Building Entity Graphs for the Web of Things Management

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. Finding correlations among ubiquitous things is a crucial prerequisite for many important applications such as things search, discovery, classification, recommendation, and composition. This article building an entity graph for Web of Things, whereas we propose a novel graph-based approach for discovering underlying connections of things via mining the rich content embodied in the human-thing interactions in terms of user, temporal and spatial information. We model this various information using two graphs, namely a spatiotemporal graph and a social graph. Then, random walk with restart (RWR) is applied to find proximities among things, a relational graph of things, entity graph, indicating implicit correlations of things is learned. The correlation analysis lays a solid foundation contributing to improved effectiveness in things management and analytics. To demonstrate the utility of the proposed approach, we present two typical applications and a systematic case study in regards to a flexible feature-based classification framework and a unified probabilistic factor based framework, respectively. Our evaluation exhibits the strength and feasibility of approach.