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Modeling Latent Relation to Boost Things Categorization Service
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DOI：10.1109/TSC.2017.2715159.png)
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摘要
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While it is well understood that the Internet of things (IoT) offers the capability of integrating the physical world and the cyber world, it also presents many significant challenges with numerous heterogeneous things connected and interacted, such as how to efficiently annotate things with semantic labels (i.e., things categorization) for searching and recommendation. Traditional ways for things categorization are not effective due to several characteristics (e.g., thing's text profiles are usually short and noise, things are heterogeneous in terms of functionality and attributes) of IoT. In this paper, we develop a novel things categorization technique to automatically predict semantic labels for a given thing. Our proposed approach formulates things categorization as a multi-label classification problem and learns a binary support vector machine classifier for each label to support multi-label classification. We extract two types of features to train classification model: 1) explicit feature from thing's profiles and spatial-temporal pattern; 2) implicit feature from thing's latent relation strength. We utilize a latent variable model to uncover thing's latent relation strength from their interaction behaviours. We conduct a comprehensive experimental study based on three real datasets, and the results show fusing thing's latent relation strength can significantly boost things categorization.
Keyword:
Feature extraction
Hidden Markov models
Semantics
Support vector machines
Internet of Things
Intelligent sensors
Things categorization
multi-label classification
interaction behaviours
latent variable model
internet of things
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