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Top1. Introduction
With the advent of sensors for collecting environmental data, many sensor ontologies have been developed. A sensor ontology can be defined as 3-tuple 
, where 
, 
and 
are respectively the set of classes, properties and relationships, Which can describe the sensors' capabilities, performance, and usage conditions that allow the discovery of different data depending on the purpose and context (Fernandez et al., 2013). Although sensor ontologies are thought to be a solution to data heterogeneity on the semantic sensor web, the subjectivity of ontology modeling results in the creation of heterogeneous ontologies, which might use different words to name the same concept, use the same word to name different concepts, create hierarchies for a specific domain region with different levels of detail and so on. The arising so-called sensor ontology heterogeneity problem blocks semantic interoperability between various sensor ontologies and limits their applications. Ontology matching is an effective technique to solve the sensor ontology heterogeneity problem by determining the semantically identical entities in heterogeneous sensor ontologies. The obtained sensor ontology alignment 
is a correspondence set, and each correspondence inside is a 4-tuple 
, where 
and 
are the entities of two sensor ontologies. 
is a confidence value holding for the correspondence between 
and 
, and 
is the relationship between 
and 
, which refers to equivalence in this work.
Due to the complex nature of the ontology matching process, evolutionary algorithm (EA) has emerged as a good methodology for computing optimal ontology alignments. However, there exist different aspects of solution that are partially or wholly in conflict, and the single-objective EA may lead to unwanted bias to one of them and reduce the solution's quality. Multi-objective EA (MOEA) estimates different aspect of solutions simultaneously, and produces a set of solutions which contains a number of non-dominated solutions, none of which can be further improved on any one objective without degrading it in another. Ontology matching based on the MOEA is a recently introduced, innovative, and efficient methodology to address the ontology matching problem (Acampora et al., 2014). However, due to the complexity of the ontology matching process, ontology alignments generated by automatic matching tools should be checked by users (Shvaiko & Euzenat, 2013). The technique causes users and automatic tools to cooperate to create high-quality matching through a process called semiautomatic ontology matching (Falconer & Noy, 2011). To further improve the quality of sensor ontology alignment, it’s necessary to make use of the user’s knowledge of the solution’s quality to guide the automatic ontology matcher’s search direction, and based on this motivation, we propose a preference-based multi-objective evolutionary algorithm (PMOEA)-based semiautomatic ontology matching technique. In particular, our contributions are as follows: