A Rule-Based Reasoner for Underwater Robots Using OWL and SWRL

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.

[1]  Asunción Gómez-Pérez,et al.  Interoperability results for Semantic Web technologies using OWL as the interchange language , 2010, J. Web Semant..

[2]  Georgios Meditskos,et al.  A Rule-Based Object-Oriented OWL Reasoner , 2008, IEEE Transactions on Knowledge and Data Engineering.

[3]  Harold Boley,et al.  Interpreting SWRL Rules in RDF Graphs , 2006, WLFM@FM.

[4]  Sebastian Rudolph,et al.  Modeling in OWL 2 without Restrictions , 2012, OWLED.

[5]  Mustafa Insel,et al.  Expert system applications in marine technologies , 2008 .

[6]  Curt Schurgers,et al.  A swarm of autonomous miniature underwater robot drifters for exploring submesoscale ocean dynamics , 2017, Nature Communications.

[7]  Elias B. Kosmatopoulos,et al.  Real-time adaptive multi-robot exploration with application to underwater map construction , 2016, Auton. Robots.

[8]  Grant Clark,et al.  Two learning approaches for a rule-based intuitive reasoner , 2011, Expert Syst. Appl..

[9]  Yarden Katz,et al.  Pellet: A practical OWL-DL reasoner , 2007, J. Web Semant..

[10]  Junsheng Zhang,et al.  Managing and Refining Rule Set for SWRL , 2008, 2008 4th International Conference on Wireless Communications, Networking and Mobile Computing.

[11]  Joanna Isabelle Olszewska,et al.  Requirements for building an ontology for autonomous robots , 2016, Ind. Robot.

[12]  Jonathan Rodriguez,et al.  SWARMs Ontology: A Common Information Model for the Cooperation of Underwater Robots , 2017, Sensors.

[13]  Gregory R. Olsen,et al.  The configuration design ontologies and the VT elevator domain theory , 1996, Int. J. Hum. Comput. Stud..

[14]  Zongmin Ma,et al.  Storing massive Resource Description Framework (RDF) data: a survey , 2016, The Knowledge Engineering Review.

[15]  H. Lan,et al.  SWRL : A semantic Web rule language combining OWL and ruleML , 2004 .

[16]  Jiafu Wan,et al.  Knowledge Reasoning with Semantic Data for Real-Time Data Processing in Smart Factory , 2018, Sensors.

[17]  Hui Shi,et al.  Evaluating an optimized backward chaining ontology reasoning system with innovative custom rules , 2017 .

[18]  Andreas Papasalouros,et al.  Towards an intelligent tutoring system for environmental decision makers. , 2010 .

[19]  Jian Li,et al.  A semantic representation model for design rationale of products , 2013, Adv. Eng. Informatics.

[20]  Achim J Kopf,et al.  Simple, affordable, and sustainable borehole observatories for complex monitoring objectives , 2014 .

[21]  Mor Peleg,et al.  Using OWL and SWRL to represent and reason with situation-based access control policies , 2011, Data Knowl. Eng..

[22]  Rafael Corchuelo,et al.  Benchmarking Data Exchange among Semantic-Web Ontologies , 2013, IEEE Transactions on Knowledge and Data Engineering.

[23]  Pascal Hitzler,et al.  Towards a Rule Based Distributed OWL Reasoning Framework , 2015, OWLED.

[24]  Ivan Kalaykov,et al.  Underwater Robotics: Surface Cleaning Technics, Adhesion and Locomotion Systems , 2016 .

[25]  Bodo Urban,et al.  An Ontology-Based Approach to Enable Knowledge Representation and Reasoning in Worker-Cobot Agile Manufacturing , 2017, Future Internet.

[26]  Brian McBride,et al.  Jena: Implementing the RDF Model and Syntax Specification , 2001, SemWeb.

[27]  Naomi Kato,et al.  An autonomous underwater robot for tracking and monitoring of subsea plumes after oil spills and gas leaks from seafloor , 2017 .

[28]  Bo Wang,et al.  An Acoustic Communication Time Delays Compensation Approach for Master–Slave AUV Cooperative Navigation , 2017, IEEE Sensors Journal.

[29]  Thomas R. Gruber,et al.  Toward principles for the design of ontologies used for knowledge sharing? , 1995, Int. J. Hum. Comput. Stud..

[30]  Mohammad Eghtesad,et al.  Design, Construction and Control of a Remotely Operated Vehicle (ROV) , 2011 .

[31]  Mieczyslaw M. Kokar,et al.  Using SWRL and OWL to Capture Domain Knowledge for a Situation Awareness Application Applied to a Supply Logistics Scenario , 2005, RuleML.

[32]  Thomas Rauschenbach,et al.  UUV Simulator: A Gazebo-based package for underwater intervention and multi-robot simulation , 2016, OCEANS 2016 MTS/IEEE Monterey.

[33]  Tamas Haidegger,et al.  Extensions to the core ontology for robotics and automation , 2015 .

[34]  Yang Xu,et al.  Ontology-Based Reasoning with Uncertain Context in a Smart Home: A Decision Network Approach , 2015, 2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT).

