Author(s): Antunes, M. ; Gomes, D. ; Aguiar, R.
Date: 2015
Persistent ID: http://hdl.handle.net/10773/16060
Origin: RIA - Repositório Institucional da Universidade de Aveiro
Subject(s): Context information; Internet of things; M2M; Big data; Internet; Semantics; Web services; Information sources; Real-world information; Semantic features; Semantic similarity; Sensing devices; Technological world; Semantic Web
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.
