Theses and Dissertations (Information Communication Technology)

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Browsing Theses and Dissertations (Information Communication Technology) by Author "Fonou-Dombeu, J. V., Dr."

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    Analysis and application of semantic web mechanism for storing and querying ontologies
    (Vaal University of Technology, 2017-02) Kwuimi, Raoul; Fonou-Dombeu, J. V., Dr.
    Since the introduction of Semantic Web in the early 2000, storing and querying ontologies have been a subject of greater research. Thus, several types of storage media and mechanisms have been developed to increase storage and query speed and efficiency of ontologies in Semantic Web applications. Existing Semantic Web mechanisms for storing and querying ontologies are implemented on several storage media and support different languages. However, there is a shortage of studies that provide an empirical analysis and application of these ontology storage and query mechanisms in the Semantic Web domain. This study conducted an analysis and application of the Semantic Web mechanisms for storing and querying ontologies. A thorough literature review was carried out to identify relevant publications pertaining to existing Semantic Web mechanisms for storing and querying ontologies as well as the platforms and storage media for implementing these mechanisms. Thereafter, the Design research method was used consisting of a set of predefined steps, namely, awareness, suggestion, development, evaluation, and conclusion. The awareness stage identified the need for an architecture to test several ontology storage media and mechanisms. In the suggestion stage a framework was proposed to empirically analyse and evaluate existing ontology storage and query mechanisms. The required Semantic Web platforms were identified to implement the framework in the development stage. The evaluation stage used a set of metrics to evaluate the framework including: the loading times of ontologies, the disc space used to store the ontology repositories and the mean and variance of query response times. Further, the evaluation stage analysed and discussed the storage mechanisms implemented in Semantic Web platforms. Finally, the outcome of the performance of the framework is presented in the conclusion stage. The framework was practically tested with six ontologies of different formats and sizes on two popular Semantic Web platforms, namely, Sesame and Jena API and the ontology storage and query mechanisms were analysed and compared. Although the underlying structures of repositories in the in-memory and native files in Jena and Sesame could not be accessed, it was possible to access and analyse the data in the repositories in the relational database storage in both Sesame and Jena. The results showed that Sesame relational uses a combination of mechanisms such as normalized triples store in combination with vertical partitioning. That combination allows Sesame to store ontologies based on their contents; in other words, each ontology has a different database schema in Sesame. Jena on the other hand, uses only a normalized triple store mechanism, also known as generic schema mechanism to store ontologies; thus, all ontologies in Jena have the same database schema. The study would be useful to the Semantic Web and Computer Science communities as it does not only provide theoretical knowledge but also the empirical findings that may serve as a base for further development of ontology storage media and mechanisms.
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    Comparative study of open source and dot NET environments for ontology development.
    (Vaal University of Technology, 2020-05) Mahoro, Leki Jovial; Moyo, Sihle; Fonou-Dombeu, J. V., Dr.
    Many studies have evaluated and compared the existing open-sources Semantic Web platforms for ontologies development. However, none of these studies have included the dot NET-based semantic web platforms in the empirical investigations. This study conducted a comparative analysis of open-source and dot NET-based semantic web platforms for ontologies development. Two popular dot NET-based semantic web platforms, namely, SemWeb.NET and dotNetRDF were analyzed and compared against open-source environments including Jena Application Programming Interface (API), Protégé and RDF4J also known as Sesame Software Development Kit (SDK). Various metrics such as storage mode, query support, consistency checking, interoperability with other tools, and many more were used to compare two categories of platforms. Five ontologies of different sizes are used in the experiments. The experimental results showed that the open-source platforms provide more facilities for creating, storing and processing ontologies compared to the dot NET-based tools. Furthermore, the experiments revealed that Protégé and RDF4J open-source and dotNetRDF platforms provide both graphical user interface (GUI) and command line interface for ontologies processing, whereas, Jena open-source and SemWeb.NET are command line platforms. Moreover, the results showed that the open-source platforms are capable of processing multiple ontologies’ files formats including Resource Description Framework (RDF) and Ontology Web Language (OWL) formats, whereas, the dot NET-based tools only process RDF ontologies. Finally, the experiment results indicate that the dot NET-based platforms have limited memory size as they failed to load and query large ontologies compared to open-source environments.
