Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.5
2.5
Journals
Applied Sciences
Special Issues
Current Advances in Intelligent Semantic Technologies
applsci-logo
Submit to Applied Sciences Review for Applied Sciences Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Current Advances in Intelligent Semantic Technologies

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 February 2026) | Viewed by 2639

Special Issue Editors

Prof. Dr. Knut Hinkelmann

E-Mail Website
Guest Editor
School of Business, FHNW University of Applied Sciences and Arts Northwestern Switzerland, Olten, Switzerland
Interests: artificial intelligence; hybrid intelligence combining AI and human; enterprise modelling; alignment of business and IT; digitalization of business processes and knowledge work
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Bernhard Humm

E-Mail Website
Guest Editor
Department of Computer Science, Hochschule Darmstadt, University of Applied Sciences, 64295 Darmstadt, Germany
Interests: applied artificial intelligence

Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute to this Special Issue on Current Advances in Intelligent Semantic Technologies. Semantic technologies include approaches to the explicit represention of knowledge in a declarative form, suitable for processing by symbolic reasoning. This Special Issue highlights recent developments in semantic technologies, their integration with machine learning and their applications.

In this Special Issue, original research articles and reviews are welcome and the main focus of the articles is assumed to be on, but not limited to, the following areas:

  • Using semantic technologies to make data and information better understandable for machines;
  • Combining semantic technologies to enable machines to interpret, process and apply data effectively and efficiently;
  • Optimized reasoning engines for large-scale knowledge graphs and ontologies;
  • The design and engineering of knowledge-based systems and their applications;
  • Dynamic ontology evolution, enabling automated updates in response to new data streams and evolving knowledge bases;
  • Ontology matching and alignment, improving semantic interoperability across heterogeneous systems;
  • Scalable approaches for automated reasoning;
  • Knowledge extraction and enrichment from unstructured or semi-structured data;
  • Extensions of large language models with semantic reasoning and explainability;
  • Intelligent information systems integrating knowledge-driven AI with data-driven machine learning approaches;
  • Formal verification techniques ensuring logical consistency in ontology-based decision-making;
  • Incorporating ethical considerations and data privacy into AI systems and reasoning;
  • The integration of machine learning and knowledge engineering;
  • Tools for the development and use of intelligent information systems.

By focusing on semantic AI, reasoning, explainable logic-based decision-making, and ontology integration, this Special Issue provides a platform for advancing intelligent information systems that seamlessly combine rule-based methods, semantic interoperability, and automated deduction.

Prof. Dr. Knut Hinkelmann
Prof. Dr. Bernhard Humm
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 250 words) can be sent to the Editorial Office for assessment.

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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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 representation and reasoning (KR&R)
  • rule-based systems
  • ontology, knowledge graphs, ontology engineering
  • automated reasoning
  • semantic interoperability
  • scalable knowledge graphs
  • formal verification in AI
  • explainable AI
  • logic-based AI and deductive 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.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

31 pages, 2230 KB  
Article
VarDiff: A Conceptual Model for Representing Variable Differences Between Clinical Decision Support Systems
by Gourav Gupta, Jan Stanek, Wolfgang Mayer, Georg Grossmann and Markus Stumptner
Appl. Sci. 2026, 16(7), 3331; https://doi.org/10.3390/app16073331 - 30 Mar 2026
Viewed by 418
Abstract
Despite significant advancements in Artificial Intelligence, its widespread adoption in the clinical domain remains restricted due to the inherent complexity, fragmented nature, and diversity of healthcare systems. Each healthcare provider has unique data, clinical guidelines, data availability, system architectures, heterogeneity, and distribution. These [...] Read more.
Despite significant advancements in Artificial Intelligence, its widespread adoption in the clinical domain remains restricted due to the inherent complexity, fragmented nature, and diversity of healthcare systems. Each healthcare provider has unique data, clinical guidelines, data availability, system architectures, heterogeneity, and distribution. These challenges hinder the application of Clinical Decision Support Systems because of a limited understanding of how existing systems can be effectively redeployed across different healthcare providers. Redeployment is needed because it enables the reuse of existing knowledge, maximizes reusability, and avoids code duplication, thereby reducing the costs, effort, and time required to develop the Clinical Decision Support System from scratch. In addition, it ensures faster deployment and wider accessibility in the case of resource-constrained healthcare providers. An essential for redeployment is to identify the possible situations in which variables differ between two dynamic environments. To address this gap, we propose a structured multi-dimensional framework that systematically analyzes the potential differences between the variables. To represent the output of differences across dimensions based on variables in a systematic, machine-readable manner, we proposed a conceptual model, “VarDiff”, and a decision matrix of possible outcomes across five differential dimensions. This conceptual model provides a systematic, structural, and logical representation of a multidimensional framework for identifying differences among variables across data ecosystems. It formalizes variable characteristics in terms of semantic entities to observe differences among variables. The adaptation categories help identify the specific adaptation type, enabling the selection of relevant adaptation strategies in the “Mutator” component. Full article
(This article belongs to the Special Issue Current Advances in Intelligent Semantic Technologies)
►▼ Show Figures

