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Information
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Knowledge Representation and Ontology-Based Data Management
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Knowledge Representation and Ontology-Based Data Management

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

Deadline for manuscript submissions: 15 November 2025 | Viewed by 8806

Special Issue Editors

Dr. Domenico Fabio Savo

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Guest Editor
Department of Management, Information, and Production Engineering (DIGIP), University of Bergamo, 24044 Dalmine, BG, Italy
Interests: knowledge representation; description logics; ontologies; semantic web
Dr. Gianluca Cima

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Guest Editor
CNRS, Bordeaux INP, LaBRI, University of Bordeaux , UMR 5800, Talence, France
Interests: knowledge representation; description logics; ontologies; semantic web
Dr. Riccardo Rosati

E-Mail Website
Guest Editor
Department of Computer, Control and Management Engineering (DIAG), Sapienza University of Rome, 00185 Rome, Italy
Interests: knowledge representation; description logics; ontologies; semantic web; databases

Special Issue Information

Dear Colleagues,

Knowledge representation and reasoning (KRR) is a fundamental branch of artificial intelligence that is instrumental in developing innovative solutions in computer science, leading to tangible benefits from both a theoretical and a practical perspective.

In particular, KRR techniques are successfully exploited in data management applications. In this context, the ontology-based data management (OBDM) paradigm is a notable example of such usage, where a logic-based ontology provides a rich conceptual description of the domain of the data, linked through declarative mapping assertions to existing data sources. The distinguishing feature of this paradigm is that system users specify their information needs directly on the high-level domain representation provided by the ontology, without having to know the technical structure of the underlying data sources.  

In this Special Issue, we aim to collect innovative research papers on KRR and its application, with a particular focus on the OBDM paradigm. We invite original work on both the theoretical and foundational aspects of KRR and OBDM, as well as papers reporting examples of their real-world applications.

Dr. Domenico Fabio Savo
Dr. Gianluca Cima
Dr. Riccardo Rosati
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

  • computational aspects of knowledge representation and reasoning
  • applications of knowledge representation and reasoning
  • ontology-based data management
  • practical aspects of ontology-based data management
  • description logics
  • knowledge graphs and open linked data
  • knowledge representation languages
  • ontology formalisms and models
  • semantic web

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Published Papers (4 papers)

