Considering social information in constructing research topic maps
| DOI | https://doi.org/10.1108/EL-10-2016-0230 |
| Published date | 03 April 2018 |
| Pages | 220-236 |
| Date | 03 April 2018 |
| Author | Hei Chia Wang,Yu Hung Chiang,Yen Tzu Huang |
| Subject Matter | Information & knowledge management,Information & communications technology,Internet |
Considering social information in
constructing research topic maps
Hei Chia Wang,Yu Hung Chiang and Yen Tzu Huang
Department of Industrial and Information Management and
Institute of Information Management, National Cheng Kung University,
Tainan, Taiwan
Abstract
Purpose –In academic work, it is important to identify a specific domain of research. Many
researchers may look to conference issues to determine interesting or new topics. Furthermore,
conference issues can help researchers identify current research trends in their fieldandlearnabout
cutting-edge developments in their area of specialization. However, so much conference information is
published online that it can be difficult to navigate and analyze in a meaningful or productive way.
Hence, the use of knowledge management (KM) could be a way to resolve these issues. In KM, ontology
is widely adopted, but most ontology construction methods do not consider social information between
target users. Therefore, this study aims to propose a novel method of constructing research topic maps
using an open directory project (ODP) and social information.
Design/methodology/approach –The approach is to incorporate conference information (i.e. title,
keywords and abstract) as sources and to consider the ways in which social information automatically
produces research topic maps. The methodology can be divided into four modules: datacollection,
element extraction, social information analysis and visualization. The data collection module collects
the required conference data from the internet and performs pre-processing. Then, the element
extraction module extracts topics, associations and other basic elements of topic maps while
considering social information. Finally, the results will be shown in the visualization module for
researchers to browse and search.
Findings –The results of this study proposethree main findings. First, creating topic maps with theODP
category information can help capture a richer set of classification associations. Second, social information
should be considered when constructing topic maps. This study includes the relationship among different
authors and topics to support informationin social networks. By considering social information, such as co-
authorship/collaborator,this method helps researchers find research topicsthat are unfamiliar but interesting
or potential cooperative opportunitiesin the future. Third, this study presents topic maps that show a clear
and simple pathwayin interested domain knowledge.
Research limitations implications –First, this study analyzes and collects conference
information, including the titles, keywords and abstracts of conference papers, so the data set must
include all of the abovementioned information. Second, social information only analyzes co-authorship
associations (collabship associations); other social information could be extracted in the future study.
Third, this study only analyzes the associations between topics. The intensity of associations is not
discussed in the study.
Originality/value –The study will have a great impact on learned societies because it bridges the gap
between theory and practice. The study is useful for researchers who want to know which conferences
are related to their research. Moreover, social networks can help researchers expand and diversify their
research.
Keywords Text mining, Social information, Open directory projects, Topic maps
Paper type Research paper
The research was based on work supported by the Taiwan Ministry of Science and Technology
under Grant N. MOST 103-2410-H-006-055-MY3.
EL
36,2
220
Received27 October 2016
Revised8 June 2017
Accepted1 August 2017
TheElectronic Library
Vol.36 No. 2, 2018
pp. 220-236
© Emerald Publishing Limited
0264-0473
DOI 10.1108/EL-10-2016-0230
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0264-0473.htm
Introduction
In the realm of information retrieval,research capability has come to be seen as the source of
knowledge (Numprasertchai and Igel, 2005). Researchers are expected to know about new
research trends and to cooperate with other researchers across disciplines (Palmer, 2013,
Tomaszewski and MacDonald, 2009). With the large number of research articles that have
been converted to electronic format, many researchers try to identify interesting research
topics by reviewing online resources. Finding a proper research domain is an important
issue for these researchers. Duringthe search process, conference papers could be one of the
greatest potential resources because most conference program chairs are likely to consider
new trends for conference topics. Unlike journals,which have stricter editing guidelines and
a longer publishing period, the conference publishing period is normally shorter and closer
to the current focus. In conferences, researchers can realize current research trends in their
field and learn about cutting-edge developments in their specializations. Moreover,
conferences play an important role in scholarly communication because they provide
scientists and scholars with an opportunityto present and discuss the preliminary results of
their research and to improve their personal social networks (González-Albo and Bordons,
2011).
Although many conference resources are available on the Internet on sites, such as
DBworld and ConfSearch, most of them must still be searched via keywords. There are
no detailed or unified classifications for conferences or the possible relationships
between classifications. For example, currently, the more popular conference resources,
such as AllConferences.com (www.allconferences.com) and conference alerts (www.
conferencealerts.com), offer only manual conference knowledge classifications or no
classification at all. They are not designed to include domain knowledge or possible
relationships between classifications. This condition leads to incomplete search results.
Researchers spend lots of time in finding the information they need. Due to the time
limitation, some useful information may not be found. Therefore, a conference
information analysis platform could be designed to help researchers find suitable
research domain-related topics.
To this end, knowledge management (KM) could be a viable resource(Biswas et al.,2014;
Hamasaki et al., 2007). Conferencecontent, such as keywords, articles and websites, could be
properly analysed and classified by the KM method. Constructing a conceptualized
architecture would allow researchers to extend topics of interest and improve the accuracy
of their search results. In recent years, topic maps, which have been widely used in the
construction of conceptual architecture, have received growing attention (Santoso et al.,
2011). They can transform implicitknowledge into explicit knowledge, providing benefits to
many applications –for example, information retrieval systems (Lee et al., 2007;Xia et al.,
2016)–and Web search engines (Al-Rajebah and Al-Khalifa, 2010;Chiu and Pan, 2014).
There have been many studies concerning topic map construction (Du et al., 2009;Santoso
et al.,2011;Yao et al.,2013). However, existing topic map construction methods do not
consider social information.
During the past decade, social network sites have become one of the most popular
access points. In social networks, access records can be used to determine relationships
among social members (Hu and Racherla, 2008;Vigneshwari and Aramudhan, 2015).
Hamasaki et al. (2007) suggested that considering social information, such as who
collaborates and/or cooperates with whom, might improve the extracted conceptual
architecture. Therefore, the purpose of this study is to incorporate conference
information (i.e. title, keywords and abstract) as sources and to consider the ways in
which social information automatically produces research topic maps. By providing the
Research topic
maps
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