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 Learning Semantic Features from Web Services
Please use this identifier to cite or link to this item http://hdl.handle.net/10773/21421

title: Learning Semantic Features from Web Services
authors: Antunes, Mário
Gomes, Diogo
Aguiar, Rui
issue date: 2016
publisher: IEEE
abstract: In recent years the technological world has grown by incorporating billions of small sensing devices, collecting and sharing real-world information. As the number of such devices grows, it becomes increasingly difficult to manage all these new information sources. There is no uniform way to share, process and understand context information. It is our personal belief that IoT and M2M scenarios will only achieve their full potential when all the devices will work and learn together without human interaction. In this paper we review the most relevant semantic metrics and propose a new unsupervised model that minimizes sense-conflation problem. Our solution was evaluated against Miller-Charles dataset, outperforming our previous work in every metric.
URI: http://hdl.handle.net/10773/21421
ISBN: 978-1-5090-4052-0
publisher version/DOI: https://doi.org/10.1109/FiCloud.2016.46
source: Future Internet of Things and Cloud (FiCloud), 2016 IEEE 4th International Conference on
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