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

Amparo Elizabeth Cano-Basave, Francesco Osborne*, Angelo Antonio Salatino

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference publication

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.

Original languageEnglish
Title of host publicationKnowledge Engineering and Knowledge Management
Subtitle of host publication20th International Conference, EKAW 2016, Bologna, Italy, November 19-23, 2016, Proceedings
EditorsEva Blomqvist, Paulo Ciancarini, Francesco Poggi, Fabio Vitali
Place of PublicationCham (CH)
PublisherSpringer
Pages51-67
Number of pages17
ISBN (Electronic)978-3-319-49004-5
ISBN (Print)978-3-319-49003-8
DOIs
Publication statusE-pub ahead of print - 4 Nov 2016
Event20th International Conference on Knowledge Engineering and Knowledge Management - Bologna, Italy
Duration: 19 Nov 201623 Nov 2016

Publication series

NameLecture Notes in Computer Science
PublisherSpinger
Volume10024
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference20th International Conference on Knowledge Engineering and Knowledge Management
Abbreviated titleEKAW 2016
CountryItaly
CityBologna
Period19/11/1623/11/16

Keywords

  • adoption priors
  • innovation priors
  • latent semantics
  • LDA
  • ontology evolution
  • ontology forecasting
  • scholarly data
  • topic evolution

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