A review of data mining ontologies

Sinha, Prasanta Kumar; Gajbe, Sagar Bhimrao; Debnath, Sourav; Sahoo, Subhranshubhusan; Chakraborty, Kanu; Mahato, Shiva Shankar

doi:10.1108/dta-04-2021-0106

Cited by 4 publications

(3 citation statements)

References 62 publications

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“…Thus, this paper takes ontology as an effective method to construct a knowledge system of finite element analysis, such as the reasoning process of the meshing method and element types based on the software Protégé. Ontology is a computer-based knowledge representation method, which allows users to define the concept structure and the relationship between concepts themselves and classify these concepts and relationships through the modeling metalanguage [24]. It not only ensures the compatibility and flexibility of the concept definition but also ensures the formal expression of the concept, making consistency check, knowledge reuse, and knowledge sharing possible [25,26].…”

Section: Information Extraction Based On Openbim and Intelligent Reas...mentioning

confidence: 99%

An Automatic Generation Method of Finite Element Model Based on BIM and Ontology

et al. 2022

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For the mechanical analysis work in the structural design phase, data conversion and information transfer between BIM model and finite element model have become the main factors limiting its efficiency and quality, with the development of BIM (building information modeling) technology application in the whole life cycle. The combined application of BIM and ontology technology has promoted the automation of compliance checking, cost management, green building evaluation, and many other fields. Based on OpenBIM, this study combines IFC (Industry Foundation Classes) and the ontology system and proposes an automatic generation method for converting BIM to the finite element model. Firstly, the elements contained in the finite element model are generalized and the information set requirement, to be extracted or inferred from BIM for the generation of the finite element model, is obtained accordingly. Secondly, the information extraction technical route is constructed to satisfy the acquisition of the information set, including three main aspects, i.e., IFC-based material information, spatial information, and other basic information; ontology-based finite element cell selection method; and APDL statement generation methods based on JAVA, C#, etc. Finally, a complete technical route and a software architecture, designed for converting BIM to the finite element model, are derived. To assess the feasibility of the method, a simple structure is tested in this paper, and the result indicates that the automatic decision-making reasoning mechanism of constructing element type and meshing method can be explored by ontology and IFC. This study contributes to the body of knowledge by providing an efficient method for automatic generation of the BIM structure model and a reference for future applications using BIM in structural analysis.

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Section: Information Extraction Based On Openbim and Intelligent Reas...mentioning

confidence: 99%

An Automatic Generation Method of Finite Element Model Based on BIM and Ontology

et al. 2022

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“…We follow an "explanation-by-exploration" approach [47], which enables users to obtain an abstract view of the journey of data through an AI workflow and then dive into the code corresponding to those steps. Fortunately, the Semantic Web community has developed a variety of knowledge representation formalisms to capture fundamental elements of data science workflows, such as provenance, data flows, and high-level activities [33,45,52,62]. That being said, data science workflows are complex and, although models and techniques for representing code as a data graphs exist [2,18], generating high-level, compact representations automatically is still an open and challenging problem.…”

Section: Introductionmentioning

confidence: 99%

Data journeys: Explaining AI workflows through abstraction

Daga

Groth

2023

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Artificial intelligence systems are not simply built on a single dataset or trained model. Instead, they are made by complex data science workflows involving multiple datasets, models, preparation scripts, and algorithms. Given this complexity, in order to understand these AI systems, we need to provide explanations of their functioning at higher levels of abstraction. To tackle this problem, we focus on the extraction and representation of data journeys from these workflows. A data journey is a multi-layered semantic representation of data processing activity linked to data science code and assets. We propose an ontology to capture the essential elements of a data journey and an approach to extract such data journeys. Using a corpus of Python notebooks from Kaggle, we show that we are able to capture high-level semantic data flow that is more compact than using the code structure itself. Furthermore, we show that introducing an intermediate knowledge graph representation outperforms models that rely only on the code itself. Finally, we report on a user survey to reflect on the challenges and opportunities presented by computational data journeys for explainable AI.

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“…It is a multidisciplinary and multiskilled pursuit (Dutta and Sinha, 2018). Literature evidently reveals that even within a single domain of interest like data mining, COVID-19, tourism and medical care various ontologies are being developed to satisfy different objectives and address multiple research questions (Sinha et al , 2021). Thus, in recent times, there is an enormous expansion of literature available on ontology in distinct dimensions, such as foundations, ontology classification, design process, methodology, principles, evaluation techniques, visualization and design tools.…”

Section: Introductionmentioning

confidence: 99%

Ranking the ontology development methodologies using the weighted decision matrix

Sinha

Dutta

Varadarajan

2022

DTA

Self Cite

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PurposeThe current work provides a framework for the ranking of ontology development methodologies (ODMs).Design/methodology/approachThe framework is a step-by-step approach reinforced by an array of ranking features and a quantitative tool, weighted decision matrix. An extensive literature investigation revealed a set of aspects that regulate ODMs. The aspects and existing state-of-the-art estimates facilitated in extracting the features. To determine weight to each of the features, an online survey was implemented to secure evidence from the Semantic Web community. To demonstrate the framework, the authors perform a pilot study, where a collection of domain ODMs, reported in 2000–2019, is used.FindingsState-of-the-art research revealed that ODMs have been accumulated, surveyed and assessed to prescribe the best probable ODM for ontology development. But none of the prevailing studies provide a ranking mechanism for ODMs. The recommended framework overcomes this limitation and gives a systematic and uniform way of ranking the ODMs. The pilot study yielded NeOn as the top-ranked ODM in the recent two decades.Originality/valueThere is no work in the literature that has investigated ranking the ODMs. Hence, this is a first of its kind work in the area of ODM research. The framework supports identifying the topmost ODMs from the literature possessing a substantial amount of features for ontology development. It also enables the selection of the best possible ODM for the ontology development.

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A review of data mining ontologies

Cited by 4 publications

References 62 publications

An Automatic Generation Method of Finite Element Model Based on BIM and Ontology

An Automatic Generation Method of Finite Element Model Based on BIM and Ontology

Data journeys: Explaining AI workflows through abstraction

Ranking the ontology development methodologies using the weighted decision matrix

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