Developing a geoscience knowledge framework for a national geological survey organisation

Howard, Andrew S.; Hatton, Bill; Reitsma, Femke; Lawrie, Ken

doi:10.1016/j.cageo.2008.06.004

Cited by 27 publications

(16 citation statements)

References 17 publications

(22 reference statements)

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“…Finally, the system should have the ability to integrate to the results of the semi-automatic processing convenient expert interpretations based on professional standards, skills and experiences. The system will have to take into account both genetic and mining interpretations, and to manage the multiplicity of interpretations and models, not only explicit ones, but also a large amount of implicit ones, as defined by Nickols [12] and advised by Howard, et al [8]. This will, then, constitute the subsystem of knowledge: the knowledge base [11].…”

Section: Expected Results Of the Systemic Approachmentioning

confidence: 99%

“…The BGS, for example, has developed and implemented a Project Management System (PMS) prior to the Knowledge System implementation. This PMS provides the BGS Knowledge System by the project planning information [8]: deployed approach, methodologies, resources and expertise.…”

Section: The Concept Of the Knowledge Systemmentioning

confidence: 99%

“…On the other hand, it is essential that the integration of those should not only be done as "best practice reference documents" [8], but should be implemented, when possible, on a functionality level of the system as "configuring best practices". That way, the system will be flexible enough to continuously integrate new "best practices" and constrained enough to ensure the use of the consensually "best practices".…”

Section: N Azzouzimentioning

confidence: 99%

“…• The system has to be able to integrate, organize, manage, analyse, query, and share knowledge using standard geoscience's vocabularies, hierarchical classifications and, if applicable, ontologies [8]. Ontology-based knowledge systems are getting more effectiveness to integrate information of various types [16].…”

Section: N Azzouzimentioning

confidence: 99%

“…At this stage, the system will help to effectively capture the tacit know-how to guide the transformation of the implicit knowledge to the explicit one [8].…”

Section: N Azzouzimentioning

confidence: 99%

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A Systemic Approach Integrating Genetic, Mining and Management Visions: The Geologic and Mining Knowledge System

Azzouzi¹

2017

JGIS

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Following the example of other industrial activities, mining evaluation is now exposed to socio-economical and technological constraints which are unstable in quick evolution. The keys to its success are increasingly related to a methodology of work more scientific than ever. The Systemic Approach has broadly showed its effectiveness in numerous disciplinary fields, both scientific and engineering ones: Biology, Economy, Social and Management Sciences, Quality Management, Information Systems… Helped by technological progress, this approach has especially excelled in the management of spatial information (e.g. GIS). It constitutes therefore an excellent solution to the problems of mining evaluation by the integration of genetic, mining and managerial data within an Information System, thus optimizing scientific and economic valuation of mineral resources.

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Section: Expected Results Of the Systemic Approachmentioning

confidence: 99%

Section: The Concept Of the Knowledge Systemmentioning

confidence: 99%

Section: N Azzouzimentioning

confidence: 99%

Section: N Azzouzimentioning

confidence: 99%

“…At this stage, the system will help to effectively capture the tacit know-how to guide the transformation of the implicit knowledge to the explicit one [8].…”

Section: N Azzouzimentioning

confidence: 99%

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A Systemic Approach Integrating Genetic, Mining and Management Visions: The Geologic and Mining Knowledge System

Azzouzi¹

2017

JGIS

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The Reproducible Data Reuse (ReDaR) Framework to Capture and Assess Multiple Data Streams

Keefer

Blake

2021

Proceedings of the Association for Information Science and Tech

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Much of the literature in knowledge discovery from data (KDD) focuses on algorithms that are faster and more accurate at capturing patterns in a given data set. However, answering a research question is fundamentally connected with how well the data is aligned with the questions being asked. Thus, data selection is one of the most important steps to ensure that models produced from the KDD process are useful in practice. A lack of documentation about the data selection rationale and the transformations needed to semantically align the data streams prevents others from reproducing the research and obfuscates development of best practices in data integration. Our goal in this paper is to provide KDD practitioners with a framework that brings together theories in provenance, information quality, and contextual reasoning, to enable researchers to achieve a semantically aligned dataset with data selection, description, and documentation based on an application-focused assessment. KEYWORDSSemantic alignment; fitness assessment; data selection; reused data; data practices. INTRODUCTIONKnowledge discovery from databases (KDD) is a multi-step process for pattern identification and classification in data that includes data selection, preprocessing, and transformation steps to prepare data for later modeling and interpretation (Fayyad et al., 1996;Mariscal et al., 2010). KDD applications focused on data-and model-based research, however, are dependent on the semantic alignment and selection of data that are relevant to the application goals. There is a lack of documentation and discussion over best practices for the alignment and selection of data from multiple data streams in modeling and knowledge-based research.Researchers in the Earth sciences often analyze and model subsurface geologic systems through the integration of multiple data streams that are conceptually heterogeneous, and syntactically and semantically ambiguous (Gil et al., 2018;Howard et al., 2009;Nickel et al., 2011). Research on global climate change requires integration of regional and global data sets, where semantic ambiguity can mistakenly encourage the comparison of semantically distint data streams (Faghmous and Kumar, 2014;Ford, et al., 2016;Hassani et al., 2019). In health care, there are moves to compile vast distributed datasets to support 'learning health systems' (Etheredge, 2007;Friedman, et al., 2015;Delaney, et al., 2015). In these environments, and others, the fidelity of data mining results and utility of modelbased insights is dependent on the semantic alignment of complex data streams and on the identification and selection of relevant data. And, as recently noted by the National Academies of Sciences, Engineering and Medicine in evaluating the data requirements for reliable, repeatable toxic risk assessments, "Without a clear definition of evidence streams or documented approaches to evidence synthesis, within each evidence stream, it is difficult to assess and reproduce the results of evidence synthesis." (NASEM, 2021).

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Semantic Models for the Geological Mapping Process

Lombardo

Piana

Mimmo

et al. 2017

AI*IA 2017 Advances in Artificial Intelligence

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Abstract. The geologic mapping process requires the organization of data according tothe general knowledge about the objects in the map, namely the geologic units, and tothe objectives of a graphic representation of such objects in a map, following some established model of geotectonic evolution. Semantics can greatly help such a process in providing a terminological base to name and classify the objects of the map and supporting the application of reasoning mechanisms for the derivation of novel properties and relations about the objects of the map.The OntoGeonous initiative has built a terminological base of geological knowledge in a machine-readable format, following the Semantic Web tenets and the Linked Data paradigm, with the construction of an appropriate data base schema that can be then filled with the objects of the map. The paper will present the conceptual model of the geologic system and how the elements of the cartographic database are classified from general definitions. Also, the paper addresses the setup of web-based services that respond to queries concerning the properties of the map elements that are not explicitly asserted in the underlying data base, but are inferred through a reasoning process.

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Developing a geoscience knowledge framework for a national geological survey organisation

Cited by 27 publications

References 17 publications

A Systemic Approach Integrating Genetic, Mining and Management Visions: The Geologic and Mining Knowledge System

A Systemic Approach Integrating Genetic, Mining and Management Visions: The Geologic and Mining Knowledge System

The Reproducible Data Reuse (ReDaR) Framework to Capture and Assess Multiple Data Streams

Semantic Models for the Geological Mapping Process

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