Semi-automatic Framework for Generating RDF Dataset from Open Data

Krataithong, Pattama; Buranarach, Marut; Hongwarittorrn, Nuttanont; Supnithi, Thepchai

doi:10.1007/978-3-319-70016-8_1

Cited by 1 publication

(3 citation statements)

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“…In contrast to these common approaches, we cover the six different kinds of learning elements that can be appended to a learned ontology. Moreover, apart from the works of Jablonski et al [10] and Krataithong et al [3], where an input is transformed to an ontology on two levels using an intermediate XML data and RDB schema, respectively, a one-level transformation was applied in the remaining approaches. In this work, similar to [10], we propose a two-level transformation, since we support heterogenous formats of input.…”

Section: Comparison and Discussionmentioning

confidence: 99%

“…This conversion is performed using XSLT. Moreover, Krataithong et al [3] proposed a semi-automatic framework that provides a support system for users in transforming TD in spreadsheet format (i.e., CSV or XSL to RDF format). This framework focused on the need for automatic schema detection before starting ontology learning from TD.…”

Section: Ontology Learning Approachesmentioning

confidence: 99%

“…Thus, most works in ontology learning require a minimum expert contribution and insight at runtime. For example, some works start building an ontology semi-automatically from scratch [2,3], requiring expert intervention at runtime. Furthermore, other works start enriching preexisting ontologies from scratch [4,5] based on expert contributions.…”

Section: Introductionmentioning

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: Ontology Learning Approachesmentioning

confidence: 99%