A crowdsourcing-based topic model for service matchmaking in Internet of Things

The Internet of Things (IoT) provide intelligence for the communication between people and physical objects. An important and critical issue in the IoT service applications is how to match the suitable IoT services with service requests. To solve this problem, researchers use semantic modeling methods to make service matching. Semantic modeling methods in IoT extract meta-data from text using rule-based approaches or machine learning techniques often suffer from the scalability and sparseness since text provided by sensors is short and unstructured. In recent years, topic modeling has been used in IoT service matchmaking. However, most topic modeling methods do not perform well in IoT service matchmaking since the text is too short. In order to address the issues, this paper proposes a new topic modeling method to extract topic signatures provided by intelligent devices. The method extends the classical knowledge representation framework and improves the qualities of service information extraction, and this process is able to improve the effectiveness of service matchmaking in IoT service. The framework incorporates human cognition to improve the effectiveness of the algorithm and make the algorithm more robust in heterogeneous systems in the IoT. The usefulness of the method is illustrated via experiments using real datasets. (C) 2018 Elsevier B.V. All rights reserved.