Global ontology research progress: a bibliometric analysis
| Date | 19 January 2015 |
| DOI | https://doi.org/10.1108/AJIM-05-2014-0061 |
| Pages | 27-54 |
| Published date | 19 January 2015 |
| Author | Qiaoli Zhu,Xuesong Kong,Song Hong,Junli Li,Zongyi He |
| Subject Matter | Library & information science,Information behaviour & retrieval |
Global ontology research
progress: a bibliometric analysis
Qiaoli Zhu, Xuesong Kong, Song Hong, Junli Li and Zongyi He
School of Resource and Environmental Science, Wuhan University,
Wuhan, China
Abstract
Purpose –The purpose of this paper is to analyse the global scientific outputs of ontology research,
an important emerging discipline that has huge potential to improve information understanding,
organization, and management.
Design/methodology/approach –This study collected literature published during 1900-2012 from
the Web of Science database. The bibliometric analysis was performed from authorial, institutional,
national, spatiotemporal, and topical aspects. Basic statistical analysis, visualization of geographic
distribution, co-word analysis, and a new index were applied to the selected data.
Findings –Characteristics of publication outputs suggested that ontology research has entered into the
soaring stage, along with increased participation and collaboration. The authors identified the leading
authors, institutions, nations, and articles in ontology research. Authors were more from North America,
Europe, and East Asia. The USA took the lead, while China grew fastest. Four major categories of
frequently used keywords were identified:applications in Semantic Web, applications in bioinformatics,
philosophytheories, and commonsupporting technology.Semantic Web researchplayed a core role, and
gene ontology study was well-developed. The study focus of ontology has shifted from philosophy to
information science.
Originality/value –This is the first study to quantify global research patterns and trends in ontology,
which might provide a potential guide for the future research . The new index provides an alternative way
to evaluate the multidisciplinary influence of researchers.
Keywords Research trend, Bibliometrics, Ontology, Disciplinary incidence index (DII),
Scientific outputs
Paper type Research paper
Introduction
Ontology is a notion originated from philosophy, mainly defined as a theory describing
the construction of the world. Even as a pure philosophical concept, it was elected for
scientific research as the theoretical base (Chidamber and Kemerer, 1994). Artificial
intelligence (AI), basically, can be regarded as the entrance to information science
community for ontology. Although the initial use of ontology in AI was merely as an
ordinary term (McCarthy, 1980) without scientific description, from then on, the term
ontology obtained its orientation in information science that the study of ontology
could be of potential in information and knowledge domains (Guarino, 1995, 1998).
Ontology was an emerging theory in information science which was responsible for long
period of arguments before its maturity, such as the various definitions (Neches et al.,
1991; Gruber, 1993; Uschold and Gruninger, 1996; Studer et al., 1998). Among these,
Gruber’s short definition “An ontology is an explicit specification of a conceptualization”
(Gruber, 1993), was commonly accepted and sometimes enriched by subsequent
researchers (Borst, 1997; Studer et al., 1998). The rules of ontology construction were also
discussed in that stage (Gruber, 1995; Guarino and Welty, 2000). Along with these
debates, research on ontology was becoming increasingly prevalent in practice,
Aslib Journal of Information
Management
Vol. 67 No. 1, 2015
pp. 27-54
©Emerald Group Publis hing Limited
2050-3806
DOI 10.1108/AJIM-05-2014-0061
Received 15 May 2014
Revised 19 September 2014
Accepted 3 November 2014
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/2050-3806.htm
This study was funded by National Natural Science Foundation of China (No. 41071290).
27
Global
ontology
research
progress
e.g. knowledge engineering (Guarino, 1997; vanHeijst et al., 1997), natural language
processing (Lang, 1991; Bateman, 1995; Bateman et al., 2010), knowledge representation
(Guarino, 1995; Sowa, 2000), information retrieval (McGuinness, 1998; Li et al., 2008),
and knowledge management (Kietz et al., 2000; Holsapple and Joshi, 2004; Brandt et al.,
2008). Over the recent two decades, scientific articles on ontology research have
demonstrated an expeditious increase in quantity, and the reason for ontology research
obtaining increasing scientific attention is the need of effective methods for
communication both from human and computer perspectives (Gruber, 1995; Studer
et al., 1998; Ding, 2001). Our world is becoming more connected especially with the
development of the Internet, but also accompanying with an information deluge
(Bawden and Robinson, 2008). Ontology has the huge potential to help people and
computers to extract the information they need and to communicate effectively with
each other (Ding and Foo, 2002; Myrgioti et al., 2009). It has been crucial since the
Semantic Web (Berners-Lee et al., 2001) was proposed as the next wave of web
transformation, because of enabling content-based access, interopera bility and
communication across the Web (Halpin and Presutti, 2011). In addition, with the
exponential growth of accessible biolog ical information, ontology research in
bioinformatics has aroused great interest of scholars (Ashburner et al., 2001).
The gene ontology (GO) is an extremely important tool for the unification of biology,
including three independent ontologies: biological process, molecular function, and
cellular component (Ashburner et al., 2000, 2001).
Despite growing popularity, there have been few attempts to gather systematic data
on the global scientific production of ontology research. Although ontology-related
research as an addition to the Semantic Web (SW) has been studied with bibliometric
techniques (Ding, 2010), this analysis was confined by the most productive authors,
highly cited journals, authors, and papers in the SW field over 1960-2009. The number
of citations was the only evaluating indicator used, and it is not comprehensive to
provide the specific patterns of ontology research. Likewise, synonymy and homonymy
problems about the authors were not expressed. Ontology research is burgeoning and
widespread in the discipline of information, which has particularly close ties to current
society. Besides statistical analysis of professional aspects, it is essential to show
readers the basic development situation, providing an easy access to understanding
ontology research. Recently, the bibliometric method, as a common and effective tool,
has already been widely applied for scientific production and research trend analysis
in various fields (van Raan et al., 2003; Leydesdorff and Wagner, 2009; Lariviere et al.,
2012). The conventional bibliometric methods generally evaluate research trends by
analysing the publication outputs, citation times, and keyword frequencies (van Raan,
2008; Bornmann et al., 2012; Costas and Bordons, 2008). In the meantime, a number of
innovative techniques have been excogitated, such as citation structure analysis
(Small and Upham, 2009), base maps and overlay techniques (Leydesdorff et al., 2012),
and network analysis (Waltman et al., 2010). Moreover, in addition to straightforward
counting, various indicators have been established to measure the scientific performance
from different perspectives, such as the h-index (Hirsch, 2005), the Eigenfactor (Bergstrom
et al., 2008), the Audience factor (Zitt and Small, 2008), and the SNIP (Moed, 2010). As an
important emerging discipline and a complex multidisciplinary field, we think the
interdisciplinary research of ontology could be interesting and meaningful.
Interdisciplinary research has variant name forms (multi, cross, transdisciplinary)
and can be approached from different perspectives (Bordons et al., 2005). Hence,
various bibliometric indicators have been proposed to facilitate it, based on different
28
AJIM
67,1
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