Hybridizing Fuzzy String Matching and Machine Learning for Improved Ontology Alignment

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

doi:10.3390/fi15070229

Cited by 3 publications

(6 citation statements)

References 52 publications

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“…Neutel and de Boer [151] concluded that BERT performed well for the automatic alignment of two occupational ontologies. Rudwan and Fonou-Dombeu [152] proposed an ontology alignment method that integrates fuzzy string-matching algorithms and the BERT model for conducting an ontology alignment, the BERT model showed promising results.…”

Section: Figure 21 Blstm -Cnn [132] [130]mentioning

confidence: 99%

A Review of State of the Art Deep Learning Models for Ontology Construction

Zengeya,

Vincent Fonou-Dombeu

2024

IEEE Access

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ABSRTACT Researchers are working towards automation of ontology construction to manage the evergrowing data on the web. Currently, there is a shift from the use of machine learning techniques towards exploration of deep learning models for ontology construction. Deep learning model are capable of extracting terms, entities, relations, and classifications, and perform axiom learning from the underutilized richness of web-based knowledge. There has been remarkable progress in automatic ontology creation using deep learning models since they can perform word embedding, long-term dependency acquisition, concept extraction from large corpora, and inference of abstracted relationships based on broad corpora. Despite their emerging importance, deep learning models remain underutilized in ontology construction, and there is no comprehensive review of their application in ontology learning. This paper presents a comprehensive review of existing deep-learning models for the construction of ontologies, the strength and the weaknesses presented by the deep learning models for ontology learning as well as promising directions to achieve a more robust deep learning models. The Deep Learning models reviewed include Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), Long-Short Term Memory (LSTMs), and Gated Recurrent Unit (GRU) as well as their ensembles. While these traditional deep learning models have achieved great success, one of their limitations is that they struggle to understand the meaning and order of data in sequences. CNNs and RNN-based models such as LSTMs and GRUs can be computationally expensive due to their large number of parameters or complex gating mechanisms. Furthermore, RNN models suffer from vanishing gradients, making it difficult to learn long-term relationships in sequences. Additionally, RNN-based models process information sequentially, limiting their ability to take advantage of powerful parallel computing hardware, slowing down training and inference, especially for long sequences. Consequently, there has been a shift towards Generative Pre-Trained (GPT) models and Bidirectional Encoder Representations from Transformers (BERT) models. This paper also reviewed the GPT-3, GPT-4, and the BERT models for extracting terms, entities, relations, and classifications. While GPT models excel in contextual understanding and flexibility, they fall short when handling domain-specific terminology and disambiguating complex relationships. Fine-tuning and domain-specific training data could minimize these shortcomings, and further enhance the performance of GPT in term and relation extraction tasks. On the other hand, the BERT models excel in comprehending context-heavy texts, but struggles with higher-level abstraction and inference tasks due to a lack of explicit semantic knowledge, thus necessitating inference for unspecified relationships. The paper recommends further research on deep learning models for ontology alignment and merging. Also, the ensembling of deep learning models and the u...

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Section: Figure 21 Blstm -Cnn [132] [130]mentioning

confidence: 99%

A Review of State of the Art Deep Learning Models for Ontology Construction

Zengeya,

Vincent Fonou-Dombeu

2024

IEEE Access

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“…There are several fuzzy string-matching algorithms for string similarity detection to explain the degree of similarity between pairs of strings. These include, but are not limited to, Jaro-Winkler, Jaccard, Levenshtein, Longest Common Subsequence (LCS), Term Frequency-Inverse Document Frequency (TF-IDF), N-gram, and many more [26,27]. In our proposed algorithm, we have utilized both Jaccard and Jaro-Winkler in the merging process.…”

Section: Fuzzy String-matching Algorithmsmentioning

confidence: 99%

“…Specifically, the Jaro-Winkler algorithm works well in processing axioms expressions that contain multiple tokens/words [26]. The Jaccard similarity coefficient algorithm was originally designed for set theory applications [27] but has also been adapted to assess the similarity between strings. This algorithm assesses the resemblance of two strings by analyzing their individual characters and identifying shared characters to ascertain their degree of similarity.…”

Section: Fuzzy String-matching Algorithmsmentioning

confidence: 99%

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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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“…Consequently, the fuzzy set is a 'vague boundary set' compared with a crisp set. Fuzzy string/text matching is a method used to find strings that match a pattern approximately (rather than exactly) [49]. In other words, fuzzy string matching is a search that tries to find matches even when users misspell/mispronounce words or enter/pronounce only partial words for the search.…”

Section: Fuzzy Set and Fuzzy String Matching Scorementioning

confidence: 99%

A Bidirectional Arabic Sign Language Framework Using Deep Learning and Fuzzy Matching Score

Mosleh,

Assiri,

Gumaei

et al. 2024

Mathematics

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Sign language is widely used to facilitate the communication process between deaf people and their surrounding environment. Sign language, like most other languages, is considered a complex language which cannot be mastered easily. Thus, technology can be used as an assistive tool to solve the difficulties and challenges that deaf people face during interactions with society. In this study, an automatic bidirectional translation framework for Arabic Sign Language (ArSL) is designed to assist both deaf and ordinary people to communicate and express themselves easily. Two main modules were intended to translate Arabic sign images into text by utilizing different transfer learning models and to translate the input text into Arabic sign images. A prototype was implemented based on the proposed framework by using several pre-trained convolutional neural network (CNN)-based deep learning models, including the DenseNet121, ResNet152, MobileNetV2, Xception, InceptionV3, NASNetLarge, VGG19, and VGG16 models. A fuzzy string matching score method, as a novel concept, was employed to translate the input text from ordinary people into appropriate sign language images. The dataset was constructed with specific criteria to obtain 7030 images for 14 classes captured from both deaf and ordinary people locally. The prototype was developed to conduct the experiments on the collected ArSL dataset using the utilized CNN deep learning models. The experimental results were evaluated using standard measurement metrics such as accuracy, precision, recall, and F1-score. The performance and efficiency of the ArSL prototype were assessed using a test set of an 80:20 splitting procedure, obtaining accuracy results from the highest to the lowest rates with average classification time in seconds for each utilized model, including (VGG16, 98.65%, 72.5), (MobileNetV2, 98.51%, 100.19), (VGG19, 98.22%, 77.16), (DenseNet121, 98.15%, 80.44), (Xception, 96.44%, 72.54), (NASNetLarge, 96.23%, 84.96), (InceptionV3, 94.31%, 76.98), and (ResNet152, 47.23%, 98.51). The fuzzy matching score is mathematically validated by computing the distance between the input and associative dictionary words. The study results showed the prototype’s ability to successfully translate Arabic sign images into Arabic text and vice versa, with the highest accuracy. This study proves the ability to develop a robust and efficient real-time bidirectional ArSL translation system using deep learning models and the fuzzy string matching score method.

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Hybridizing Fuzzy String Matching and Machine Learning for Improved Ontology Alignment

Cited by 3 publications

References 52 publications

A Review of State of the Art Deep Learning Models for Ontology Construction

A Review of State of the Art Deep Learning Models for Ontology Construction

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

A Bidirectional Arabic Sign Language Framework Using Deep Learning and Fuzzy Matching Score

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