To ontologise or not to ontologise: An information model for a geospatial knowledge infrastructure

Stock, Kristin; Stojanovic, Tim; Reitsma, Femke; Ou, Yang; Bishr, Mohamed; Ortmann, Jens; Robertson, Anne

doi:10.1016/j.cageo.2011.10.021

Cited by 27 publications

(20 citation statements)

References 10 publications

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“…Although their contribution does not elucidate the ontologies in light of an abstraction framework, a reader can easily see a parallel and useful abstraction framework such as that in Table 1. The domain realm of ontologies builds on top of the foundation to include specific concepts to a topical situation; e.g., Stock et al (2012) describe a coastal science domain. Domain ontologies seek to further contextualize foundation ontologies by adding nuanced relationships specific to elements within topical domains, and by extension relationships across super-order domains.…”

Section: Ontologies Of Information Representationmentioning

confidence: 99%

“…Couclelis (2010) used the foundational realm to focus her International Journal of Geographical Information Scienceresearch on the foundations of geographic information representation that we will extend below. Stock et al (2012) develop a roughly ordered set of 10 ontologies, using an upperlevel ontology as the foundation of the nine others that span all the way to application ontology of coastal science data. Although their contribution does not elucidate the ontologies in light of an abstraction framework, a reader can easily see a parallel and useful abstraction framework such as that in Table 1.…”

Section: Ontologies Of Information Representationmentioning

confidence: 99%

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Foundations of sustainability information representation theory: spatial–temporal dynamics of sustainable systems

Nyerges

Roderick

Prager

et al. 2013

International Journal of Geographical Information Science

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A critical need exists to broaden and deepen sustainability information foundations that can foster growth of actionable knowledge about human-environment relations to address grand challenges in sustainable system domains such as sustainable development, social-ecological systems, and hazards influencing global environmental change. Broad-based information is needed to integrate across domains to address sustainability problems cast as complex systems problems that vary widely across space and time. Deep-based information is needed to address nuanced and contextual relationships that exist within and across domains. Both broad and deep information together are needed to better address spatial-temporal dynamics in complex sustainable systems. According to many publications, the concept of sustainable systems is considered to be at the core of self-organizing systems; and in turn, the concept of selforganizing systems is at the core of social-ecological systems, coupled natural-human systems, and human-environment systems. The sustainable systems concept is elucidated in terms of ontological and epistemological foundations from geographic information representation theory. A framework for Measurement-informed Ontology and Epistemology for Sustainability Information Representation, drawing from research about ontology and epistemology in geographic information science, provides the foundation for elucidating concepts and relations about sustainability information representation in general and sustainable systems in particular. The framework is developed to form a core of sustainability information representation theory, and consequently provides a basis for articulating first principles about the character of space-time data models that can be used to create computational models within geographic information systems (GIS). An example applies the framework to common pool resources as sustainable systems. Developing the framework and exploring an example fosters intellectual bridge building between sustainability science and sustainability management in the form of a sustainability information science. That intellectual bridge building of sustainability information science supports societal progress moving knowledge into decision action toward sustainable development, encouraging new insight for designs of space-time data models, and extending GIS as an information technology foundation for sustainability management. Conclusions and directions for next steps in research about sustainability information representation theory in general and sustainable systems in particular are offered.

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Section: Ontologies Of Information Representationmentioning

confidence: 99%

Section: Ontologies Of Information Representationmentioning

confidence: 99%

Foundations of sustainability information representation theory: spatial–temporal dynamics of sustainable systems

Nyerges

Roderick

Prager

et al. 2013

International Journal of Geographical Information Science

View full text Add to dashboard Cite

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“…Due to the continuous release of user-generated content and linking open data on the Internet, people need to explore knowledge interconnection methods which both conform to the development of the network information resources and meet users' requirements from a new perspective according to the knowledge organization principles in the large data environment, to reveal human cognition on a deeper level [55].…”

