{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T10:17:37Z","timestamp":1779358657432,"version":"3.51.4"},"reference-count":37,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2019,2,27]],"date-time":"2019-02-27T00:00:00Z","timestamp":1551225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51679180"],"award-info":[{"award-number":["51679180"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51709218"],"award-info":[{"award-number":["51709218"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51709218"],"award-info":[{"award-number":["51709218"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Hubei Province Youth Natural Science Fund","award":["2016CFB362"],"award-info":[{"award-number":["2016CFB362"]}]},{"name":"Open Research Fund of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing","award":["17I03"],"award-info":[{"award-number":["17I03"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Recognizing ship behavior is important for maritime situation awareness and intelligent transportation management. Some scholars extracted ship behaviors from massive trajectory data by statistical analysis. However, the meaning of the behaviors, i.e., semantic meanings of behaviors and their relationships, are not explicit. Ship behaviors are affected by navigational area and traffic rules, so their meanings can be obtained only in specific maritime situations. The work establishes the semantic model of ship behavior (SMSB) to represent and reason the meaning of the behaviors. Firstly, a semantic network is built based on maritime traffic rules and good seamanship. The corresponding detection methods are then proposed to identify basic ship behaviors in various maritime scenes, including dock, anchorage, traffic lane, and general scenes. After that, dynamic Bayesian network (DBN) is used to reason potential ship behaviors. Finally, trajectory annotation and semantic query of the model are validated in the different scenes of harbor. The basic behaviors and potential behaviors in all typical scenes of any harbor can be obtained accurately and expressed conveniently using the proposed model. The model facilitates the ships behavior research, contributing to the semantic trajectory analysis.<\/jats:p>","DOI":"10.3390\/ijgi8030107","type":"journal-article","created":{"date-parts":[[2019,2,27]],"date-time":"2019-02-27T11:41:03Z","timestamp":1551267663000},"page":"107","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":44,"title":["Semantic Modelling of Ship Behavior in Harbor Based on Ontology and Dynamic Bayesian Network"],"prefix":"10.3390","volume":"8","author":[{"given":"Yuanqiao","family":"Wen","sequence":"first","affiliation":[{"name":"National Engineering Research Center for Water Transport Safety, Wuhan 430063, China"},{"name":"Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, China"},{"name":"Hubei Key Laboratory of Inland Shipping Technology, Wuhan 430063, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6885-5449","authenticated-orcid":false,"given":"Yimeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Navigation, Wuhan University of Technology, Wuhan 430063, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3233-4446","authenticated-orcid":false,"given":"Liang","family":"Huang","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Water Transport Safety, Wuhan 430063, China"},{"name":"Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunhui","family":"Zhou","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Inland Shipping Technology, Wuhan 430063, China"},{"name":"School of Navigation, Wuhan University of Technology, Wuhan 430063, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Changshi","family":"Xiao","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Inland Shipping Technology, Wuhan 430063, China"},{"name":"School of Navigation, Wuhan University of Technology, Wuhan 430063, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Inland Shipping Technology, Wuhan 430063, China"},{"name":"School of Navigation, Wuhan University of Technology, Wuhan 430063, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Navigation, Wuhan University of Technology, Wuhan 430063, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9003-0478","authenticated-orcid":false,"given":"Wenqiang","family":"Zhan","sequence":"additional","affiliation":[{"name":"School of Navigation, Wuhan University of Technology, Wuhan 430063, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9240-1714","authenticated-orcid":false,"given":"Zhongyi","family":"Sui","sequence":"additional","affiliation":[{"name":"School of Navigation, Wuhan University of Technology, Wuhan 430063, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,2,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Vouros, G.A., Doulkeridis, C., Santipantakis, G., and Vlachou, A. (2018). Taming Big Maritime Data to Support Analytics. Information Fusion and Intelligent Geographic Information Systems (IF&IGIS\u201917) ACM Computing Surveys (CSUR), Springer.","DOI":"10.1007\/978-3-319-59539-9_2"},{"key":"ref_2","first-page":"1746","article-title":"Semantic model of ship behaviour based on ontology engineering","volume":"2018","author":"Zhang","year":"2018","journal-title":"J. Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1080\/01490419.2014.902885","article-title":"From movement data to objects behavior using semantic trajectory and semantic events","volume":"37","author":"Vandecasteele","year":"2014","journal-title":"Mar. Geod."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zhu, F. (2011, January 8\u201310). Mining ship spatial trajectory patterns from AIS database for maritime surveillance. Proceedings of the 2nd IEEE International Conference on Emergency Management and Management Sciences (ICEMMS), Beijing, China.","DOI":"10.1109\/ICEMMS.2011.6015796"},{"key":"ref_5","unstructured":"Yang, X., Zhang, Y., Liu, W., and Zhu, F. (2015, January 2\u20134). Ship behavior recognition based on infrared video analysis in a maritime environment. Proceedings of the 2015 14th International Conference on ITS Telecommunications (ITST), Copenhagen, Denmark."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Da Silva, M.C.T., Times, V.C., de Mac\u00eado, J.A., and Renso, C. (2015, January 19\u201323). SWOT: A conceptual data warehouse model for semantic trajectories. Proceedings of the ACM Eighteenth International Workshop on Data Warehousing and OLAP, Melbourne, VIC, Australia.","DOI":"10.1145\/2811222.2811232"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1007\/s11042-010-0680-2","article-title":"Abstracting and reasoning over ship trajectories and web data with the Simple Event Model (SEM)","volume":"57","author":"Schreiber","year":"2012","journal-title":"Multimedia Tools Appl."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1111\/j.1467-9671.2011.01246.x","article-title":"Weka-STPM: A Software Architecture and Prototype for Semantic Trajectory Data Mining and Visualization","volume":"15","author":"Bogorny","year":"2011","journal-title":"Trans. GIS"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"975","DOI":"10.1111\/tgis.12334","article-title":"Enhancing data privacy with semantic trajectories: A raster-based framework for GPS stop\/move management","volume":"22","author":"Wang","year":"2018","journal-title":"Trans. GIS"},{"key":"ref_10","unstructured":"Santipantakis, G.M., Glenis, A., Patroumpas, K., Vlachou, A., Doulkeridis, C., Vouros, G.A., Pelekis, N., and Theodoridis, Y. (2018). SPARTAN: Semantic integration of big spatio-temporal data from streaming and archival sources. Future Gener. Comput. Syst., in press."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Buechel, M., Hinz, G., Ruehl, F., Schroth, H., Gyoeri, C., and Knoll, A. (2017, January 11\u201314). Ontology-based traffic scene modeling, traffic regulations dependent situational awareness and decision-making for automated vehicles. Proceedings of the 2017 IEEE Intelligent Vehicles Symposium (IV), Redondo Beach, CA, USA.","DOI":"10.1109\/IVS.2017.7995917"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.websem.2011.04.002","article-title":"BNOSA: A Bayesian network and ontology based semantic annotation framework","volume":"9","author":"Rajput","year":"2011","journal-title":"Web. Semant."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1016\/j.jocs.2017.02.005","article-title":"SOR: An optimized semantic ontology retrieval algorithm for heterogeneous multimedia big data","volume":"28","author":"Guo","year":"2018","journal-title":"J. Comput. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Lane, R.O., Nevell, D.A., Hayward, S.D., and Beaney, T.W. (2010, January 26\u201329). Maritime anomaly detection and threat assessment. Proceedings of the 13th Conference on Information Fusion (FUSION), Edinburgh, UK.","DOI":"10.1109\/ICIF.2010.5711998"},{"key":"ref_15","unstructured":"Castaldo, F., Palmieri, F.A., Bastani, V., Marcenaro, L., and Regazzoni, C. (2014, January 7\u201310). Abnormal vessel behavior detection in port areas based on dynamic bayesian networks. Proceedings of the 2014 17th International Conference on Information Fusion (FUSION), Salamanca, Spain."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1007\/s10115-015-0845-4","article-title":"A framework for anomaly detection in maritime trajectory behavior","volume":"47","author":"Lei","year":"2016","journal-title":"Knowl. Inf. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1145\/2501654.2501656","article-title":"Semantic trajectories modeling and analysis","volume":"45","author":"Parent","year":"2013","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1145\/2483669.2483682","article-title":"Semantic trajectories: Mobility data computation and annotation","volume":"4","author":"Yan","year":"2013","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1111\/tgis.12011","article-title":"Constant\u2014A conceptual data model for semantic trajectories of moving objects","volume":"18","author":"Bogorny","year":"2014","journal-title":"Trans. GIS"},{"key":"ref_20","unstructured":"Junior, A.S., Times, V.C., Renso, C., Matwin, S., and Cabral, L.A. (2018, January 26\u201328). A semi-supervised approach for the semantic segmentation of trajectories. Proceedings of the 2018 19th IEEE International Conference on Mobile Data Management (MDM), Aalborg, Denmark."