Affiliations: Business School, Beijing Normal University, Beijing,
China | College of Information Science and Technology, Beijing
Normal University, Beijing, China | Schoolof Information Engineering, China University of
Geosciences, Beijing, China | Computer Science Department, Institute
Mines-TELECOM/TELECOM SudParis, Paris, France
Note: [] Corresponding author: Rongfang Bie, College of Information
Science and Technology, Beijing Normal University, Beijing, China. E-mail:
rfbie@bnu.edu.cn
Abstract: An exciting paradise of data is emerging into our daily life along
with the development of the Web of Things. Nowadays, volumes of heterogeneous
raw data are continuously generated and captured by trillions of smart devices
like sensors, smart controls, readers and other monitoring devices, while
various events occur in the physical world. It is hard for users including
people and smart things to master valuable information hidden in the massive
data, which is more useful and understandable than raw data for users to get
the crucial points for problems-solving. Thus, how to automatically and
actively extract the knowledge of events and their internal links from the big
data is one key challenge for the future Web of Things. This paper proposes an
effective approach to extract events and their internal links from large scale
data leveraging predefined event schemas in the Web of Things, which starts
with grasping the critical data for useful events by filtering data with
well-defined event types in the schema. A case study in the context of smart
campus is presented to show the application of proposed approach for the
extraction of events and their internal semantic links.
Keywords: Web of Events, Web of Things, restful, information extraction, mobile