[35]  David Gil,et al.  Internet of Things: A Review of Surveys Based on Context Aware Intelligent Services , 2016, Sensors.

[36]  Keqing He,et al.  Customization of Service-Oriented Domain Models Using SWRL , 2014, 2014 IEEE International Conference on Services Computing.

[37]  Yong Se Kim,et al.  A Teaching Strategies Engine Using Translation from SWRL to Jess , 2006, Intelligent Tutoring Systems.

[38]  Lun Li,et al.  A Fuzzy MEBN Ontology Language Based on OWL2 , 2016, FSDM.

[39]  Bijan Parsia,et al.  Modularity and Web Ontologies , 2006, KR.

[40]  Miguel-Ángel Sicilia,et al.  Integrating reasoning and clinical archetypes using OWL ontologies and SWRL rules , 2011, J. Biomed. Informatics.

[41]  Markus Kucera,et al.  Ontologies used in robotics: A survey with an outlook for automated driving , 2017, 2017 IEEE International Conference on Vehicular Electronics and Safety (ICVES).

[42]  Edin Omerdic,et al.  Inspection-Class Remotely Operated Vehicles—A Review , 2017 .

[43]  Satyandra K. Gupta,et al.  Overview of an Ontology-Based Approach for Kit Building Applications , 2017, 2017 IEEE 11th International Conference on Semantic Computing (ICSC).

[44]  Grzegorz J. Nalepa,et al.  Pellet-HeaRT - Proposal of an Architecture for Ontology Systems with Rules , 2010, KI.

[45]  Joe Tekli,et al.  An Overview on XML Semantic Disambiguation from Unstructured Text to Semi-Structured Data: Background, Applications, and Ongoing Challenges , 2016, IEEE Transactions on Knowledge and Data Engineering.

[46]  Neelam Duhan,et al.  Developing human family tree using SWRL rules , 2016, 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom).

[47]  Adolfo Lozano Tello,et al.  Ontology and SWRL-Based Learning Model for Home Automation Controlling , 2010, ISAmI.

[48]  Mihaela Hnatiuc,et al.  The ROV communication and control , 2017, 2017 IEEE 23rd International Symposium for Design and Technology in Electronic Packaging (SIITME).

[49]  Jaroslaw Bak,et al.  Rule-Based Reasoning System for OWL 2 RL Ontologies , 2014, ICCCI.

[50]  Khaleel Ur Rahman Khan,et al.  SemRPer - A Rule based Personalization System for Semantic Web , 2015, Int. J. Web Appl..

[51]  Bidyadhar Subudhi,et al.  The state of art of Autonomous Underwater Vehicles in current and future decades , 2014, 2014 First International Conference on Automation, Control, Energy and Systems (ACES).

[52]  Kanda Runapongsa Saikaew,et al.  A Comparative Study of Rule-Based Inference Engines for the Semantic Web , 2018, IEICE Trans. Inf. Syst..

[53]  Justin E. Manley,et al.  Unmanned Maritime Vehicles, 20 years of commercial and technical evolution , 2016, OCEANS 2016 MTS/IEEE Monterey.

[54]  Benjamin N. Grosof,et al.  Supporting Rule System Interoperability on the Semantic Web with SWRL , 2005, SEMWEB.

[55]  Samson W. Tu,et al.  Querying the Semantic Web with SWRL , 2007, RuleML.

[56]  Néstor Lucas Martínez,et al.  A Mission Planning Approach for Precision Farming Systems Based on Multi-Objective Optimization , 2018, Sensors.

[57]  Daeyoung Park,et al.  Merged Ontology and SVM-Based Information Extraction and Recommendation System for Social Robots , 2017, IEEE Access.

[58]  Peter F. Patel-Schneider,et al.  A Syntax for Rules in OWL 2 , 2009, OWLED.

[59]  Sergio M. Jesus,et al.  Overview and first year progress of the Widely scalable Mobile Underwater Sonar Technology H2020 project , 2016 .

[60]  Li Yan,et al.  A Formal Approach of Construction Fuzzy XML Data Model Based on OWL 2 Ontologies , 2018, IEEE Access.

[61]  Csongor Nyulas,et al.  The SWRLAPI: A Development Environment for Working with SWRL Rules , 2008, OWLED.

[62]  Suresh Kumar,et al.  OWL, RDF, RDFS inference derivation using Jena semantic framework & pellet reasoner , 2014, 2014 International Conference on Advances in Engineering & Technology Research (ICAETR - 2014).

[63]  Yan Sun,et al.  A Habit-Based SWRL Generation and Reasoning Approach in Smart Home , 2015, 2015 IEEE 21st International Conference on Parallel and Distributed Systems (ICPADS).

[64]  Alexander Fritze,et al.  An Approach to Automated Fusion System Design and Adaptation , 2017, Sensors.

[65]  Robert Bogue,et al.  Underwater robots: a review of technologies and applications , 2015, Ind. Robot.

[66]  Konrad Grzanek,et al.  Forward Chaining with State Monad , 2016, ICAISC.

[67]  Dave Elliman,et al.  Ontology languages for the semantic web: A never completely updated review , 2006, Knowl. Based Syst..