Forecasting the Spreading of Technologies in Research Communities

Abstract

Technologies such as algorithms, applications and formats are an important part of the knowledge produced and reused in the research process. Typically, a technology is expected to originate in the context of a research area and then spread and contribute to several other fields. For example, Semantic Web technologies have been successfully adopted by a variety of fields, e.g., Information Retrieval, Human Computer Interaction, Biology, and many others. Unfortunately, the spreading of technologies across research areas may be a slow and inefficient process, since it is easy for researchers to be unaware of potentially relevant solutions produced by other research communities. In this paper, we hypothesise that it is possible to learn typical technology propagation patterns from historical data and to exploit this knowledge i) to anticipate where a technology may be adopted next and ii) to alert relevant stakeholders about emerging and relevant technologies in other fields. To do so, we propose the Technology-Topic Framework, a novel approach which uses a semantically enhanced technology-topic model to forecast the propagation of technologies to research areas. A formal evaluation of the approach on a set of technologies in the Semantic Web and Artificial Intelligence areas has produced excellent results, confirming the validity of our solution.

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

“…The Technology-Topic Framework [24] is an approach that characterises technologies according to their propagation through research topics drawn from CSO, and uses this representation to forecast the future propagation of novel technologies across research fields. The aim is to suggest promising technologies to scholars and accelerate the flow of knowledge from one community to another and the pace of technology propagation.…”

The evolution of IJHCS and CHI: A quantitative analysis

“…Mobile Computing grew from 142 publications in CHI during 2009 (14 in IJHCS) to 222 in 2018 (18 in IJHCS, with a peak of 23 in 2017). Its most active subtopics in this period include Smartphones (10 to 61 in CHI), Wearable Computing (15,62), and Mobile Applications (3,19).…”

Pragmatic Ontology Evolution: Reconciling User Requirements and Application Performance

“…For example, we could map the concept temporal logic to each book that contains one of the alternative labels (e.g., "temporal logics") or sub-concepts (e.g., "temporal operators") in the CSO ontology. This technique has been applied with good results in a variety of fields, such as automatic classification of proceedings [2], technology forecasting [6], recommender systems [3,13], community detection [5], and others.…”

“…This is indeed the case with CSO, which was previously tested on several tasks [24], including automatic tagging of scientific publications [1], recommendation generation [2], clustering [5], and technology forecasting [6]. Alternatively, we suggest to pre-filter the candidate concepts [16] or to generate a task-specific gold standard.…”

“…Indeed, ontologies have proved to be very useful in the context of a variety of tasks [1], including the integration of data from different sources, domain reasoning, classification [2], generation of recommendations [3], cluster analysis [4], community detection [5], sentiment analysis, forecasting [6], and others. Naturally, ontologies need to be regularly maintained and need to evolve according to changes in the domain or new requirements from users or applications [7].…”