Semiometrics: producing a compositional view of influence


McRae-Spencer, Duncan (2007) Semiometrics: producing a compositional view of influence. University of Southampton, School of Electronics and Computer Science, Doctoral Thesis , 159pp.

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Description/Abstract

High-impact academic papers are not necessarily the most cited. For example,
Einstein's 'Special Relativity' paper from 1905 received (and continues to receive)
fewer citations from other papers than his 'Brownian Motion" paper of the same
year, despite the former radically changing the course of an entire scientific
discipline to a much greater extent. Similarly, 'impact' metrics using citation count
alone are, it is argued, not adequate for determining the scientific influence of
papers, authors or small groups of authors. Although valid, they remain
controversial when used to determine influence of larger groups or journals. While
the term 'impact' has become closely linked to a journal's citation-based Journal
Impact Factor score, this thesis uses the term 'influence' to describe the wider
effectiveness of research, combining citation and metadata analysis to allow richer
calculations to be performed over large-scale document networks. As a result, more
qualitative influence ratings can be determined and a broader outlook on scientific
disciplines can be produced. These ratings are best applied using an ontology-based
data source, allowing more efficient inference than under a traditional RDBMS
system, and allowing easier integration between heterogeneous data sources. These
metrics, termed 'Semantic Bibliometrics' or 'Semiometrics', can be applied at a
variety of levels of granularity, allowing a compositional framework for impact and
influence analysis. This thesis describes the process of data preparation, systems
architecture, metric value and data integration for such a system, introducing novel
approaches at all four stages, thereby creating a working semiometrics system for
determining influence at different semantic levels of granularity.

Item Type: Thesis (Doctoral)
Subjects: H Social Sciences > HA Statistics
Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science
Divisions: University Structure - Pre August 2011 > School of Electronics and Computer Science
ePrint ID: 191379
Date Deposited: 11 Jul 2011 13:50
Last Modified: 27 Mar 2014 19:43
URI: http://eprints.soton.ac.uk/id/eprint/191379

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