Abstract
The heterogeneity problem among different sensor ontologies hinders the interaction of information. Ontology matching is an effective method to address this problem by determining the heterogeneous concept pairs. In the matching process, the similarity measure serves as the kernel technique, which calculates the similarity value of two concepts. Since none of the similarity measures can ensure its effectiveness in any context, usually, several measures are combined together to enhance the result’s confidence. How to find suitable aggregating weights for various similarity measures, i.e., ontology metamatching problem, is an open challenge. This paper proposes a novel ontology metamatching approach to improve the sensor ontology alignment’s quality, which utilizes the heterogeneity features on two ontologies to tune the aggregating weight set. In particular, three ontology heterogeneity measures are firstly proposed to, respectively, evaluate the heterogeneity values in terms of syntax, linguistics, and structure, and then, a semiautomatically learning approach is presented to construct the conversion functions that map any two ontologies’ heterogeneity values to the weights for aggregating the similarity measures. To the best of our knowledge, this is the first time that heterogeneity features are proposed and used to solve the sensor ontology metamatching problem. The effectiveness of the proposal is verified by comparing with using state-of-the-art ontology matching techniques on Ontology Alignment Evaluation Initiative (OAEI)’s testing cases and two pairs of real sensor ontologies.
Highlights
A sensor network is composed of various sensors
To support the semantic interaction between intelligent systems based on sensor ontology, we need to determine the correspondence between their heterogeneous concepts in sensor ontologies, which is the so-called sensor ontology matching [8]
This paper proposes a novel ontology metamatching approach to face this challenge, which uses the heterogeneity features of two ontologies to semiautomatically tune the weights for aggregating different similarity measures
Summary
A sensor network is composed of various sensors. In order to realize the information integration and communication between multiple sensor networks, a semantic sensor web is born, which is composed of the semantic web and the sensor network. There have been many sensor ontologies, such as SensorOntology2009 (https://www.w3 .org/2005/Incubator/ssn/wiki/SensorOntology2009), IoTLite (https://www.w3.org/Submission/2015/SUBM-iot-lite20151126/), original SSN (Semantic Sensor Network) (https://www.w3.org/2005/Incubator/ssn/wiki/SSN#Sensor), new SSN (https://www.w3.org/ns/ssn/), and SOSA (Sensor, Observation, Sample, and Actuator) (https://www.w3.org/ ns/sosa/) These ontologies can represent the function, performance, and usage conditions of sensors, which can provide different data for different purposes and contexts [4, 5]. The training process of conversion function is shown, where Or and Oi represent the ith pair of ontologies for training (Or is the reference ontology and Oi is a target ontology), and Wi, Wi′, and Wi′′ are the ith set of weights for aggregating the syntax-based, linguistics-based, and structure-based similarity measures, respectively, which are given by experts and have the best effectiveness to measure the ith pair of ontologies.
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