Abstract
The generation of high quality and re-usable ontologies depends on effective methodologies aimed at supporting the crucial process of identifying the ontology requirements, in terms of the number of potential end-users and end-users’ informational needs. It is widely recognized that the exploitation of competency questions (CQs) plays an important role in this requirement definition phase. In this paper, we aim at introducing a new general approach to exploit (web) search trends, and the huge amount of searches that people make every-day with web search engines, as a pivotal complementary source of information for the identification of informal needs of large numbers of end-users. To achieve this goal we use the “auto-suggest” results provided by search engines like Bing and Google as a goldmine of data and insights. We select a set of keywords to identify the ontology terminology, and we collect and analyze a huge amount of web search queries (WSQs) related to the selected set of keywords. In turn, we identify the search trends related to the collected WSQs and we show how the corpus of selected WSQs can be used to assess the usage likelihood of a selected ontology w.r.t. the identified (web) search trends. The experimental results are used to discuss the practical utility of the proposed approach. Copyright © 2020 for this paper by its authors.