Abstract
Due to the problem of data heterogeneity in the semantic sensor networks, the communications among different sensor network applications are seriously hampered. Although sensor ontology is regarded as the state-of-the-art knowledge model for exchanging sensor information, there also exists the heterogeneity problem between different sensor ontologies. Ontology matching is an effective method to deal with the sensor ontology heterogeneity problem, whose kernel technique is the similarity measure. How to integrate different similarity measures to determine the alignment of high quality for the users with different preferences is a challenging problem. To face this challenge, in our work, a Multiobjective Evolutionary Algorithm (MOEA) is used in determining different nondominated solutions. In particular, the evaluating metric on sensor ontology alignment’s quality is proposed, which takes into consideration user’s preferences and do not need to use the Reference Alignment (RA) beforehand; an optimization model is constructed to define the sensor ontology matching problem formally, and a selection operator is presented, which can make MOEA uniformly improve the solution’s objectives. In the experiment, the benchmark from the Ontology Alignment Evaluation Initiative (OAEI) and the real ontologies of the sensor domain is used to test the performance of our approach, and the experimental results show the validity of our approach.
Highlights
Nowadays, sensors are playing a more and more vital role in the distributed systems, and the sensor network has been broadly used in many areas of society, such as military, industrial, smart home, and many other fields [1, 2]
To meet different users’ requirements, this paper proposes a multiobjective sensor ontology matching technique, which uses the Multiobjective Evolutionary Algorithm (MOEA) to simultaneously optimize the inflection point solutions [18], i.e., three solutions with the best recall, precision, and f -measure [19]
The sensor ontology is defined as a triple O = ðC, P, IÞ, where C is the set of classes, P is the set of properties, and I is the set of instances
Summary
Sensors are playing a more and more vital role in the distributed systems, and the sensor network has been broadly used in many areas of society, such as military, industrial, smart home, and many other fields [1, 2]. Because GOAL needs to obtain the reference matching result in advance, so it is difficult to apply it in practical applications To overcome this drawback, this work proposes user preference-based evaluation metrics on sensor ontology alignment’s quality, which is able to work without the RA. The rest of the paper is organized as follows: Section 2 devoted to introducing the basic concepts on the sensor ontology and the evaluation metrics on ontology alignment; Section 3 gives an optimization model and describes how to use MOEA/D to select the user’s three preferred solutions; Section 4 presents the experimental results and analysis; Section 5 draws the conclusion and presents the future work
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