Ontology Learning for Systems Engineering Body of Knowledge

Yang, Lan; Cormican, Kathryn; Yu, Ming

doi:10.1109/tii.2020.2990953

Cited by 26 publications

(14 citation statements)

References 206 publications

(296 reference statements)

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“…In addition, ontology and formalisms are developed for systems engineering. Yang et al [21] provided a unified ontology to describe a systems engineering body of knowledge for the International Council on Systems Engineering [20]. Seidner and Roux [22] proposed a formal method (ontology concepts) for behavior simulation, which is used for safety analyses when implementing systems engineering.…”

Section: Methodsmentioning

confidence: 99%

Design Ontology Supporting Model-Based Systems Engineering Formalisms

Zheng

et al. 2022

IEEE Systems Journal

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Model-based systems engineering (MBSE) provides an important capability for managing the complexities of system development. MBSE empowers the formalism of system architectures for supporting model-based requirement elicitation, specification, design, development, testing, fielding, etc. However, the modeling languages and techniques are heterogeneous, even within the same enterprise system, which leads to difficulties for data interoperability. The discrepancies among data structures and language syntaxes make information exchange among MBSE models more difficult, resulting in considerable information deviations when connecting data flows across the enterprise. Therefore, this article presents an ontology based upon graphs, objects, points, properties, roles, and relationships with extensions (GOPPRRE), providing metamodels that support the various MBSE formalisms across lifecycle stages. In particular, knowledge graph models are developed to support unified model representations to further implement ontological data integration based on GOPPRRE throughout the entire lifecycle. The applicability of the MBSE formalism is verified using quantitative and qualitative approaches. Moreover, the GOPPRRE ontologies are used to create the MBSE formalisms in a domain-specific modeling tool, MetaGraph, for evaluating its availability. The results demonstrate that the proposed ontology supports the formal structures and descriptive logic of the systems engineering lifecycle.

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Section: Methodsmentioning

confidence: 99%

Design Ontology Supporting Model-Based Systems Engineering Formalisms

Zheng

et al. 2022

IEEE Systems Journal

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“…The input data to an ontology learning system might be unstructured (e.g. textbooks) [8], semi-structured (e.g. HTML web pages) [28], or structured (e.g.…”

Section: Related Workmentioning

confidence: 99%

“…relational databases) [29]. The method used for ontology learning can be automatic [7], [8] or it can involve human intervention. Asim et al [13] review the automatic ontology learning approaches and classify the techniques they use into three main categories: (1) linguistic methods [9], based on language characteristics such as part-of-speech-tagging; (2) statistical methods [10], based on probability and frequency measures; and (3) logical methods [11], based on inductive logic programming techniques to derive hypotheses.…”

Section: Related Workmentioning

confidence: 99%

“…The value of the threshold θ w is selected in such a way that the type of semantic relation between two activities is approved only if they have a weighted vote of at least half of the maximum possible vote. A comprehensive list of parameters is available online 8 . The PAO (referred to as the game PAO) is created from the selected semantic relations by a Java program incorporating the OWL API [82] and the HermiT [70] reasoner 9 .…”

Section: B Proceduresmentioning

confidence: 99%

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Process Activity Ontology Learning From Event Logs Through Gamification

et al. 2021

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The quality of event log data is a constraining factor in achieving reliable insights in process mining. Particular quality problems are posed by activity labels which are meant to be representative of organisational activities, but may take different manifestations (e.g. as a result of manual entry synonyms may be introduced). Ideally, such problems are remedied by domain experts, but they are time-poor and data cleaning is a time-consuming and tedious task. Ontologies provide a means to formalise domain knowledge and their use can provide a scalable solution to fixing activity label similarity problems, as they can be extended and reused over time. Existing approaches to activity label quality improvement use manuallygenerated ontologies or ontologies that are too general (e.g. WordNet). Limited attention has been paid to facilitating the development of purposeful ontologies in the field of process mining. This paper is concerned with the creation of activity ontologies by domain experts. For the first time in the field of process mining, their participation is facilitated and motivated through the application of techniques from crowdsourcing and gamification. Evaluation of our approach to the construction of activity ontologies by 35 participants shows that they found the method engaging and that its application results in high-quality ontologies.

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“…We came across several ontologies focusing on a particular kind of instructional design; for example, a mobile learning ontology was designed for abductive science inquiry style of instruction (Ahmed & Parsons, 2013). An ontology for learning scenarios based on collaborative learning theories (Isotani et al, 2013), gamification (Challco et al, 2020) and on subject matter such as word problems in mathematics (Lalingkar, Ramanathan, & Ramani, 2015), software engineering body of knowledge (Abran et al, 2006), systems engineering (Yang, Cormican, & Yu, 2020) were proposed. Recently, an ontology was proposed for nutrition and food literacy skills (Mitsis, Zarkogianni, Bountouni, Athanasiou, & Nikita, 2019).…”

Section: Ontologies For Adult Literacy Instructional Designmentioning

confidence: 99%

An ontology based modeling framework for design of educational technologies

Chimalakonda

Nori

2020

Smart Learn. Environ.

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Despite rapid progress, most of the educational technologies today lack a strong instructional design knowledge basis leading to questionable quality of instruction. In addition, a major challenge is to customize these educational technologies for a wide range of customizable instructional designs. Ontologies are one of the pertinent mechanisms to represent instructional design in the literature. However, existing approaches do not support modeling of flexible instructional designs. To address this problem, in this paper, we propose an ontology based framework for systematic modeling of different aspects of instructional design knowledge based on domain patterns. As part of the framework, we present ontologies for modeling goals, instructional processes and instructional material. We demonstrate the ontology framework by presenting instances of the ontology for the large scale case study of adult literacy in India (287 million learners spread across 22 Indian Languages), which requires creation of hundreds of similar but varied eLearning Systems based on flexible instructional designs. The implemented framework is available at http://rice.iiit.ac.in and is transferred to National Literacy Mission Authority of Government of India. The proposed framework could be potentially used for modeling instructional design knowledge for school education, vocational skills and beyond.

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Ontology Learning for Systems Engineering Body of Knowledge

Cited by 26 publications

References 206 publications

Design Ontology Supporting Model-Based Systems Engineering Formalisms

Design Ontology Supporting Model-Based Systems Engineering Formalisms

Process Activity Ontology Learning From Event Logs Through Gamification

An ontology based modeling framework for design of educational technologies

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