Constructing an information science resource ontology based on the Chinese Social Science Citation Index
| Published date | 10 March 2014 |
| Pages | 202-218 |
| DOI | https://doi.org/10.1108/AJIM-10-2013-0114 |
| Date | 10 March 2014 |
| Author | Junping Qiu,Wen Lou |
| Subject Matter | Library & information science,Information behaviour & retrieval |
Constructing an information
science resource ontology based
on the Chinese Social Science
Citation Index
Junping Qiu
Research Center for Science Evaluation, Wuhan University, Wuhan, China, and
Wen Lou
School of Information Management, Wuhan University, Wuhan, China
Abstract
Purpose – The purpose of this study is to construct a Chinese information science resource ontology
and to explore a new method for semiautomatic ontology construction.
Design/methodology/approach – More than 8,290 articles indexed in the Chinese Social Science
Citation Index (CSSCI), covering the years 2001 to 2010, were included in this study. Statistical
analysis, co-occurrence analysis, and semantic similarity methods were applied to the selected articles.
The ontology was built using existing construction principles and methods, as well as categories and
hierarchy definitions based on CSSCI indexing fields.
Findings – Seven categories were found to be relevant for the Chinese information science resource
ontology, which, in this study, consists of a three-tier architecture, 78,291 instances, and 182,109 pairs
of semantic relations. These results indicate the following: further improvements are required in
ontology construction methods; resource ontology is a breakthrough concept in ontology studies; the
combination of semantic similarities and co-occurrence analysis can quantitatively describe
relationships between concepts.
Originality/value – This study pioneers the resource ontology concept. It is one of the first to
combine informetric methods with semantic similarity to reveal deep relationships in textual data.
Keywords Information science, Chinese SocialScience Citation Index, Ontologyconstruction,
Resource ontology,Semantic similarity
Paper type Research paper
Introduction
Ontology is a concept originally studied in philosophy that has also been explored in
artificial intelligence and other computer science fields (Li, 2005). Gruber considered
ontology to be the explicit specification of concepts. His definition has been widely
applied to information systems and knowledge management. Contemporary
scholarship divides the concept of ontology into domain ontology, top-ontology (also
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This paper was supported by a major program of the national social science foundation of China,
“Semantic-based deep integration and visualisation of library resources” (11&ZD152), the
High-level International Journal Program of Wuhan University (2012GSP032), and the
Fundamental Research Funds for the Central Universities “Semantic information retrieval based
on resource ontology” (2013104010201). The authors would like to thank Yu Fan, Li Yue and Li
Mengru for their helpful suggestions for improving this manuscript.
AJIM
66,2
202
Received 25 October 2013
Revised 11 December 2013
Accepted 17 December 2013
Aslib Journal of Information
Management
Vol. 66 No. 2, 2014
pp. 202-218
qEmerald Group Publishing Limited
2050-3806
DOI 10.1108/AJIM-10-2013-0114
called general ontology), Applied ontology, and representation ontology (Dong, 2008).
In this study, semantic theory is used as a basis, with additional information imported
to expand semantic theory to construct resource ontology. a resource ontology (RO) is
an explicit specification of a library resource sharing concept model combined with
concept relationships. RO is based on five principles:
(1) Library resources are objective.
(2) Concepts and relationships between concepts can be derived using an
informetric analysis.
(3) Every concept has a clear definition (is “explicit”).
(4) An RO should be specified so that it can be recognised and processed
computationally.
(5) “Sharing” means that a concept model should refer to generally acknowledged
concept sets of library resources.
The core objective of RO is the construction of a concept structure that defines
properties and determines relationships based on structures and relationships implicit
in the literature being analysed. RO includes ontologies that describe the digital
resources, their contents, and their applications. This study explores a new ontology
construction method, and provides a theoretical and methodological reference for
automated ontology construction. Library electronic resources, downloaded article s
from the Chinese Social Science Citation Index (CSSCI), and Chinese information
science are used to construct an example RO.
Related work
Gruber proposed five objective criteria for ontologies: clarity, coherence, extendibility,
minimal encoding bias, and minimal ontologi cal commitment (Gruber, 1995).
Unfortunately, it is difficult to simultaneou sly satisfy all five criteria when
constructing ontologies. Extendibility does not conflict with minimal encoding bias,
nor does clarity conflict with minimal ontological commitment. However, optimising
clarity and minimising ontological commitment risks a lower extensibility and a
greater encoding bias.
Current widespread ontology construction methods include IDER5, Skeleton,
TOVE, METHONTOLOGY, KACTUS, and SENSUS. Although they have different
names and objectives, the core concepts and major procedures are all similar,
incorporating such ter ms as selection, concept e xtraction, semantic rela tions
extraction, classification system building, ont ology construction and ontology
evaluation (Liu, 2006). Of these, concept extraction and semantic relations extraction
are the key terms for this study.
Concept extraction. A concept is either a category concept or an instance concept.
There are two basic methods for extracting them. One method is selecting and
enlarging concept sets with reference to a current expert vocabulary and lexicon. This
method satisfies Gruber’s criteria. Usually, a thesaurus or encyclopaedia is chosen as
the candidate concept set, which is enlarged via expert surveys and literature reviews
(Zhang and Dang, 2007; L’Homme and Gabriel, 2012). The other method is machine
learning, which extracts concepts from structured data and documents. Biomedical
ontology construction has long been a research objective of automated or
Information
science resource
ontology
203
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