Expert profiling for collaborative innovation: big data perspective. Issue 4 (20th November 2017)

• Record Type: Journal Article
• Authors: Silva, Thushari; Ma, Jian
• Is Part Of: Information discovery and delivery. Volume 45:Issue 4(2017)
• Page Start: 169
• Page End: 180
• Publication Date: 2017-11-20
• Subjects: Data mining -- Crowdsourcing -- Data integration -- Big data -- Knowledge discovery -- Expert profiling; Information retrieval -- Periodicals; Document delivery -- Periodicals; Digital libraries -- Periodicals; Information storage and retrieval systems -- Periodicals; 025.524
• DOI: 10.1108/IDD-03-2017-0021
• Languages: English
• ISSNs: 2398-6247

Abstract

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.