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    Investigating the Use of Linked Data Technology to Improved Data Access in South African Municipalities
    (Vaal University of Technology, 2022-08-30) Ovono, Gerald; Moyo, S.; Fonou-Dombeu, J. V., Dr.
    Background: In recent years, the expansive growth of technologies has similarly increased the amount of information stored. This has been the case in local government areas that by nature involve various entities with the need to exchange and reuse information. Furthermore, as eGovernment initiatives are being realised in municipalities, researchers and academics are starting to standardise the Linked Data technology to foster the necessary reuse of information. In South Africa, municipalities are the closest point of service delivery to the communities. Ontology modelling makes possible the description of municipalities' knowledge domains in computer processing. In view of standardizing and achieving South African municipalities’ services interoperability, this research study shows great interest. The study started by reviewing the literature on semantic web technology and Linked Data (LD) technology in the field of government and open government. The review has led to the development of a framework for the municipalities' ontology model. Methodology: The municipalities’ ontology model has been conceptualised and evaluated by a set of Competency Questions (CQs) translated into SPARQL queries through experimentation. The experiment's first phase was to perform a goal modelling to capture the CQs from the municipalities’ General Inquiries (GI) and then construct the SPARQL queries. Tropos methodology and CQs Translation (CQT) approach have been used in that phase. Four CQs categories were identified such as Boolean questions, factual questions, list questions, and complex questions. The second phase was to conceptualize the municipalities' ontology model in Web Ontology Language (OWL) supported by Protégé ontology management tools. The third phase was to evaluate the municipality ontology model. The ontology validation was performed from the use of the Pellet reasoner plugin of Protégé to match the ontology design principles, while the evaluation was to execute the SPARQL queries against the municipality ontology model using protégé. Results and discussion: The resulting classes, terms and instances from the SPARQL queries were presented to participants to be rated on a scale of 1 to 5 on how the CQs have been answered. The analysis of the participants' responses indicated 72% weighted mean in Boolean questions, 96% weighted mean in factual questions, 56% weighted mean in list questions and 84% weighted mean in complex questions, Conclusions: The participants’ responses selection rates analysis has demonstrated that the conceptualised municipalities’ ontology model evaluation resulted in positive participants’ selected percentage weighted means values. Hence, Linked Data technology improves data access in South African municipalities.
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    Investigation and application of artificial intelligence algorithms for complexity metrics based classification of semantic web ontologies
    (Vaal University of Technology, 2019-11) Koech, Gideon Kiprotich; Fonou-Dombeu, J. V., Dr.
    The increasing demand for knowledge representation and exchange on the semantic web has resulted in an increase in both the number and size of ontologies. This increased features in ontologies has made them more complex and in turn difficult to select, reuse and maintain them. Several ontology evaluations and ranking tools have been proposed recently. Such evaluation tools provide a metrics suite that evaluates the content of an ontology by analysing their schemas and instances. The presence of ontology metric suites may enable classification techniques in placing the ontologies in various categories or classes. Machine Learning algorithms mostly based on statistical methods used in classification of data makes them the perfect tools to be used in performing classification of ontologies. In this study, popular Machine Learning algorithms including K-Nearest Neighbors, Support Vector Machines, Decision Trees, Random Forest, Naïve Bayes, Linear Regression and Logistic Regression were used in the classification of ontologies based on their complexity metrics. A total of 200 biomedical ontologies were downloaded from the Bio Portal repository. Ontology metrics were then generated using the OntoMetrics tool, an online ontology evaluation platform. These metrics constituted the dataset used in the implementation of the machine learning algorithms. The results obtained were evaluated with performance evaluation techniques, namely, precision, recall, F-Measure Score and Receiver Operating Characteristic (ROC) curves. The Overall accuracy scores for K-Nearest Neighbors, Support Vector Machines, Decision Trees, Random Forest, Naïve Bayes, Logistic Regression and Linear Regression algorithms were 66.67%, 65%, 98%, 99.29%, 74%, 64.67%, and 57%, respectively. From these scores, Decision Trees and Random Forests algorithms were the best performing and can be attributed to the ability to handle multiclass classifications.