European Knowledge Acquisition Workshop

EKAW 2016: Knowledge Engineering and Knowledge Management pp 51-67

Ontology Forecasting in Scientific Literature: Semantic Concepts Prediction Based on Innovation-Adoption Priors

  • Amparo Elizabeth Cano-Basave
  • Francesco Osborne
  • Angelo Antonio Salatino
Conference paper

DOI: 10.1007/978-3-319-49004-5_4

Volume 10024 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Cano-Basave A.E., Osborne F., Salatino A.A. (2016) Ontology Forecasting in Scientific Literature: Semantic Concepts Prediction Based on Innovation-Adoption Priors. In: Blomqvist E., Ciancarini P., Poggi F., Vitali F. (eds) Knowledge Engineering and Knowledge Management. EKAW 2016. Lecture Notes in Computer Science, vol 10024. Springer, Cham

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.

Keywords

Topic evolution Ontology forecasting Ontology evolution Latent semantics LDA Innovation priors Adoption priors Scholarly data 

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Amparo Elizabeth Cano-Basave
    • 1
  • Francesco Osborne
    • 2
  • Angelo Antonio Salatino
    • 2
  1. 1.Aston Business SchoolAston UniversityBirminghamUK
  2. 2.Knowledge Media InstituteOpen UniversityMilton KeynesUK