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Research

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19 pages, 2296 KiB  
Article
A Hybrid Approach to Ontology Construction for the Badini Kurdish Language
by Media Azzat, Karwan Jacksi and Ismael Ali
Information 2024, 15(9), 578; https://doi.org/10.3390/info15090578 - 19 Sep 2024
Viewed by 1826
Abstract
Semantic ontologies have been widely utilized as crucial tools within natural language processing, underpinning applications such as knowledge extraction, question answering, machine translation, text comprehension, information retrieval, and text summarization. While the Kurdish language, a low-resource language, has been the subject of some [...] Read more.
Semantic ontologies have been widely utilized as crucial tools within natural language processing, underpinning applications such as knowledge extraction, question answering, machine translation, text comprehension, information retrieval, and text summarization. While the Kurdish language, a low-resource language, has been the subject of some ontological research in other dialects, a semantic web ontology for the Badini dialect remains conspicuously absent. This paper addresses this gap by presenting a methodology for constructing and utilizing a semantic web ontology for the Badini dialect of the Kurdish language. A Badini annotated corpus (UOZBDN) was created and manually annotated with part-of-speech (POS) tags. Subsequently, an HMM-based POS tagger model was developed using the UOZBDN corpus and applied to annotate additional text for ontology extraction. Ontology extraction was performed by employing predefined rules to identify nouns and verbs from the model-annotated corpus and subsequently forming semantic predicates. Robust methodologies were adopted for ontology development, resulting in a high degree of precision. The POS tagging model attained an accuracy of 95.04% when applied to the UOZBDN corpus. Furthermore, a manual evaluation conducted by Badini Kurdish language experts yielded a 97.42% accuracy rate for the extracted ontology. Full article
(This article belongs to the Special Issue Knowledge Representation and Ontology-Based Data Management)
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19 pages, 5781 KiB  
Article
A Methodology for Integrating Hierarchical VMAP-Data Structures into an Ontology Using Semantically Represented Analyses
by Philipp Spelten, Morten-Christian Meyer, Anna Wagner, Klaus Wolf and Dirk Reith
Information 2024, 15(1), 21; https://doi.org/10.3390/info15010021 - 29 Dec 2023
Viewed by 1781
Abstract
Integrating physical simulation data into data ecosystems challenges the compatibility and interoperability of data management tools. Semantic web technologies and relational databases mostly use other data types, such as measurement or manufacturing design data. Standardizing simulation data storage and harmonizing the data structures [...] Read more.
Integrating physical simulation data into data ecosystems challenges the compatibility and interoperability of data management tools. Semantic web technologies and relational databases mostly use other data types, such as measurement or manufacturing design data. Standardizing simulation data storage and harmonizing the data structures with other domains is still a challenge, as current standards such as the ISO standard STEP (ISO 10303 ”Standard for the Exchange of Product model data”) fail to bridge the gap between design and simulation data. This challenge requires new methods, such as ontologies, to rethink simulation results integration. This research describes a new software architecture and application methodology based on the industrial standard ”Virtual Material Modelling in Manufacturing” (VMAP). The architecture integrates large quantities of structured simulation data and their analyses into a semantic data structure. It is capable of providing data permeability from the global digital twin level to the detailed numerical values of data entries and even new key indicators in a three-step approach: It represents a file as an instance in a knowledge graph, queries the file’s metadata, and finds a semantically represented process that enables new metadata to be created and instantiated. Full article
(This article belongs to the Special Issue Knowledge Representation and Ontology-Based Data Management)
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26 pages, 2738 KiB  
Article
Semantic Modelling Approach for Safety-Related Traffic Information Using DATEX II
by J. Javier Samper-Zapater, Julián Gutiérrez-Moret, Jose Macario Rocha, Juan José Martinez-Durá and Vicente R. Tomás
Information 2024, 15(1), 3; https://doi.org/10.3390/info15010003 - 19 Dec 2023
Cited by 2 | Viewed by 2276
Abstract
The significance of Linked Open Data datasets for traffic information extends beyond just including open traffic data. It incorporates links to other relevant thematic datasets available on the web. This enables federated queries across different data platforms from various countries and sectors, such [...] Read more.
The significance of Linked Open Data datasets for traffic information extends beyond just including open traffic data. It incorporates links to other relevant thematic datasets available on the web. This enables federated queries across different data platforms from various countries and sectors, such as transport, geospatial, environmental, weather, and more. Businesses, researchers, national operators, administrators, and citizens at large can benefit from having dynamic traffic open data connected to heterogeneous datasets across Member States. This paper focuses on the development of a semantic model that enhances the basic service to access open traffic data through a LOD-enhanced Traffic Information System in alignment with the ITS Directive (2010/40/EU). The objective is not limited to just viewing or downloading data but also to improve the extraction of meaningful information and enable other types of services that are only achievable through LOD. By structuring the information using the RDF format meant for machines and employing SPARQL for querying, LOD allows for comprehensive and unified access to all datasets. Considering that the European standard DATEX II is widely used in many priority areas and services mentioned in the ITS Directive, LOD DATEX II was developed as a complementary approach to DATEX II XML. This facilitates the accessibility and comprehensibility of European traffic data and services. As part of this development, an ontological model called dtx_srti, based on the DATEX II Ontology, was created to support these efforts. Full article
(This article belongs to the Special Issue Knowledge Representation and Ontology-Based Data Management)
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Review

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19 pages, 3787 KiB  
Review
Survey on Knowledge Representation Models in Healthcare
by Batoul Msheik, Mehdi Adda, Hamid Mcheick and Mohamed Dbouk
Information 2024, 15(8), 435; https://doi.org/10.3390/info15080435 - 26 Jul 2024
Cited by 1 | Viewed by 1750
Abstract
Knowledge representation models that aim to present data in a structured and comprehensible manner have gained popularity as a research focus in the pursuit of achieving human-level intelligence. Humans possess the ability to understand, reason and interpret knowledge. They acquire knowledge through their [...] Read more.
Knowledge representation models that aim to present data in a structured and comprehensible manner have gained popularity as a research focus in the pursuit of achieving human-level intelligence. Humans possess the ability to understand, reason and interpret knowledge. They acquire knowledge through their experiences and utilize it to carry out various actions in the real world. Similarly, machines can also perform these tasks, a process known as knowledge representation and reasoning. In this survey, we present a thorough analysis of knowledge representation models and their crucial role in information management within the healthcare domain. We provide an overview of various models, including ontologies, first-order logic and rule-based systems. We classify four knowledge representation models based on their type, such as graphical, mathematical and other types. We compare these models based on four criteria: heterogeneity, interpretability, scalability and reasoning in order to determine the most suitable model that addresses healthcare challenges and achieves a high level of satisfaction. Full article
(This article belongs to the Special Issue Knowledge Representation and Ontology-Based Data Management)
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