Abstract
Web Ontology Language (OWL) is designed to represent varied knowledge about things and the relationships of things. It is widely used to express complex models and address information heterogeneity of specific domains, such as underwater environments and robots. With the help of OWL, heterogeneous underwater robots are able to cooperate with each other by exchanging information with the same meaning and robot operators can organize the coordination easier. However, OWL has expressivity limitations on representing general rules, especially the statement “If … Then … Else …”. Fortunately, the Semantic Web Rule Language (SWRL) has strong rule representation capabilities. In this paper, we propose a rule-based reasoner for inferring and providing query services based on OWL and SWRL. SWRL rules are directly inserted into the ontologies by several steps of model transformations instead of using a specific editor. In the verification experiments, the SWRL rules were successfully and efficiently inserted into the OWL-based ontologies, obtaining completely correct query results. This rule-based reasoner is a promising approach to increase the inference capability of ontology-based models and it achieves significant contributions when semantic queries are done.
Highlights
Underwater environment is characteristically, dynamic and dangerous, leading to several difficulties for human beings to perform underwater operations
This paper introduces the Ontology Web Language (OWL)-based SWARMs ontology as an information model to to enable heterogeneous robotic vehicles to obtain a common understanding of shared knowledge
Enable heterogeneous robotic vehicles to obtain a common understanding of shared knowledge
Summary
Underwater environment is characteristically, dynamic and dangerous, leading to several difficulties for human beings to perform underwater operations. The SWARMs ontology is a Common Information Model and represents several domain-specific knowledge, including communication domain, environment domain, mission planning domain and robotic vehicle domain. The advantage of modeling SWARMs platform with OWL is that OWL is capable for representing the exchanged information between robotic vehicles and enabling robotic vehicles to share and reuse different knowledge In such networked ontology, it is more convenient for users to search desired resources and deliver the information to computers for further processing. With the successful insertion of SWRL rules, the SWARMs ontology is enhanced with expression capabilities and could provide the SWARMs users with a precise query service by running the rule-based reasoner. The query results generated by the rule-based reasoner are able to help the SWARMs users to take immediate actions in order to make a better decision, obtain higher benefits and avoid economic loss.
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