Ontology forecasting in scientific literature:semantic concepts prediction based on innovation-adoption priors


The ontology engineering research community has focused for many years on supporting the creation, development and evolution of ontologies. Ontology forecasting, which aims at predicting semantic changes in an ontology, represents instead a new challenge. In this paper, we want to give a contribution to this novel endeavour by focusing on the task of forecasting semantic concepts in the research domain. Indeed, ontologies representing scientific disciplines contain only research topics that are already popular enough to be selected by human experts or automatic algorithms. They are thus unfit to support tasks which require the ability of describing and exploring the forefront of research, such as trend detection and horizon scanning. We address this issue by introducing the Semantic Innovation Forecast (SIF) model, which predicts new concepts of an ontology at time t + 1, using only data available at time t. Our approach relies on lexical innovation and adoption information extracted from historical data. We evaluated the SIF model on a very large dataset consisting of over one million scientific papers belonging to the Computer Science domain: the outcomes show that the proposed approach offers a competitive boost in mean average precision-at-ten compared to the baselines when forecasting over 5 years.

Publication DOI: https://doi.org/10.1007/978-3-319-49004-5_4
Divisions: College of Business and Social Sciences > Aston Business School
Event Title: 20th International Conference on Knowledge Engineering and Knowledge Management
Event Type: Other
Event Dates: 2016-11-19 - 2016-11-23
Uncontrolled Keywords: adoption priors,innovation priors,latent semantics,LDA,ontology evolution,ontology forecasting,scholarly data,topic evolution,Theoretical Computer Science,Computer Science(all)
ISBN: 978-3-319-49003-8, 978-3-319-49004-5
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Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Conference contribution
Published Date: 2016-11-04
Published Online Date: 2016-11-04
Accepted Date: 2016-04-01
Authors: Cano-Basave, Amparo Elizabeth
Osborne, Francesco
Salatino, Angelo Antonio



Version: Accepted Version

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