Search (9 results, page 1 of 1)

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Abstract

This research explores current controversies within country dance communities and the implications of cultural and ethical issues related to representation of gender and race in a KOS for an ICH, while investigating the importance of context and the applicability of semantic approaches in the implementation of synonym rings. During development of a controlled vocabulary to represent dance concepts for country dance choreography, this study encountered and considered the importance of history and culture regarding synonymous and near-synonymous terms used to describe dance roles and choreographic elements. A subset of names for the same choreographic concepts across four subdomains of country dance (English country dance, Scottish country dance, contra dance, and modern western square dance) were used as a case study. These concepts included traditionally gendered dance roles and choreographic terms with a racially pejorative history. Through the lens of existing research on ethical knowl­edge organization, this study focused on principles and methods of transparency, multivocality, cultural warrant, cultural hospitality, and intersectionality to conduct a domain analysis of country dance resources. The analysis revealed differing levels of engagement and distinction among dance practitioners and communities for their preferences to use different terms for the same concept. Various lexical, grammatical, affective, social, political, and cultural aspects also emerged as important contextual factors for the use and assignment of terms. As a result, this study proposes the use of semantic annotation to represent those contextual factors and to allow mechanisms of user choice in the design of a country dance knowl­edge organization system. Future research arising from this study would focus on expanding examination to other country dance genres and continued exploration of the use of semantic approaches to represent contextual factors in controlled vocabulary development.

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Abstract

Purpose The purpose of this paper is to introduce a multi-level framework for semantic modeling (MFSM) based on four signification levels: objects, classes of entities, instances and domains. In addition, four fundamental propositions of the signification process underpin these levels, namely, classification, decomposition, instantiation and contextualization. Design/methodology/approach The deductive approach guided the design of this modeling framework. The authors empirically validated the MFSM in two ways. First, the authors identified the signification processes used in articles that deal with semantic modeling. The authors then applied the MFSM to model the semantic context of the literature about lean manufacturing, a field of management science. Findings The MFSM presents a highly consistent approach about the signification process, integrates the semantic modeling literature in a new and comprehensive view; and permits the modeling of any semantic context, thus facilitating the development of knowledge organization systems based on semantic search. Research limitations/implications The use of MFSM is manual and, thus, requires a considerable effort of the team that decides to model a semantic context. In this paper, the modeling was generated by specialists, and in the future should be applicated to lay users. Practical implications The MFSM opens up avenues to a new form of classification of documents, as well as for the development of tools based on the semantic search, and to investigate how users do their searches. Social implications The MFSM can be used to model archives semantically in public or private settings. In future, it can be incorporated to search engines for more efficient searches of users. Originality/value The MFSM provides a new and comprehensive approach about the elementary levels and activities in the process of signification. In addition, this new framework presents a new form to model semantically any context classifying its objects.

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Abstract

A comprehensive set of evaluation criteria, named OnE, for evaluating ontologies has been proposed in this paper. Each criterion of OnE has been defined in a way such that together they are capable of evaluating any ontology from all aspects. The process of using OnE for evaluation has been demonstrated by evaluating chemical ontologies. Also, for this purpose, an ontology on the domain of agricultural chemicals has been constructed by following the human-centric faceted approach for ontology construction (HCFOC) and has been evaluated using OnE. The results obtained after the evaluation has provided insights about the ontologies. The constructed ontology aims to support any information system trying to support farmers in the process of decision making while selecting chemicals for use in agriculture. Also, it is envisaged that the demonstrated ontology and the set of evaluation criteria named OnE will redefine ontology evaluation and make it easy while making a strong impact on ontology developers.

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Abstract

This study contributes to the recent discussions on indicating interdisciplinarity, that is, going beyond catch-all metrics of interdisciplinarity. We propose a contextual framework to improve the granularity and usability of the existing methodology for interdisciplinary knowledge flow (IKF) in which scientific disciplines import and export knowledge from/to other disciplines. To characterize the knowledge exchange between disciplines, we recognize three aspects of IKF under this framework, namely broadness, intensity, and homogeneity. We show how to utilize them to uncover different forms of interdisciplinarity, especially between disciplines with the largest volume of IKF. We apply this framework in two use cases, one at the level of disciplines and one at the level of journals, to show how it can offer a more holistic and detailed viewpoint on the interdisciplinarity of scientific entities than aggregated and context-unaware indicators. We further compare our proposed framework, an indicating process, with established indicators and discuss how such information tools on interdisciplinarity can assist science policy practices such as performance-based research funding systems and panel-based peer review processes.

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Abstract

In the last few years, the size of Linked Open Data (LOD) describing artworks, in general or domain-specific Knowledge Graphs (KGs), is gradually increasing. This provides (art-)historians and Cultural Heritage professionals with a wealth of information to explore. Specifically, structured data about iconographical and iconological (icon) aspects, i.e. information about the subjects, concepts and meanings of artworks, are extremely valuable for the state-of-the-art of computational tools, e.g. content recognition through computer vision. Nevertheless, a data quality evaluation for art domains, fundamental for data reuse, is still missing. The purpose of this study is filling this gap with an overview of art-historical data quality in current KGs with a focus on the icon aspects. Design/methodology/approach This study's analyses are based on established KG evaluation methodologies, adapted to the domain by addressing requirements from art historians' theories. The authors first select several KGs according to Semantic Web principles. Then, the authors evaluate (1) their structures' suitability to describe icon information through quantitative and qualitative assessment and (2) their content, qualitatively assessed in terms of correctness and completeness. Findings This study's results reveal several issues on the current expression of icon information in KGs. The content evaluation shows that these domain-specific statements are generally correct but often not complete. The incompleteness is confirmed by the structure evaluation, which highlights the unsuitability of the KG schemas to describe icon information with the required granularity. Originality/value The main contribution of this work is an overview of the actual landscape of the icon information expressed in LOD. Therefore, it is valuable to cultural institutions by providing them a first domain-specific data quality evaluation. Since this study's results suggest that the selected domain information is underrepresented in Semantic Web datasets, the authors highlight the need for the creation and fostering of such information to provide a more thorough art-historical dimension to LOD.

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Date

22. 5.2021 12:43:05

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Date

22. 1.2021 14:24:32

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Date

20. 1.2015 18:30:22