Expert profiling for collaborative innovation: big data perspective
| DOI | https://doi.org/10.1108/IDD-03-2017-0021 |
| Published date | 20 November 2017 |
| Date | 20 November 2017 |
| Pages | 169-180 |
| Author | Thushari Silva,Jian Ma |
| Subject Matter | Library & information science,Library & information services,Lending,Document delivery,Collection building & management,Stock revision,Consortia |
Expert profiling for collaborative innovation:
big data perspective
Thushari Silva
Faculty of Information Technology, University of Moratuwa, Moratuwa, Sri Lanka, and
Jian Ma
Department of Information Systems, City University of Hong Kong, Kowloon, Hong Kong
Abstract
Purpose – Expert profiling plays an important role in expert finding for collaborative innovation in research social networking platforms. Dynamic
changes in scientific knowledge have posed significant challenges on expert profiling. Current approaches mostly rely on knowledge of other experts,
contents of static web pages or their behavior and thus overlook the insight of big social data generated through crowdsourcing in research social
networks and scientific data sources. In light of this deficiency, this research proposes a big data-based approach that harnesses collective
intelligence of crowd in (research) social networking platforms and scientific databases for expert profiling.
Design/methodology/approach – A big data analytics approach which uses crowdsourcing is designed and developed for expert profiling. The
proposed approach interconnects big data sources covering publication data, project data and data from social networks (i.e. posts, updates and
endorsements collected through the crowdsourcing). Large volume of structured data representing scientific knowledge is available in Web of
Science, Scopus, CNKI and ACM digital library; they are considered as publication data in this research context. Project data are located at the
databases hosted by funding agencies. The authors follow the Map-Reduce strategy to extract real-time data from all these sources. Two main steps,
features mining and profile consolidation (the details of which are outlined in the manuscript), are followed to generate comprehensive user profiles.
The major tasks included in features mining are processing of big data sources to extract representational features of profiles, entity-profile
generation and social-profile generation through crowd-opinion mining. At the profile consolidation, two profiles, namely, entity-profile and
social-profile, are conflated.
Findings – (1) The integration of crowdsourcing techniques with big research data analytics has improved high graded relevance of the constructed
profiles. (2) A system to construct experts’ profiles based on proposed methods has been incorporated into an operational system called ScholarMate
(www.scholarmate.com).
Research limitations – One shortcoming is currently we have conducted experiments using sampling strategy. In the future we will perform
controlled experiments of large scale and field tests to validate and comprehensively evaluate our design artifacts.
Practical implications – The business implication of this research work is that the developed methods and the system can be applied to streamline
human capital management in organizations.
Originality/value – The proposed approach interconnects opinions of crowds on one’s expertise with corresponding expertise demonstrated in
scientific knowledge bases to construct comprehensive profiles. This is a novel approach which alleviates problems associated with existing methods.
The authors’ team has developed an expert profiling system operational in ScholarMate research social network (www.scholarmate.com), which is
a professional research social network that connects people to research with the aim of “innovating smarter” and was launched in 2007.
Keywords Data mining, Crowdsourcing, Data integration, Big data, Knowledge discovery, Expert profiling
Paper type Research paper
1. Introduction
Research social networks (RSNs) – virtual networks of
scholars – became a vital part of research and innovation
ecosystems. Applications such as Twitter and LinkedIn are
widely used to promote research work, access others’ research
and hold discussion around research ideas (Paton, 2014).
Thus, RSNs such as LinkedIn have become common
platforms for innovative collaborations. Social data in
(research) social networks reveal an astoundingly large volume
of information about scholars which is otherwise inaccessible.
The inwardness in leveraging this big social data for
innovational collaboration is expert profiling (Lu et al., 2016;
Silva et al., 2013). Expert profiling controls the quality of
expert discovery, hence the productivity of the research and
innovation (Chen et al., 2014).
Expert profiling is the process of determining key attributes
that can be used to characterize a given expert. The objective
of expert profiling is to extract expertise of researchers/experts.
According to existing literature, expert profiling approaches
can be classified into two main categories: topic-centric
methods and social-network based methods. (Vu et al., 2017;
Middleton et al., 2004;Tang et al., 2010). Topic-centric
methods capture subjective self-claimed information about
The current issue and full text archive of this journal is available on
Emerald Insight at: www.emeraldinsight.com/2398-6247.htm
Information Discovery and Delivery
45/4 (2017) 169–180
© Emerald Publishing Limited [ISSN 2398-6247]
[DOI 10.1108/IDD-03-2017-0021]
Received 21 March 2017
Revised 12 July 2017
Accepted 8 August 2017
169
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