Expert profiling for collaborative innovation: big data perspective

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.

Conceptual Framework of Kxpert:Knowledge-Based Information Retrieval for Expert Profiling

“…Although there are many frameworks and models on expert profiling in previous works, only a few are found to be closely related to the needs of the case university. As discussed in the previous section, the main frameworks being selected for further analysis in this research are the expert finding (Balog et al, 2012) and expert profiling (Silva & Ma, 2017), after thorough considerations on other relevant frameworks. Table 2 shows the comparison between these two frameworks, and the considerations taken into account in this study.…”

“…Since this study is specific on certain criteria in finding and profiling knowledge experts, a different approach is focused on. Among the existing frameworks that are relevant to this study are knowledge-based framework (Rodrigues et al, 2015; Zukhi et al, 2020), expert finding (Balog et al, 2012), and expert profiling (Silva & Ma, 2017). Overall, these frameworks promise reliable and secure processes that link people in a system to facilitate knowledge seekers in finding the right knowledge experts.…”

Feasibility of activity-based expert profiling using text mining of scientific publications and patents

“…The success of collaborating, its innovativeness and creativity depends on several factors and varies from field to field (Chin-Dusting et al 2005;Cope et al 2018). However, it is particularly important to find suitable partners for the purpose at hand (Silva and Ma 2017;Borge and Bröring 2017).…”

“…These measures, especially the h-index, while being criticized by the research community, are used as representations of the "scientific standing of authors, affecting, among others, grant allocation and career advancement" (Brink 2013;Harzing and Alakangas 2016;Kuan et al 2011;Teixeira da Silva 2017). A variety of expert recommendation approaches utilizes expert profiling and research evaluation (Silva and Ma 2017;Balog et al 2012), among them well-known representatives such as AMiner 1 (Tang et al 2008), ExpertSeer (Chen et al 2015), BMExpert 2 (Wang et al 2015) and Jane 3 (Schuemie and Kors 2008).…”

“…(1) The lack of transparency of user profiling in expert recommendation "could result in the loss of trustworthiness" (Silva and Ma 2017;Serdyukov et al 2011;. It is uncommon to provide meaningful profiling of expertise besides plain ranking lists of experts (Chen et al 2015).…”