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. 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 MillerCharles dataset, achieving a correlation of 0.6.
Our previous solution REF minimizes this issue using public web services to gather corpus.
Semantic Features for Context Organization
2015 3rd International Conference on Future Internet of Things and Cloud
2015 3rd International Conference on Future Internet of Things and Cloud
Computer Science