Improving Ontologies through Ontology Learning: a University Case

Gil, Richard; Borges, Ana Maria Fernandes; Contreras, Leonardo; Martín-Bautista, María J.

doi:10.1109/csie.2009.957

Cited by 5 publications

(3 citation statements)

References 16 publications

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“…SPARQL and DL queries are used to retrieve the direct and inferred data from the ontology. In [21], the authors described a meta-model design and relevant ontology.…”

Section: Related Workmentioning

confidence: 99%

Ontology-Based Smart System to Automate Higher Education Activities

et al. 2021

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The need for smart e-learning environments is resulting in new challenges for researchers and practitioners to develop intelligent systems that can be used to automate the Higher Education (HE) activities in an intelligent way. Some common examples of such activities are “analyzing, finding, and ranking the right resource to teach a course,” “analyzing and finding the people with common research interests to start joint research projects,” and “using data analytics and machine reasoning techniques for conducting the exams with different levels of complexities.” Ontological reasoning and smart data analytics can play an important role in analyzing and automating these HE activities and processes. In this paper, we present a framework named as Higher Education Activities and Processes Automation Framework (HEAPAF). The HEAPAF framework can be used to identify, extract, process, and produce the semantically enriched data in machine understandable format from different educational resources. We also present the Higher Education Ontology (HEO) that we designed and developed to accommodate the HE data and then to perform analysis and reasoning on it. As a proof of concept, we present a case study on the topic, “analyzing, finding, and ranking the right resources to teach a course,” which can dramatically improve the learning patterns of students in the growing smart educational environment. Finally, we provide the evaluation of our framework as evidence of its competency and consistency in improving academic analytics for educational activities and processes by using machine reasoning.

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“…SPARQL and DL queries are used to retrieve the direct and inferred data from the ontology. In [21], the authors described a meta-model design and relevant ontology.…”

Section: Related Workmentioning

confidence: 99%

Ontology-Based Smart System to Automate Higher Education Activities

et al. 2021

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“…A fuzzy ontology-based framework has been presented in [28] to facilitate the organization of scientific research. In [29], the authors designed and presented a meta-model ontology. The work explains the methodology developed for ontological improvement by applying a semi-supervised learning method.…”

Section: Related Workmentioning

confidence: 99%

Find a Research Collaborator: An Ontology-Based Solution to Find the Right Resources for Research Collaboration

Alrehaili,

Aslam,

Alharbi

et al. 2024

ijacsa

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Researchers in Higher Education (HE) institutions/academia and in industry are continuously engaged in generating new solutions and products for existing and emergent problems. Doing quality research and producing better scientific results depend greatly on solid research teams and scientific collaborators. Research output in HE institutions and industry can be optimized with appropriate resources in research teams and collaborations with suitable research partners. The main challenge in finding suitable resources for joint research projects and scientific collaborations pertains to the availability of data and metadata of researchers and their scientific work in traditional formats, for instance, websites, portals, documents, and traditional databases. However, these traditional data sources do not support intelligent and smart ways of finding and querying the right resources for joint research and scientific collaboration. A possible solution resides in the deployment of Semantic Web (SW) techniques and technologies for representing researcher and their research contribution data in a machine-understandable format, thus ultimately proving useful for smart and intelligent query-answering purposes. In pursuit of this, we present a general Methodology for Ontology Design and Development (MODD). We also describe the use of this methodology to design and develop Higher Education Ontology (HEO). This HEO can be used to automate various activities and processes in HE. In addition, we describe the use and adoption of the HEO through a case study on the topic of "finding the right resources for joint research and scientific collaboration". Finally, we provide an analysis and evaluation of our methodology for posing smart queries and evaluating the results based on machine reasoning.

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“…A semi-supervised learning agent for text-mining was developed (using the Rapid-i tool [67]) to enhance the OL. Then, some professors, as expertOLeKSS Users, used the tool GATE to update the host ontology [68,69]. Process 4: Relevant knowledge about the professors' profile subdomains from a Relational Database (RDB) of another university were obtained [70].…”

Section: Olekss Processesmentioning

confidence: 99%