Approximate Semantic Matching over Linked Data Streams

Abstract

Abstract. In the Internet of Things (IoT), data can be generated by all kinds of smart things. In such context, enabling machines to process and understand such data is critical. Semantic Web technologies, such as Linked Data, provide an effective and machine-understandable way to represent IoT data for further processing. It is a challenging issue to match Linked Data streams semantically based on text similarity as text similarity computation is time consuming. In this paper, we present a hashing-based approximate approach to efficiently match Linked Data streams with users' needs. We use the Resource Description Framework (RDF) to represent IoT data and adopt triple patterns as user queries to describe users' data needs. We then apply locality-sensitive hashing techniques to transform semantic data into numerical values to support efficient matching between data and user queries. We design a modified k nearest neighbors (kNN) algorithm to speedup the matching process. The experimental results show that our approach is up to five times faster than the traditional methods and can achieve high precisions and recalls.

An Effective Semantic Event Matching System in the Internet of Things (IoT) Environment

“…The performance efficiency decreases if the number of attributes that are to be matched to their approximate matches increases. Then, in [ 38 ], the authors used the k nearest neighbors (kNN) algorithm to speed up the matching process. The kNN algorithm should be trained manually beforehand for different patterns or types of queries to be matched accurately.…”