Attributes Extraction for Fine-grained Differentiation of the Internet of Things Patterns

The Internet of Things (IoT) is a paradigm with multitudes of design patterns. However, in order to use these patterns quickly and effectively, one must be able to make a differentiation between the existing patterns. At the moment, there is no known catalogue for the IoT patterns in which each pattern is described at a fine-grained level, i.e. in terms of its attributes. The need to discuss these patterns in terms of their attributes is important as it enables ease of understanding and allows us to group related patterns together for speedy retrieval. In this paper, we present an attributes extraction system which generates a list of attributes for a given IoT pattern. The attributes extraction system is based on identification and extraction of important sentences which describe the core properties of the given IoT pattern. The system uses multiple linguistics features to identify the most important sentences in a document with regard to describing the core essence of a given pattern. The system calculates an independent score for each sentence per feature. Through aggregation, the independent scores for each feature can then be combined to give a weighted mean score for each sentence. The evaluation results show that the attributes selected by the system are consistent with human ranking in the bulk of the examined documents.