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

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.

The Computer Science Ontology: A Comprehensive Automatically-Generated Taxonomy of Research Areas

“…In the rest of the paper, we will refer to this corpus as the Rexplore data set [7]. Some parts of the ontology were later refined manually by domain experts during the preparation of two ontology-assisted surveys in the fields of Semantic Web [36] and Software Architecture [16].…”

“…The approach adopting CSO yielded the highest average agreement and also obtained the highest agreement with three out of six domain experts. EDAM and CSO have been used in two recent reviews in the field of Human-Computer Interaction (HCI) [43] and Semantic Web [36]. The first study [43] focuses the evolution of HCI in the last 50 years and analyzes the papers published by the International Journal of Human-Computer Studies (IJHCS, active since 1969) and the Conference on Human Factors in Computing Systems (CHI, active since 1982).…”

The Computer Science Ontology: A Large-Scale Taxonomy of Research Areas

“…This consists of about 16 million publications, mainly in the field of Computer Science. Some relationships were also refined manually by domain experts during the preparation of two ontology-assisted surveys in the fields of Semantic Web [23] and Software Architecture [13].…”

The evolution of IJHCS and CHI: A quantitative analysis

“…CSO was produced by applying the Klink-2 algorithm on a large corpus of publications [13] and has been used to support a range of applications and methods for community detection, trend forecasting, and paper classification -see [4] for a comprehensive review of the application of CSO to a variety of tasks. It was also adopted by two mapping studies in the fields of Semantic Web [14] and Software Architecture [10].…”

“…Live Streaming Autonomous Vehicles reflected by related topics, such as Cryptography (20 to 47 in CHI), Authentication Systems (14,26), Privacy Concerns (6,16), Computer Crime (5,14), and Personal Data (0, 9).…”