Semantic features for context organization

Mário Antunes, Diogo Gomes, Rui L. Aguiar, "Semantic features for context organization", Proc. 3rd edition Future Internet of Things and Cloud (FiCloud-2015), Rome, Aug 2015

Tags: Context Information, Internet of Things, M2M, Machine Learning, text mining, Unsupervised Learning


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. In previous publications we discussed efficient ways to organize context information that is independent of structure and representation. However, our previous solution suffers from semantic sensitivity. In this paper we review semantic methods that can be used to minimize this issue, and propose an unsupervised semantic similarity solution that combines distributional profiles with public web services. Our solution was evaluated against Miller-Charles dataset, achieving a correlation of 0.6.


Conference: 3rd edition Future Internet of Things and Cloud (FiCloud-2015) in Rome