Ontological analysis for dynamic data model exploration

Hobbs, Mike and Luca, Cristina L. and Fatima, Arooj and Warnes, Mark (2014) Ontological analysis for dynamic data model exploration. Electronic Journal of Applied Statistical Analysis, 5 (1). pp. 42-56. ISSN 2070-5948

[img]
Preview
Text
Published Version
Available under the following license: Creative Commons Attribution Non-commercial No Derivatives.

Download (544kB) | Preview
Official URL: http://dx.doi.org/10.1285/i2037-3627v5n1p42

Abstract

Increasing access to data and computational resources allows use to use more expressive approaches to data analysis. We propose using established statistical metrics to assist the automatic analysis of free text transcripts. The meaningful concepts from a domain and their axiomatic relationships can be captured in an ontology. This provides an aggregate model which describes the domain. However, the fine detail from individual elements and their characteristics are subsumed by the whole. Keeping multiple 'micro models' of the data, along with meta information allows a range of different view points. This can be applied to free text documents that within a domain where significant information is carried by one or a few instances such as in the analysis of interview transcripts. This paper presents a framework that utilises ontological tools to create domain models in a way that it allows for a distributed and parallel implementation necessary for big data analysis.

Item Type: Journal Article
Keywords: Ontology, model, classifer, concept mapping
Faculty: ARCHIVED Faculty of Science & Technology (until September 2018)
Depositing User: Lisa Blanshard
Date Deposited: 11 Jul 2019 12:18
Last Modified: 14 Nov 2019 16:13
URI: http://arro.anglia.ac.uk/id/eprint/704544

Actions (login required)

Edit Item Edit Item