Advances in Ontology and the Semantic Web

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

Deadline for manuscript submissions: 20 August 2024 | Viewed by 3594

Special Issue Editors


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Guest Editor
CNR, Istituto di Analisi dei Sistemi ed Informatica IASI “Antonio Ruberti”, Via Taurini 19, I-00185 Rome, Italy
Interests: methods and tools for ontology building; semantic interoperability; semantic similarity and relatedness; semantic search; quantum computing

E-Mail Website
Guest Editor
CNR, Istituto di Analisi dei Sistemi ed Informatica IASI “Antonio Ruberti”, Via Taurini 19, I-00185 Rome, Italy
Interests: semantic web; similarity reasoning; formal concept analysis; fuzzy sets; domain ontologies; geographic information systems

Special Issue Information

Dear Colleagues,

In the recent years, most of the Artificial Intelligence applications for predictive analysis are based on machine learning and neural network approaches, which rely on models based on an implicit (sub-symbolic) knowledge representation derived from the experience (data-driven). However, many applications for automated reasoning and searching tasks require explicit models based on a human readable (symbolic) representation of rules and properties. Furthermore, symbolic knowledge can also play a relevant role to address the so-called Explainable AI, whose goal is to provide human comprehensible explanations to the decisions taken by a predictive system.

In this respect, computational ontologies and the Semantic Web deserve a great interest since they are rooted in formal logic that is at the basis of the representation of symbolic knowledge.

This Special Issue aims at presenting methodological and technological advancements, as well as relevant use cases, in the scope of ontologies and the Semantic Web. In particular, high quality contributions are expected in, but not limited to, the areas of: languages for ontology representation; methodologies and tools for ontology engineering; ontology integration; ontology-based reasoning; ontology-based semantic search; data annotation; data integration and interoperability; semantic similarity; semantic relatedness; knowledge graphs; query answering on knowledge graphs; ontologies and sub-symbolic models; logic-based semantics for Explainable AI.

Dr. Francesco Taglino
Dr. Anna Formica
Guest Editors

Manuscript Submission Information

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Keywords

  • computational ontologies
  • knowledge graphs
  • semantics of data
  • logic-based reasoning
  • symbolic and sub-symbolic knowledge
  • ontologies for explainable AI

Published Papers (3 papers)

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Research

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26 pages, 1660 KiB  
Article
An Automated English Essay Scoring Engine Based on Neutrosophic Ontology for Electronic Education Systems
by Saad M. Darwish, Raad A. Ali and Adel A. Elzoghabi
Appl. Sci. 2023, 13(15), 8601; https://doi.org/10.3390/app13158601 - 26 Jul 2023
Viewed by 1016
Abstract
Most educators agree that essays are the best way to evaluate students’ understanding, guide their studies, and track their growth as learners. Manually grading student essays is a tedious but necessary part of the learning process. Automated Essay Scoring (AES) provides a feasible [...] Read more.
Most educators agree that essays are the best way to evaluate students’ understanding, guide their studies, and track their growth as learners. Manually grading student essays is a tedious but necessary part of the learning process. Automated Essay Scoring (AES) provides a feasible approach to completing this process. Interest in this area of study has exploded in recent years owing to the difficulty of simultaneously improving the syntactic and semantic scores of an article. Ontology enables us to consider the semantic constraints of the actual world. However, there are several uncertainties and ambiguities that cannot be accounted for by standard ontologies. Numerous AES strategies based on fuzzy ontologies have been proposed in recent years to reduce the possibility of imprecise knowledge presentation. However, no known efforts have been made to utilize ontologies with a higher level of fuzzification in order to enhance the effectiveness of identifying semantic mistakes. This paper presents the first attempt to address this problem by developing a model for efficient grading of English essays using latent semantic analysis (LSA) and neutrosophic ontology. In this regard, the presented work integrates commonly used syntactic and semantic features to score the essay. The integration methodology is implemented through feature-level fusion. This integrated vector is used to check the coherence and cohesion of the essay. Furthermore, the role of neutrosophic ontology is investigated by adding neutrosophic membership functions to the crisp ontology to detect semantic errors and give feedback. Neutrosophic logic allows the explicit inclusion of degrees of truthfulness, falsity, and indeterminacy. According to the comparison with state-of-the-art AES methods, the results show that the proposed model significantly improves the accuracy of scoring the essay semantically and syntactically and is able to provide feedback. Full article
(This article belongs to the Special Issue Advances in Ontology and the Semantic Web)
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16 pages, 1177 KiB  
Article
WASP—A Web Application to Support Syntactically and Semantically Correct SNOMED CT Postcoordination
by Cora Drenkhahn, Tessa Ohlsen, Joshua Wiedekopf and Josef Ingenerf
Appl. Sci. 2023, 13(10), 6114; https://doi.org/10.3390/app13106114 - 16 May 2023
Viewed by 1135
Abstract
Expressive clinical terminologies are of utmost importance for achieving a semantically interoperable data exchange and reuse in healthcare. SNOMED CT, widely respected as the most comprehensive terminology in medicine, provides formal concept definitions based on description logic which not only allows for advanced [...] Read more.
Expressive clinical terminologies are of utmost importance for achieving a semantically interoperable data exchange and reuse in healthcare. SNOMED CT, widely respected as the most comprehensive terminology in medicine, provides formal concept definitions based on description logic which not only allows for advanced querying of SNOMED-CT-coded data but also for flexibly augmenting its 350,000 concepts by allowing a controlled combination of these. This ability for postcoordination largely increases the expressivity of the terminology but correlates with an intrinsic complexity. Complicated by the current lack of tooling support, postcoordination is widely either ignored or applied in an error-prone way. To help facilitate the adoption of postcoordination, we implemented a web application that guides users through the creation of postcoordinated expressions (PCEs) while ensuring adherence to syntactic and semantic constraints. Our approach was largely facilitated by making use of the extensive SNOMED CT specifications as well as advanced HL7 FHIR Terminology Services. Qualitative evaluations confirmed the usability of the developed application and the correctness of the PCEs created with it. Full article
(This article belongs to the Special Issue Advances in Ontology and the Semantic Web)
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Review

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16 pages, 546 KiB  
Review
Semantic Similarity Based on Taxonomies
by Antonio De Nicola, Anna Formica, Ida Mele and Francesco Taglino
Appl. Sci. 2023, 13(21), 11959; https://doi.org/10.3390/app132111959 - 01 Nov 2023
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Abstract
The evaluation of the semantic similarity of concepts organized according to taxonomies is a long-standing problem in computer science and has attracted great attention from researchers over the decades. In this regard, the notion of information content plays a key role, and semantic [...] Read more.
The evaluation of the semantic similarity of concepts organized according to taxonomies is a long-standing problem in computer science and has attracted great attention from researchers over the decades. In this regard, the notion of information content plays a key role, and semantic similarity measures based on it are still on the rise. In this review, we address the methods for evaluating the semantic similarity between either concepts or sets of concepts belonging to a taxonomy that, often, in the literature, adopt different notations and formalisms. The results of this systematic literature review provide researchers and academics with insight into the notions that the methods discussed have in common through the use of the same notation, as well as their differences, overlaps, and dependencies, and, in particular, the role of the notion of information content in the evaluation of semantic similarity. Furthermore, in this review, a comparative analysis of the methods for evaluating the semantic similarity between sets of concepts is provided. Full article
(This article belongs to the Special Issue Advances in Ontology and the Semantic Web)
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