Meta-modeling framework: A new approach to manage meta-modelbase and modeling knowledge

Malhotra, Rashmi

doi:10.1016/j.knosys.2006.10.007

Cited by 9 publications

(1 citation statement)

References 36 publications

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“…Ontologies are considered as appropriate modelling structures for representing complex domains under a homogenous terminology and a knowledge integration platform that handles structured or unstructured information resources [10]. Most platforms developed, however, provide a taxonomic (is-a) backbone, built on strict hierarchical (often disciplinary) intrarelations, that is used to detect mereological (part-of) inter-relations at the same or adjacent knowledge levels (see, e.g., [11][12][13]), a format that cannot satisfy the third part of integrating modelling. In most aquatic ecosystem ontologies (see e.g., [14][15][16][17], the scientific domain (and its scale) is drawn first and then the object of interest is represented as a downstream instance of this domain; there certainly exists the risk either to supersede interrelations existing beyond the domain's scale boundaries or to rely on partonomic functions that are not consistent with the site hydrogeobiochemistry.…”

Section: Introductionmentioning

confidence: 99%

An ontological approach to multi-scale modelling of environmental fate and ecological effects in aquatic ecosystems

Siontorou¹,

Batzias²

2013

Computational Methods and Experimental Measurements XVI

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Aquatic ecosystems are complex and multifaceted webs that continuously shift states to accommodate natural and anthropogenic inputs. Managing such a dynamic scheme necessitates multidisciplinary and multi-scale modelling. Although a variety of modelling strategies and tools have been suggested and used, their applicability is often restricted to one scale (usually the macro-or meso-scale) or to related domains of knowledge (usually hydrogeomorphology or ecology), making accurate predictions on whole system responses difficult to achieve. A critical impediment in constructing an integrative framework lies in the structure of knowledge, which remains context-specific across different disciplines. Acquiring and evaluating information (at the required amount and level of granularity) in collaborative interactions is often a challenge, as is the transfer of this information to others, including scientists, stakeholders, resource managers, policymakers, and the public. This paper presents a methodological framework for bridging the gaps between macro-and micro-scales in ecosystem modelling through an ontological platform designed/developed to accommodate partonomic functions in and between different knowledge domains and levels.

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