The Key Role of Ontology Alignment and Enrichment Methodologies for Aligning and Enriching Dwipa Ontology with the Weather Concept on the Tourism Domain

Kuntarto, Guson Prasamuarso; Alrin, Yossy; Gunawan, Irwan Prasetya

doi:10.1109/icicos48119.2019.8982437

Cited by 5 publications

(4 citation statements)

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“…In order to analyze each approach in regards with the aforementioned described criteria, we obtain a table as shown in Table 3.4. d [Kuntarto et al, 2019] e [Sbissi et al, 2020] Table 3.4: Tabular comparison of different ontology learning approaches based on the main distinguishing criteria…”

Section: Comparison and Discussionmentioning

confidence: 99%

“…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%

“…This approach uses WordNet as a lexical database and semantic similarity measure to help select the best terms to represent ontology components. Kuntarto et al [Kuntarto et al, 2019] focused on enriching Dwipa Ontology III with weather concepts from two sources of weather ontologies. These new weather concepts are generated by measuring their linguistic similarities.…”

Section: Booshehri and Lukschmentioning

confidence: 99%

“…For the ontology verification activity, we used a feature-based evaluation that is oriented toward evaluating the structural and schema aspects of evolved ontologies to draw conclusions about their structure and schema quality. This evaluation has been adopted by different techniques, such as OntoClean [Guarino and Welty, 2002], OntoQA [Tartir et al, 2005], and OntoMetrics [Lantow, 2016]. For our evolved ontology-based context models, Onto-Metrics was chosen as a feature-based evaluation framework.…”

Section: Feature-based Evaluationmentioning

confidence: 99%

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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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Section: Comparison and Discussionmentioning

confidence: 99%