Automatic Ontology Learning from Heterogeneous Relational Databases: Application in Alimentation Risks Field

Aggoune, Aicha

doi:10.1007/978-3-319-89743-1_18

Cited by 9 publications

(9 citation statements)

References 13 publications

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“…Based on the aforementioned criteria, we classified the ontology learning approaches as shown in Table 1. The comparative table shows that a large number of approaches, such as those in [4,5,[10][11][12][13][14][15][16], have a single input format. For example, Volker and Niepert [11] built an ontology from LOD (DBpedia).…”

Section: Comparison and Discussionmentioning

confidence: 99%

“…In addition, almost all approaches aimed to return one or a few specific learning elements. For example, Booshehri and Luksch [5] extracted only non-taxonomic relations, while Aggoune [15] included four learning elements such as concepts, non-taxonomic relations, datatype properties, and individuals. In contrast to these common approaches, we cover the six different kinds of learning elements that can be appended to a learned ontology.…”

Section: Comparison and Discussionmentioning

confidence: 99%

“…This follows previous works in the field of ontology learning from text with the difference that it also uses the knowledge scattered in DBpedia to improve the ontology learning output. Moreover, Aggoune [15] introduced a semantic approach for automatic ontology learning from many heterogeneous RDBs in order to facilitate their integration. This approach uses WordNet as a lexical database and a semantic similarity measure to help select the best terms to represent the ontology components.…”

Section: Ontology Learning Approachesmentioning

confidence: 99%

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Automatic Ontology-Based Model Evolution for Learning Changes in Dynamic Environments

et al. 2021

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Knowledge engineering relies on ontologies, since they provide formal descriptions of real-world knowledge. However, ontology development is still a nontrivial task. From the view of knowledge engineering, ontology learning is helpful in generating ontologies semi-automatically or automatically from scratch. It not only improves the efficiency of the ontology development process but also has been recognized as an interesting approach for extending preexisting ontologies with new knowledge discovered from heterogenous forms of input data. Driven by the great potential of ontology learning, we present an automatic ontology-based model evolution approach to account for highly dynamic environments at runtime. This approach can extend initial models expressed as ontologies to cope with rapid changes encountered in surrounding dynamic environments at runtime. The main contribution of our presented approach is that it analyzes heterogeneous semi-structured input data for learning an ontology, and it makes use of the learned ontology to extend an initial ontology-based model. Within this approach, we aim to automatically evolve an initial ontology-based model through the ontology learning approach. Therefore, this approach is illustrated using a proof-of-concept implementation that demonstrates the ontology-based model evolution at runtime. Finally, a threefold evaluation process of this approach is carried out to assess the quality of the evolved ontology-based models. First, we consider a feature-based evaluation for evaluating the structure and schema of the evolved models. Second, we adopt a criteria-based evaluation to assess the content of the evolved models. Finally, we perform an expert-based evaluation to assess an initial and evolved models’ coverage from an expert’s point of view. The experimental results reveal that the quality of the evolved models is relevant in considering the changes observed in the surrounding dynamic environments at runtime.

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Section: Comparison and Discussionmentioning

confidence: 99%

Section: Comparison and Discussionmentioning

confidence: 99%

Section: Ontology Learning Approachesmentioning

confidence: 99%

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Automatic Ontology-Based Model Evolution for Learning Changes in Dynamic Environments

et al. 2021

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“…Recently in [16], the authors used machine learning techniques. They proposed a semantic approach to automatically generate ontology from heterogeneous relational databases.…”

Section: Related Workmentioning

confidence: 99%

Using Reverse Engineering for Building Ontologies with Deeper Taxonomies from Relational Databases

Sbai¹,

Louhdi²,

Behja³

et al. 2019

JSW

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The relational model is characterized by its high quality and has been widely used by information systems. However, unlike the conceptual model, the relational model is semantically poor since it doesn't enable the representation of inheritance. In this paper, we present an algorithmic approach to extract generalization/ specialization inheritance hierarchies. We perform a reverse engineering by analyzing stored data. Finally, we evaluated our approach by conducting several experiments on relational databases. The results were satisfying in terms of recovering the tables lost during the transformation from the entity relationship model to the relational model.

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“…However, the automatic learning is a difficult task that has several important limitations with respect to the thesis objectives and research questions as pointed out in Table 3.4 First, the type of input requirements varies from one approach to another. Some approaches use unstructured texts as their input [Booshehri andLuksch, 2015, Sbissi et al, 2020], while others are concerned with structured data [Aggoune, 2018, Kuntarto et al, 2019. However, we notice that all reviewed approaches [Yao et al, 2014, Booshehri and Luksch, 2015, Aggoune, 2018, Sbissi et al, 2020] have a single input format.…”

Section: Comparison and Discussionmentioning

confidence: 99%

Towards an Approach for Intelligent Adaptation Decision-Making of Pervasive Middleware

Jabla¹

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This thesis describes research to gain insight into pervasive middleware solutions and context-aware solutions that expand their perspective from static to dynamic pervasive environments. The motivation behind this research arose from a need to reconsider and replace today's context-aware solutions with more intelligent solutions to account for dynamic environments and users' preferences changes at runtime. In this context, the end goal is to focus on offering intelligent context-aware solutions that could deal with the automatic context model evolution and new decisions generation according to context changes at runtime. To do so, in the current thesis, we illustrate a hybrid approach termed IConAS -a means of combining the practical advantages of context evolution with the decision-making adaptation. This combination leads to intelligent context-aware solutions that could reflect changes occurring in their surrounding dynamic environments at runtime. The thesis concentrates on the three important contributions as follows:• Definition of the IConAS approach that combines two main approaches. This hybrid approach aims to offer intelligent context-aware solutions through augmenting an existing middleware. The purpose of this augmentation is to support runtime and automatic context evolution and decision-making adaptation in order to reflect changes in dynamic environments;Next, I would like to extend my heartiest gratitude and thanks to my co-supervisor Prof. Maha Khemaja, who believed in me from the beginning of this thesis and who never ceased to encourage, guide and kindly nudge me forward when I doubt my ability or could not see the finish line. This thesis would not have been achievable without her constant feedback, her enormous research contributions and her emotional support. I am sincerely thankful to my external evaluators of the thesis, Prof.

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Automatic Ontology Learning from Heterogeneous Relational Databases: Application in Alimentation Risks Field

Cited by 9 publications

References 13 publications

Automatic Ontology-Based Model Evolution for Learning Changes in Dynamic Environments

Automatic Ontology-Based Model Evolution for Learning Changes in Dynamic Environments

Using Reverse Engineering for Building Ontologies with Deeper Taxonomies from Relational Databases

Towards an Approach for Intelligent Adaptation Decision-Making of Pervasive Middleware

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