Figure 1

23 pages, 1156 KB  
Article
An Industry-Ready Machine Learning Ontology
by Bernhard G. Humm
Appl. Sci. 2026, 16(2), 843; https://doi.org/10.3390/app16020843 - 14 Jan 2026
Viewed by 853
Abstract
This article presents an industry-ready ontology for the machine learning domain, which is named “ML Ontology”. While based on lightweight modelling languages, ML ontology provides novel features including built-in queries and quality assurance, as well as sophisticated reasoning. With ca. 700 individuals that [...] Read more.
This article presents an industry-ready ontology for the machine learning domain, which is named “ML Ontology”. While based on lightweight modelling languages, ML ontology provides novel features including built-in queries and quality assurance, as well as sophisticated reasoning. With ca. 700 individuals that define key ML concepts and ca. 5000 RDF triples, ML Ontology ranks among the largest domain-specific ontologies for ML. An experiment to estimate the correctness and completeness of ML terminology included in ML Ontology indicates an F1-score of 0.83. A benchmark evaluating query performance reveals query response times far below 100 ms even for complex queries and memory consumption below 3.5 MB. Its industry-readiness is demonstrated by benchmarks as well as two use case implementations within a data science platform. ML Ontology is open source and published under an MIT license. Full article
(This article belongs to the Special Issue Current Advances in Intelligent Semantic Technologies)
►▼ Show Figures

Figure 1

17 pages, 773 KB  
Article
Enhancing Trait Thesauri Interoperability Using a Manual and Automated Alignment Approach
by Jessica Titocci, Martina Pulieri, Ilaria Rosati and Naouel Karam
Appl. Sci. 2025, 15(23), 12484; https://doi.org/10.3390/app152312484 - 25 Nov 2025
Cited by 1 | Viewed by 490
Abstract
Over the past decade, trait data collection and mobilisation have expanded significantly, yet much of this data remains only partially compliant with FAIR principles. A major challenge lies in the inconsistent use of standards for harmonising heterogeneous trait data, along with the diversity, [...] Read more.
Over the past decade, trait data collection and mobilisation have expanded significantly, yet much of this data remains only partially compliant with FAIR principles. A major challenge lies in the inconsistent use of standards for harmonising heterogeneous trait data, along with the diversity, redundancy, and poor alignment of semantic artefacts developed to address this challenge. This study presents an approach to enhance the interoperability of the Trait Thesauri developed within the LifeWatch Italy research infrastructure for annotating and standardising trait data and metadata of aquatic organisms. This approach combines manual and automated alignment techniques, tested within the 2023 Ontology Alignment Evaluation Initiative. Domain experts manually aligned the Phytoplankton, Zooplankton, Macroalgae, Macrozoobenthos, and Fish trait thesauri, while five matching tools, LogMap, LogMapKG, LogMapLt, Matcha, and OLaLa, were tested for automated mappings. Both approaches revealed significant overlap among thesauri: Manual mapping identified 160 cross-thesauri correspondences and served as a benchmark for evaluating automated matching systems. Automated tools showed variable performance, with OLaLa achieving the best automated alignment results, with an F-measure of 0.93. Challenges in alignment included varying linguistic expressions and differing levels of concept specificity. The results highlight the importance of combining automation with expert validation to ensure mapping quality and allowed the development of a unified Trait Thesaurus, which integrates approximately 500 harmonised concepts, reducing redundancy and enhancing FAIR compliance in ecological and trait-based research. Full article
(This article belongs to the Special Issue Current Advances in Intelligent Semantic Technologies)
►▼ 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