Modular Ontology Design: A State-of-Art of Diseases Ontology Modeling and Possible Issue

Lazarre, Warda; Kaladzavi, Guidedi; Amaria, Samdalle; Kolyang,

doi:10.18280/ria.360319

Cited by 3 publications

(3 citation statements)

References 21 publications

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“…Relationship in ontology specifies how objects are interrelated [31] by determining the domain and range of the relation. Lazarre et al [32], Hovy [33], Atkinson et al [34] gave more details on modularisation and constraints necessary when building ontologies. With proté gé 5.0, we used the VOWL tool to have a better view of the different concepts, their data properties and relationships between the concepts (see Figure 3).…”

Section: Relationshipsmentioning

confidence: 99%

OntoCin: A Multimedia Ontology for the Semantic Indexation of Cinematographic Resources on the Web of Data

Samdalle,

Hayatou,

Warda

et al. 2024

RIA

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The flood of Multimedia Resources on the Web of Data and offline platforms is a clear proof of the increase of such resources in day-to-day activities of modern society, especially web series, documentaries, fictions, etc. Multimedia Ontologies applied to film-related features entail to describe not only the film production process, but also their social and environmental inferences (with cultures, attractive sites, etc.). The insufficient insight of annotated features in existing ontologies affects retrieval accuracy on useful facts necessary in today's society. This paper presents a Multimedia Ontology for the co-construction and indexing of Cinematographic resources (OntoCin) on the Semantic Web, built on the Human Activity Theory (HAT) modelling approach and the Competency-based Questions Methodology which allowed to scope cinematographic knowledge. This ontology enables information retrieval by enhancing annotation and indexing of scenes, emotions, shooting places (touristic sites), film-users' preferences, socio-cultural knowledge and impressions on the Web of Data. We made some queries on the ontology and came out with results. This helped to set ground for a semantic wiki architecture that will facilitate the co-construction of multimedia resources based on this Multimedia Ontology.

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Section: Relationshipsmentioning

confidence: 99%

OntoCin: A Multimedia Ontology for the Semantic Indexation of Cinematographic Resources on the Web of Data

Samdalle,

Hayatou,

Warda

et al. 2024

RIA

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“…It provides a shared language for modeling a domain and offers important information that cannot be obtained from simple dictionaries. Ontology refers to the collection of concepts that are utilized to describe and represent a specific domain [3]. Gruber defines ontology as an explicit specification of a conceptualization [4], while computer science defines it as a formal representation of knowledge in a hierarchical way [5].…”

Section: Related Work Of Semantic Similarity Measures Based On Wn/awnmentioning

confidence: 99%

Assessing Semantic Similarity Measures and Proposing a WuP-Resnik Hybrid Metric for Enhanced Arabic Language Processing

Dilekh,

Boulahia,

Benharzallah

2023

RIA

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The accurate quantification of semantic similarity among Arabic words presents a significant challenge in Natural Language Processing (NLP). This is a critical aspect for a wide array of text-centric applications, including recommendation systems, plagiarism detection, and information retrieval. Enhanced performance in searches and classification is achieved by simplifying concepts within machine processing and unifying words with close meanings. This research investigates the complexities of measuring semantic similarity in Arabic, a language with distinct features such as the absence of short vowels in written text that renders distinguishing words without vowel diacritics challenging for computing systems. The effectiveness of various semantic similarity metrics is meticulously evaluated in this study, with a specific focus on their applicability to Arabic WordNet and English WordNet. The challenges associated with using Arabic WordNet for measuring word similarity are illuminated, and an innovative metric, integrating the Wu-Palmer and Resnik metrics, is proposed to enhance result accuracy. The primary accomplishment of this research resides in the identification of an optimal semantic similarity metric with a reduced error rate, thereby boosting the precision of results in NLP. This pivotal advancement paves the way for more accurate semantic assessments and improved performance across a broad spectrum of applications.

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“…Since managing knowledge represented by ontologies using machine learning tools becomes increase, authors introduced possibility to capture ontology characteristics through hidden Markov Model (HMM) [13] and proposed in the study of Warda et al [14] an approach to turn ontology into HMM based on ontology triples produced by SPARQL queries on it.…”

Section: Introductionmentioning

confidence: 99%

A Hidden Markov Model-Based Approach for Lightweight Ontology Modularization Using K-Means Clustering

Warda,

Ahmed,

Hayatou

et al. 2024

ISI

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Nowadays, ontologies are backbone of Semantic Web. Several domains use ontologies as knowledge models. As their number is constantly increasing, designers are opting to reuse some of those that exist to build new ones. When it is impossible to reuse a part depending on its organization, they import the whole ontology and this makes the manipulation cumbersome especially if the ontology has large concepts. Therefore, segmenting ontologies into partitions, if they are not yet, becomes a constant challenge for designers. This paper presents an approach to modularize ontology using hidden Markov model. Ontology triples are extracted within ontology through SPARQL queries and labelled with integers. The labelled triples constituted a Markov chain where ontology concepts are states and ontology relationships are symbols. This set is used to initialize HMM parameters such as states transition probabilities and symbols observation probabilities matrix and initial states probabilities vector. The transition probabilities matrix of HMM is then used as input of K-Means algorithm to generated modules of ontology concepts. This approach does not handled ontology axioms, which characterize heavy ontologies, and only lightweight ontologies are considered. Experiment on eighteen ontologies, obtained modules satisfied ontology modularization criteria such as independence, non-redundancy, correctness and completeness.

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Modular Ontology Design: A State-of-Art of Diseases Ontology Modeling and Possible Issue

Cited by 3 publications

References 21 publications

OntoCin: A Multimedia Ontology for the Semantic Indexation of Cinematographic Resources on the Web of Data

OntoCin: A Multimedia Ontology for the Semantic Indexation of Cinematographic Resources on the Web of Data

Assessing Semantic Similarity Measures and Proposing a WuP-Resnik Hybrid Metric for Enhanced Arabic Language Processing

A Hidden Markov Model-Based Approach for Lightweight Ontology Modularization Using K-Means Clustering

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