Application of ontological analysis for metadata processing in the interpretation of BIG DATA at the semantic level

Rogushina, J.V.; Gladun, Anatoly

doi:10.15407/pp2020.04.055

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…This system allows anybody to define or iterate on an ontology or its sub-elements. This OWA framework is particularly useful for developing big-data insights (Rogushina and Gladun, 2020). As an example, turbine Supervisory Control and Data Acquisition (SCADA) data are complex, involving hundreds of thousands of data variables, where the same metadata vocabulary can have different meanings between turbine models.…”

Section: Common Representation: Standardmentioning

confidence: 99%

Knowledge engineering for wind energy

Marykovskiy,

Clark,

Day

et al. 2024

Wind Energ. Sci.

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Abstract. With the rapid evolution of the wind energy sector, there is an ever-increasing need to create value from the vast amounts of data made available both from within the domain and from other sectors. This article addresses the challenges faced by wind energy domain experts in converting data into domain knowledge, connecting and integrating them with other sources of knowledge, and making them available for use in next-generation artificial intelligence systems. To this end, this article highlights the role that knowledge engineering can play in the digital transformation of the wind energy sector. It presents the main concepts underpinning knowledge-based systems and summarises previous work in the areas of knowledge engineering and knowledge representation in a manner that is relevant and accessible to wind energy domain experts. A systematic analysis of the current state of the art on knowledge engineering in the wind energy domain is performed with available tools put into perspective by establishing the main domain actors and their needs, as well as identifying key problematic areas. Finally, recommendations for further development and improvement are provided.

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Section: Common Representation: Standardmentioning

confidence: 99%

Knowledge engineering for wind energy

Marykovskiy,

Clark,

Day

et al. 2024

Wind Energ. Sci.

View full text Add to dashboard Cite

show abstract

“…Передбачається, що запропонований підхід може бути інтегровано із методами семантичної обробки метаданих Big Data з метою пошуку в них інформації щодо потрібних користувачам СІО [31].…”

Section: висновки та перспективи подальшої роботиunclassified

The use of ontological knowledge for multi-criteria comparison of complex information objects

Rogushina

Gladun

2022

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In this work we consider comparison of complex information objects (CIO) as a component of intelligent decision-making. The specifics of proposed approach is that we compare not all theoretically possible CIOs but only their subset that is relevant for cur- rent situation and contains existing and available objects. Thus, we find an acceptable solution from the set of available ones that can be not optimal (according to certain criteria). We propose formal ontology-based model of CIO that considered as an element of intellectual information system. This model (in contrast to the domain ontology) defines unique names for positions of classes and class individuals to indicate the SIO structure. The methods of CIO comparison based on the use of knowledge from the relevant do- main ontology are considered. Various approaches to determining of semantic proximity and semantic similarity are considered as metrics for quantitative evaluation to select parameters of information objects that can be used for calculation of these evaluations. We propose an algorithm for semantic comparison of CIOs which are based on the same ontology and have a similar structure. This algorithm allows generation of comparison criteria and determining hierarchy of this criteria for the current situation. We propose to evaluate the semantic-level similarity of the elements of individual CIOs to certain reference CIO defined by the user (as a description of the optimal solution or generated CIO properties). As a result, a subset of CIOs that satisfy the user requirements is cre- ated, but we have to select only one CIO among them, which will be used in the future to fulfill the user task. Therefore, we need in a set of criteria for CIO comparison and methods to determine the importance of each of these criteria at the current moment in time. For this purpose, we propose to use the method of hierarchical analysis based on a pairwise comparison of the importance of individual criteria.

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Application of ontological analysis for metadata processing in the interpretation of BIG DATA at the semantic level

Cited by 2 publications

References 18 publications

Knowledge engineering for wind energy

Knowledge engineering for wind energy

The use of ontological knowledge for multi-criteria comparison of complex information objects

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