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.

Cano-Basave, A., Osborne, F., Salatino, A. (2016). Ontology forecasting in scientific literature: Semantic concepts prediction based on innovation-adoption priors. In Knowledge Engineering and Knowledge Management. EKAW 2016 (pp.51-67). Springer Verlag [10.1007/978-3-319-49004-5_4].

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

Osborne F;
2016

Abstract

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.
paper
Adoption priors; Innovation priors; Latent semantics; LDA; Ontology evolution; Ontology forecasting; Scholarly data; Topic evolution;
English
20th International Conference on Knowledge Engineering and Knowledge Management, EKAW 2016 - 19 November 2016 through 23 November 2016
2016
Ciancarini, P; Poggi, F; Vitali, F; Blomqvist, E
Knowledge Engineering and Knowledge Management. EKAW 2016
978-3-319-49003-8
2016
10024 LNAI
51
67
https://link.springer.com/chapter/10.1007/978-3-319-49004-5_4
none
Cano-Basave, A., Osborne, F., Salatino, A. (2016). Ontology forecasting in scientific literature: Semantic concepts prediction based on innovation-adoption priors. In Knowledge Engineering and Knowledge Management. EKAW 2016 (pp.51-67). Springer Verlag [10.1007/978-3-319-49004-5_4].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/381535
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