Advanced Ontologies and Semantic Web Technologies

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (15 August 2023) | Viewed by 1464

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Guest Editor
Department of Electrical and Computer Engineering, Hellenic Mediterranean University, 71410 Estavromenos, Greece
Interests: databases; artificial intelligence; software engineering; semantic web; distributed algorithms; communication protocols; fault tolerance; multimedia databases; methods of automatic optimization of software; interactive verifier
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Special Issue Information

Dear Colleagues,

In recent years, the amount of data that various web applications have to manage has grown rapidly. Semantic web technologies provide intelligent ways to manage large volumes of data. These ways can include search engines, intelligent agents, push systems, etc. The representation used by the semantic web is based on ontologies. In recent years, there are many implementations of ontologies in many application fields. These ontologies can contain tens of thousands of classes each. Therefore, effective ways of managing them are needed.

Topics (but not limited):

  • Knowledge representation and reasoning;
  • Ontology development and enrichment;
  • Ontology and linked data set quality assurance;
  • Semantic harmonization and ontology alignment;
  • Knowledge graphs;
  • Knowledge representation systems in life sciences and medicine;
  • NLP and text mining using semantic technologies;
  • Novel approaches for data integration of heterogeneous data sources;
  • Novel tools and ontologies for data interpretation and visualization;
  • Deep learning and semantic technologies;
  • Real-world ontology-based applications;
  • Methods and tools for the FAIRification of datasets;
  • Artificial intelligence techniques for the Semantic Web;
  • Ontologies and semantic web for decision support;
  • Semantic technologies and AI explainability;
  • Ontology summarization.

Dr. Nikolaos Papadakis
Guest Editor

Manuscript Submission Information

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Published Papers (1 paper)

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Research

19 pages, 1318 KiB  
Article
Systematic Approach for Measuring Semantic Relatedness between Ontologies
by Abdelrahman Osman Elfaki and Yousef H. Alfaifi
Electronics 2023, 12(6), 1394; https://doi.org/10.3390/electronics12061394 - 15 Mar 2023
Viewed by 1212
Abstract
Measuring ontology matching is a critical issue in knowledge engineering and supports knowledge sharing and knowledge evolution. Recently, linguistic scientists have defined semantic relatedness as being more significant than semantic similarities in measuring ontology matching. Semantic relatedness is measured using synonyms and hypernym–hyponym [...] Read more.
Measuring ontology matching is a critical issue in knowledge engineering and supports knowledge sharing and knowledge evolution. Recently, linguistic scientists have defined semantic relatedness as being more significant than semantic similarities in measuring ontology matching. Semantic relatedness is measured using synonyms and hypernym–hyponym relationships. In this paper, a systematic approach for measuring ontology semantic relatedness is proposed. The proposed approach is developed with a clear and fully described methodology, with illustrative examples used to demonstrate the proposed approach. The relatedness between ontologies has been measured based on class level by using lexical features, defining semantic similarity of concepts based on hypernym–hyponym relationships. For evaluating our proposed approach against similar works, benchmarks are generated using five properties: related meaning features, lexical features, providing technical descriptions, proving applicability, and accuracy. Technical implementation is carried out in order to demonstrate the applicability of our approach. The results demonstrate an achieved accuracy of 99%. The contributions are further highlighted by benchmarking against recent related works. Full article
(This article belongs to the Special Issue Advanced Ontologies and Semantic Web Technologies)
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