Exploiting latent relevance for relational learning of ubiquitous things

With recent advances in radio-frequency identification (RFID), wireless sensor networks, and Web services, physical things are becoming an integral part of the emerging ubiquitous Web. While this integration offers many exciting opportunities such as efficient supply chains and improved environmental monitoring, it also presents many significant challenges. One such challenge lies in how to classify, discover, and manage ubiquitous things, which is critical for efficient and effective object search, recommendation, and composition. In this paper, we focus on automatically classifying ubiquitous things into manageable semantic category labels by exploiting the information hidden in interactions between users and ubiquitous things. We develop a novel approach to extract latent relevance by building a relational network of ubiquitous things (RNUbiT) where similar things are linked via virtual edges according to their latent relevance. A discriminative learning algorithm is also developed to automatically determine category labels for ubiquitous things. We conducted experiments using real-world data and the experimental results demonstrate the feasibility and validity of our proposed approach.