| 47330 |
Creator |
166ff8803e7e4bc39bf57257c1241a04 |
| 47330 |
Creator |
21e3abf33e3daaa89c07ea7d5da24bb0 |
| 47330 |
Creator |
fafb9680e3053ad740ec3e44d38f5000 |
| 47330 |
Creator |
25b3b10b9da03c08922eae28b1249552 |
| 47330 |
Date |
2016 |
| 47330 |
Is Part Of |
repository |
| 47330 |
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. |
| 47330 |
authorList |
authors |
| 47330 |
presentedAt |
ext-5dd44f92bb6a665f950eeaf9d1a87c01 |
| 47330 |
status |
peerReviewed |
| 47330 |
uri |
http://data.open.ac.uk/oro/document/506624 |
| 47330 |
uri |
http://data.open.ac.uk/oro/document/506626 |
| 47330 |
uri |
http://data.open.ac.uk/oro/document/506641 |
| 47330 |
uri |
http://data.open.ac.uk/oro/document/506642 |
| 47330 |
uri |
http://data.open.ac.uk/oro/document/506643 |
| 47330 |
uri |
http://data.open.ac.uk/oro/document/506644 |
| 47330 |
uri |
http://data.open.ac.uk/oro/document/514118 |
| 47330 |
volume |
10024 |
| 47330 |
type |
AcademicArticle |
| 47330 |
type |
Article |
| 47330 |
label |
Cano Basave, Amparo ; Osborne, Francesco and Salatino, Angelo (2016). Ontology
Forecasting in Scientific Literature: Semantic Concepts Prediction based on Innovation-Adoption
Priors. In: Knowledge Engineering and Knowledge Management, Lecture Notes in Computer
Science, pp. 51–67. |
| 47330 |
label |
Cano Basave, Amparo ; Osborne, Francesco and Salatino, Angelo (2016). Ontology
Forecasting in Scientific Literature: Semantic Concepts Prediction based on Innovation-Adoption
Priors. In: Knowledge Engineering and Knowledge Management, Lecture Notes in Computer
Science, pp. 51–67. |
| 47330 |
Title |
Ontology Forecasting in Scientific Literature: Semantic Concepts Prediction based
on Innovation-Adoption Priors |
| 47330 |
in dataset |
oro |