A decade of Semantic Web research through the lenses of a mixed methods approach

Article type: Research Article

Authors: Kirrane, Sabrina^{a; *} | Sabou, Marta^b | Fernández, Javier D.^a | Osborne, Francesco^c | Robin, Cécile^d | Buitelaar, Paul^d | Motta, Enrico^c | Polleres, Axel^a

Affiliations: [a] Vienna University of Economics and Business, Austria. E-mails: [email protected], [email protected], [email protected] | [b] Vienna University of Technology, Austria. E-mail: [email protected] | [c] Knowledge Media institute (KMi), The Open University, UK. E-mails: [email protected], [email protected] | [d] Insight Centre for Data Analytics, National University of Ireland, Galway, Ireland. E-mails: [email protected], [email protected]

Correspondence: [*] Corresponding author. E-mail: [email protected].

Abstract: The identification of research topics and trends is an important scientometric activity, as it can help guide the direction of future research. In the Semantic Web area, initially topic and trend detection was primarily performed through qualitative, top-down style approaches, that rely on expert knowledge. More recently, data-driven, bottom-up approaches have been proposed that offer a quantitative analysis of the evolution of a research domain. In this paper, we aim to provide a broader and more complete picture of Semantic Web topics and trends by adopting a mixed methods methodology, which allows for the combined use of both qualitative and quantitative approaches. Concretely, we build on a qualitative analysis of the main seminal papers, which adopt a top-down approach, and on quantitative results derived with three bottom-up data-driven approaches (Rexplore, Saffron, PoolParty), on a corpus of Semantic Web papers published between 2006 and 2015. In this process, we both use the latter for “fact-checking” on the former and also to derive key findings in relation to the strengths and weaknesses of top-down and bottom-up approaches to research topic identification. Although we provide a detailed study on the past decade of Semantic Web research, the findings and the methodology are relevant not only for our community but beyond the area of the Semantic Web to other research fields as well.

Keywords: Research topics, research trends, linked data, Semantic Web, scientometrics

DOI: 10.3233/SW-200371

Journal: Semantic Web, vol. 11, no. 6, pp. 979-1005, 2020