Metadata management and application

Satija, M.P.; Bagchi, Mayukh; Martínez-Ávila, Daniel

doi:10.5958/0976-2469.2020.00030.2

Cited by 10 publications

(4 citation statements)

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“…A schema "(also called metadata model) refers to a high-level, annotation model used for capturing descriptive information about varied facets of an information resource, facilitating the broader objective of achieving a unified understanding of the semantics of the data." [12]. It can be generic or domain-specific, modeling data structures of specialized domains.…”

Section: Metadata and The Data Catalogue Domainmentioning

confidence: 99%

Ontology-Driven Multi-Level Conceptual Modeling for Dataset and Distributions Descriptions

Borges,

Prata,

Campos

2023

Frontiers in Artificial Intelligence and Applications

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This paper presents an improvement proposal for an ontology-driven multi-level conceptual model for the data catalogue domain. Data catalogues gather metadata that describe resources in different and heterogeneous digital platforms (repositories). They are supported by Information Systems (IS) that use these descriptors to provide visibility and support resources exploration and analysis. Domain ontologies are essential to promote quality ISs, as they are developed to reflect the intended reality. The proposed conceptual model is well-founded on the Unified Foundational Ontology and the Multi-Level Theory, based on the widely used DCAT vocabulary, a standardized metadata schema for describing datasets and data services. The resulting model addresses ambiguities and contemplates high-level types contributing to the conformance of domain concepts and relationships. In addition, they provide knowledge about the different types of resource descriptors and relationships contained in a specific catalogue, favoring its management. The paper enhances the previous model by extending it to handle descriptors representing a dataset according to the data equivalence across multiple distributions. We also demonstrate the model by describing a dataset with no data equivalence in its distributions, taken from a real-world scenario, thus providing a structured representation to manage metadata sets in the data catalogue domain.

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Section: Metadata and The Data Catalogue Domainmentioning

confidence: 99%

Ontology-Driven Multi-Level Conceptual Modeling for Dataset and Distributions Descriptions

Borges,

Prata,

Campos

2023

Frontiers in Artificial Intelligence and Applications

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“…As metadata identified as application, Fleischer & Combelles (2019) are presented the realization of metadata concepts through ontologies in a business application. According to Satija et al (2020), the ideation of application-specific metadata application profiles (APs) is based on the principles of functionality, modularity and agile data management. On top of that, by Ravi (2017) stated that the metadata application should be a major component of the digital information processing.…”

Section: Metadata As Applicationmentioning

confidence: 99%

Escalating the Importance of Preserving the Electronic Records Metadata in Online Business Transaction Activities

Bunawan¹

2021

IJARBSS

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This paper highlighted the importance of preserving the electronic records metadata in online business activities. Due to the COVID-19, all types of businesses are affected and need to change their process of selling the product and services to customers. As the COVID-19 restricted face-to-face transaction or physical attendance activities, the sellers and owners of business choose to conduct their business using online platform. By conducting the business online, the process of providing the evidence of transaction is required. This is where the use of electronic records as evidence of the transaction is applied. In providing the evidence, the preservation of entire metadata of electronic records is also required and becomes the importance aspect in online business. As to identify the importance, the analysis on the literatures is conducted. By using the scoping review technique, the details of the current business process activities and identification of five (5) type of metadata function are presented. The metadata function includes identifying metadata as evidence, asset, tool, application and repository. Last but not least, this paper concludes that the entire metadata function presented has becomes the key aspect in escalating the importance of preserving the electronic records metadata in online business transaction activities.

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“…Next, let us discuss the issue of metadata management [26] as the formal foundation for explainable KOEs which opens up for consideration a novel ethical cardinal in sync with explainable AI (XAI) [27]. Explainability is crucial to modern AI systems, including knowledge-based ones, as they "aim to make AI system results more understandable to humans" [27], and accordingly, a novel (non-trivial ) proposal is to consider metadata as the pivot which facilitates machine explainability in KOEs.…”

Section: Metadata For Explainabilitymentioning

confidence: 99%

Towards Knowledge Organization Ecosystems

Bagchi¹

2021

Preprint

Self Cite

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It is needless to mention the (already established) overarching importance of knowledge organization and its tried-and-tested high quality schemes in knowledge based Artificial Intelligence (AI) systems. But equally, it is also hard to ignore that, increasingly, standalone KOSs are becoming functionally ineffective components for such systems, given their inability to capture the continuous facetization and drift of domains. The paper proposes a radical re-conceptualization of KOSs as a first step to solve such an inability, and, accordingly, contributes in the form of the following dimensions: (i) an explicit characterization of Knowledge Organization Ecosystems (KOEs) (possibly for the first time) and their positioning as pivotal components in realizing sustainable knowledge-based AI solutions, (ii) as a consequence of such a novel characterization, a first examination and characterization of KOEs as Socio-Technical Systems (STSs), thus opening up an entirely new stream of research in knowledge-based AI, and (iii) motivating KOEs not to be mere STSs but STSs which are grounded in Ethics and Responsible Artificial Intelligence cardinals from their very genesis. The paper grounds the above contributions in relevant research literature in a distributed fashion throughout the paper, and finally concludes by outlining the future research possibilities.

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Metadata management and application

Cited by 10 publications

References 0 publications

Ontology-Driven Multi-Level Conceptual Modeling for Dataset and Distributions Descriptions

Ontology-Driven Multi-Level Conceptual Modeling for Dataset and Distributions Descriptions

Escalating the Importance of Preserving the Electronic Records Metadata in Online Business Transaction Activities

Towards Knowledge Organization Ecosystems

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