which fails to meet that needed for clinical trust. This study aims to improve model interpretability by comparing four interpretability techniques, which are Layer-wise Relevance Propagation (LRP), Adversarial Training, Class Activation Maps (CAMs), and the Spatial Attention Mechanism, and determining which fits best the model, enhancing its transparency with minimal impact on its performance. Each technique was evaluated for its impact on the accuracy, sensitivity, specificity, AUC-ROC, Mean Relevance Score (MRS), and a calculated trade-off score that balances interpretability and performance. The results indicate that LRP was the most effective in enhancing interpretability, achieving high scores across all metrics without sacrificing diagnostic accuracy. The model achieved 0.91 accuracy and 0.85 interpretability (MRS), demonstrating its potential for clinical integration. In contrast, Adversarial Training, CAMs, and the Spatial Attention Mechanism showed trade-offs between interpretability and performance, each highlighting unique image features but with some impact on specificity and accuracy. Full article
(This article belongs to the Special Issue Knowledge Management and Semantic Web Technologies for Explainable Artificial Intelligence)
►▼ Show Figures

Figure 1

22 pages, 3412 KiB  
Article
Educational Cyber–Physical Systems (ECPSs) for University 4.0
by Laurent Gallon, Khouloud Salameh, Richard Chbeir, Samia Bachir and Philippe Aniorté
Information 2024, 15(12), 790; https://doi.org/10.3390/info15120790 - 9 Dec 2024
Viewed by 772
Abstract
University 4.0 represents the adaptation of the Education 4.0 paradigm to the university context. The core principle is the automated supervision of the entire student learning process by an AI-driven computer assistant, allowing for timely adjustments based on the student’s progression. Critical to [...] Read more.
University 4.0 represents the adaptation of the Education 4.0 paradigm to the university context. The core principle is the automated supervision of the entire student learning process by an AI-driven computer assistant, allowing for timely adjustments based on the student’s progression. Critical to this process is the assistant’s ability to collect comprehensive information on all student activities within the curriculum, particularly overseeing pedagogical activities in real time to make necessary adaptations. This utilizes Educational Cyber–Physical Systems (ECPSs) to gather all relevant data and extract appropriate information effectively. This article examines a specific case of practical work involving students at two distinct geographical locations collaborating in a blended learning environment. A specialized ECPS is deployed to collect data from equipment at both sites, enabling the modeling of the pedagogical sequence, the cyber–physical system, and the data necessary for monitoring student progress in real time. Full article
(This article belongs to the Special Issue Knowledge Management and Semantic Web Technologies for Explainable Artificial Intelligence)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop