Machine Learning Selection of Candidate Ontologies for Automatic Extraction of Context Words and Axioms from Ontology Corpus

Rudwan, Mohammed Suleiman Mohammed; Fonou-Dombeu, Jean Vincent

doi:10.1007/978-3-031-21047-1_24

Cited by 3 publications

(3 citation statements)

References 13 publications

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“…To the best of our knowledge, the majority of previous studies [8][9][10]24] did not process the logical axioms and annotations in their ontology-merging methods. This shortcoming is addressed in our proposed algorithm through the processing of various criteria, including logical axioms, individuals and annotations, with the aim of producing quality output ontologies that include relevant features from candidate ontologies as well as meeting the prescribed ontology-merging quality criteria [12][13][14].…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Algorithm for Multi-Criteria Ontology Merging through Iterative Update of RDF Graph

Rudwan,

Fonou-Dombeu

2024

BDCC

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Ontology merging is an important task in ontology engineering to date. However, despite the efforts devoted to ontology merging, the incorporation of relevant features of ontologies such as axioms, individuals and annotations in the output ontologies remains challenging. Consequently, existing ontology-merging solutions produce new ontologies that do not include all the relevant semantic features from the candidate ontologies. To address these limitations, this paper proposes a novel algorithm for multi-criteria ontology merging that automatically builds a new ontology from candidate ontologies by iteratively updating an RDF graph in the memory. The proposed algorithm leverages state-of-the-art Natural Language Processing tools as well as a Machine Learning-based framework to assess the similarities and merge various criteria into the resulting output ontology. The key contribution of the proposed algorithm lies in its ability to merge relevant features from the candidate ontologies to build a more accurate, integrated and cohesive output ontology. The proposed algorithm is tested with five ontologies of different computing domains and evaluated in terms of its asymptotic behavior, quality and computational performance. The experimental results indicate that the proposed algorithm produces output ontologies that meet the integrity, accuracy and cohesion quality criteria better than related studies. This performance demonstrates the effectiveness and superior capabilities of the proposed algorithm. Furthermore, the proposed algorithm enables iterative in-memory update and building of the RDF graph of the resulting output ontology, which enhances the processing speed and improves the computational efficiency, making it an ideal solution for big data applications.

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Section: Literature Reviewmentioning

confidence: 99%

A Novel Algorithm for Multi-Criteria Ontology Merging through Iterative Update of RDF Graph

Rudwan,

Fonou-Dombeu

2024

BDCC

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“…However, these methods may not be sufficient on their own, as they do not consider the semantic features of ontologies, especially when concepts are linguistically different but represent the same underlying knowledge, such as acronyms and synonyms. Additionally, some machine learning-based methods used for ontology selection and alignment such as Word2Vec and Skip-gram are not able to recognize previously unseen vocabulary [20,21]. Some studies have proposed structure-based methods for ontology alignment [22][23][24], but these are limited by the different goals and purposes of the design of the target ontologies [23].…”

Section: Introductionmentioning

confidence: 99%

Hybridizing Fuzzy String Matching and Machine Learning for Improved Ontology Alignment

Rudwan

Fonou-Dombeu

2023

Future Internet

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Ontology alignment has become an important process for identifying similarities and differences between ontologies, to facilitate their integration and reuse. To this end, fuzzy string-matching algorithms have been developed for strings similarity detection and have been used in ontology alignment. However, a significant limitation of existing fuzzy string-matching algorithms is their reliance on lexical/syntactic contents of ontology only, which do not capture semantic features of ontologies. To address this limitation, this paper proposed a novel method that hybridizes fuzzy string-matching algorithms and the Deep Bidirectional Transformer (BERT) deep learning model with three machine learning regression classifiers, namely, K-Nearest Neighbor Regression (kNN), Decision Tree Regression (DTR), and Support Vector Regression (SVR), to perform the alignment of ontologies. The use of the kNN, SVR, and DTR classifiers in the proposed method resulted in the building of three similarity models (SM), encoded SM-kNN, SM-SVR, and SM-DTR, respectively. The experiments were conducted on a dataset obtained from the anatomy track in the Ontology Alignment and Evaluation Initiative 2022 (OAEI 2022). The performances of the SM-kNN, SM-SVR, and SM-DTR models were evaluated using various metrics including precision, recall, F1-score, and accuracy at thresholds 0.70, 0.80, and 0.90, as well as error rates and running times. The experimental results revealed that the SM-SVR model achieved the best recall of 1.0, while the SM-DTR model exhibited the best precision, accuracy, and F1-score of 0.98, 0.97, and 0.98, respectively. Furthermore, the results showed that the SM-kNN, SM-SVR, and SM-DTR models outperformed state-of-the-art alignment systems that participated in the OAEI 2022 challenge, indicating the superior capability of the proposed method.

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“…Natural Language Processing (NLP) is one of the most significant sciences utilized in the semantic web. NLP analyses human natural language in text format using computer techniques to obtain meaningful semantic information [23]. Several NLP methods have been utilized in conjunction with ontologies in many studies, for instance, [24][25][26].…”

Section: Introductionmentioning

confidence: 99%

An Ontology Development Methodology Based on Ontology-Driven Conceptual Modeling and Natural Language Processing: Tourism Case Study

Haridy

Ismail

Badr

et al. 2023

BDCC

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Ontologies provide a powerful method for representing, reusing, and sharing domain knowledge. They are extensively used in a wide range of disciplines, including artificial intelligence, knowledge engineering, biomedical informatics, and many more. For several reasons, developing domain ontologies is a challenging task. One of these reasons is that it is a complicated and time-consuming process. Multiple ontology development methodologies have already been proposed. However, there is room for improvement in terms of covering more activities during development (such as enrichment) and enhancing others (such as conceptualization). In this research, an enhanced ontology development methodology (ON-ODM) is proposed. Ontology-driven conceptual modeling (ODCM) and natural language processing (NLP) serve as the foundation of the proposed methodology. ODCM is defined as the utilization of ontological ideas from various areas to build engineering artifacts that improve conceptual modeling. NLP refers to the scientific discipline that employs computer techniques to analyze human language. The proposed ON-ODM is applied to build a tourism ontology that will be beneficial for a variety of applications, including e-tourism. The produced ontology is evaluated based on competency questions (CQs) and quality metrics. It is verified that the ontology answers SPARQL queries covering all CQ groups specified by domain experts. Quality metrics are used to compare the produced ontology with four existing tourism ontologies. For instance, according to the metrics related to conciseness, the produced ontology received a first place ranking when compared to the others, whereas it received a second place ranking regarding understandability. These results show that utilizing ODCM and NLP could facilitate and improve the development process, respectively.

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Machine Learning Selection of Candidate Ontologies for Automatic Extraction of Context Words and Axioms from Ontology Corpus

Cited by 3 publications

References 13 publications

A Novel Algorithm for Multi-Criteria Ontology Merging through Iterative Update of RDF Graph

A Novel Algorithm for Multi-Criteria Ontology Merging through Iterative Update of RDF Graph

Hybridizing Fuzzy String Matching and Machine Learning for Improved Ontology Alignment

An Ontology Development Methodology Based on Ontology-Driven Conceptual Modeling and Natural Language Processing: Tourism Case Study

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