Section: Application Of Knowledge Visualization Servicementioning

confidence: 99%

Big Data Management for Cloud-Enabled Geological Information Services

Zhu

Tan

Luo

et al. 2018

Scientific Programming

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Cloud computing as a powerful technology of performing massive-scale and complex computing plays an important role in implementing geological information services. In the era of big data, data are being collected at an unprecedented scale. Therefore, to ensure successful data processing and analysis in cloud-enabled geological information services (CEGIS), we must address the challenging and time-demanding task of big data processing. This review starts by elaborating the system architecture and the requirements for big data management. This is followed by the analysis of the application requirements and technical challenges of big data management for CEGIS in China. This review also presents the application development opportunities and technical trends of big data management in CEGIS, including collection and preprocessing, storage and management, analysis and mining, parallel computing based cloud platform, and technology applications.

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Section: Introductionmentioning

confidence: 99%

“…Currently, there is the increasing number of research papers devoted to the issue of the community of practitioners' knowledge management and the implementation of individual processes of knowledge transformation. [7], [8].The European concept of knowledge management [2] identifies five processes of knowledge life cycle: knowledge identification, creation, storage, distribution and use. Knowledge life cycle actually reflects the methods and technologies of knowledge management at each technological step.…”

mentioning

confidence: 99%

Information Models and Methods of the University's Scientific Knowledge Life Cycle Support

Zhomartkyzy¹,

Tatiana²,

Miłosz³

2014

Annals of Computer Science and Information Systems

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Abstract-The main aim of this work is to develop methods and technologies of the university's scientific knowledge management. The paper examines the concept of knowledge management and life-cycle processes of the university's scientific knowledge. Text Mining and Semantic Web technologies are used to develop the ontological information model and for information resources processing. The paper describes the developed information model of the university's scientific knowledge, the methods of forming scientific profiles, and the concept of the university's scientific knowledge semantic portal. I. INTRODUCTIONK NOWLEDGE , intellectual property, and intellectual resources have been understood in recent decades as a major driving force of the economy of the "third wave" (the new economy), the economy based on knowledge of expanded reproduction [1]. Knowledge as intellectual capital is gradually becoming one of the most important factors in the development of economy and the society.For a modern institution of higher education as an open social and economic self-organizing system the processes of creation, accumulation, and dissemination of knowledge is becoming a key factor for training competitive specialists.Paraphrasing [2], we can determine that the university's scientific knowledge as the combination of data and information with added opinions, skills and experience of the university's scientists and faculty members is a valuable asset that can provide an advantage in the market of educational services in project activities, scientific and practical work, and innovative activities.The intellectual capital or intangible assets of the university are the source of new scientific knowledge. Edvinsson developed a hierarchical structure of intellectual capital of a higher education institution -a "Skandia Value Scheme" model [3]. The main components of this model are human assets (the capital) and innovating capital which are tacit knowledge.Tacit knowledge form the human capital which is embodied in the university staff as a body of knowledge, qualifications, each employee's innovation, as the system of values, culture and philosophy of the institution. It is believed that practical tacit knowledge is the key to decision making and management.There is a continuous exchange between explicit and implicit knowledge and their transformation. [4]. Nonaka I., Takeuchi H. suggested a cyclical process of knowledge transformation: socialization, externalization, combination, and internalization.Kantner defines the concept of knowledge management as a strategy for the organization and the process of knowledge transformation [5], [6]. Currently, there is the increasing number of research papers devoted to the issue of the community of practitioners' knowledge management and the implementation of individual processes of knowledge transformation. [7], [8].The European concept of knowledge management [2] identifies five processes of knowledge life cycle: knowledge identification, creation, storage, distribution and use. Knowledge ...

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To ontologise or not to ontologise: An information model for a geospatial knowledge infrastructure

Cited by 27 publications

References 10 publications

Foundations of sustainability information representation theory: spatial–temporal dynamics of sustainable systems

Foundations of sustainability information representation theory: spatial–temporal dynamics of sustainable systems

Big Data Management for Cloud-Enabled Geological Information Services

Information Models and Methods of the University's Scientific Knowledge Life Cycle Support

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