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1418","DOI":"10.1016\/j.eswa.2014.08.057","article-title":"Semantic management of moving objects: A vision towards smart mobility","volume":"42","author":"Ilarri","year":"2015","journal-title":"Expert. Syst. Appl."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ruback, L., Casanova, M.A., Raffaet\u00e0, A., Renso, C., and Vidal, V. (2016, January 11\u201313). Enriching mobility data with linked open data. Proceedings of the 20th International Database Engineering & Applications Symposium, Montreal, QC, Canada.","DOI":"10.1145\/2938503.2938550"},{"key":"ref_23","unstructured":"Martin, B., and Gotthard, M. (2018). A Survey on Spatiotemporal and Semantic Data Mining. Trends in Spatial Analysis and Modelling, Springer."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"De Graaff, V., de By, R.A., and van Keulen, M. (2016, January 4\u20138). Automated semantic trajectory annotation with indoor point-of-interest visits in urban areas. Proceedings of the 31st Annual ACM Symposium on Applied Computing, Pisa, Italy.","DOI":"10.1145\/2851613.2851709"},{"key":"ref_25","unstructured":"Nogueira, T.P. (2017). A Framework for Automatic Annotation of Semantic Trajectories. [Ph.D. Thesis, Universit\u00e9 Grenoble Alpes]."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Sester, M., Bernard, L., and Paelke, V. (2009). Towards semantic interpretation of movement behavior. Advances in GIScience, Springer.","DOI":"10.1007\/978-3-642-00318-9"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Br\u00fcggemann, S., Bereta, K., Xiao, G., and Koubarakis, M. (2016, January 17\u201321). Ontology-based data access for maritime security. Proceedings of the 15th International Semantic Web Conference, Kobe, Japan.","DOI":"10.1007\/978-3-319-34129-3_45"},{"key":"ref_28","unstructured":"Dividino, R., Soares, A., Matwin, S., Isenor, A.W., Webb, S., and Brousseau, M. (June, January 30). Semantic integration of real-time heterogeneous data streams for ocean-related decision making. Proceedings of the Big Data and Artificial Intelligence for Military Decision Making, Bordeaux, France."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Santipantakis, G., Kotis, K.I., and Vouros, G.A. (2015, January 6\u20138). Ontology-based data sources\u2019 integration for maritime event recognition. Proceedings of the 2015 6th International Conference on Information, Intelligence, Systems and Applications (IISA), Corfu, Greece.","DOI":"10.1109\/IISA.2015.7388072"},{"key":"ref_30","unstructured":"Claramunt, C., Ray, C., Salmon, L., Camossi, E., Hadzagic, M., Jousselme, A.-L., Andrienko, G., Andrienko, N., Theodoridis, Y., and Vouros, G. (2017, January 21\u201324). Maritime data integration and analysis: Recent progress and research challenges. Proceedings of the Advances in Database Technology-EDBT, Venice, Italy."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Sowa, J.F. (2006). Semantic networks. Encyclopedia of Cognitive Science, John Wiley & Sons.","DOI":"10.1002\/0470018860.s00065"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"11","DOI":"10.3233\/SW-2011-0025","article-title":"The owl API: A java API for owl ontologies","volume":"2","author":"Horridge","year":"2011","journal-title":"Semant. Web"},{"key":"ref_33","unstructured":"Hahmann, S., and Burghardt, D. (2010, January 14\u201317). Connecting linkedgeodata and geonames in the spatial semantic web. Proceedings of the 6th International GIScience Conference, Zurich, Switzerland."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1007\/s10462-010-9197-3","article-title":"Semantic web reasoners and languages","volume":"35","author":"Mishra","year":"2011","journal-title":"Artif. Intell. Rev."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.future.2014.05.004","article-title":"Mobile cloud-based depression diagnosis using an ontology and a Bayesian network","volume":"43","author":"Chang","year":"2015","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"15253","DOI":"10.1016\/j.eswa.2011.05.074","article-title":"Medicine expert system dynamic Bayesian Network and ontology based","volume":"38","author":"Arsene","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"ref_37","unstructured":"Harris, S., Seaborne, A., and Prud\u2019hommeaux, E. (2013). SPARQL 1.1 Query Language, World Wide Web Consortium."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/8\/3\/107\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:35:13Z","timestamp":1760186113000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/8\/3\/107"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,2,27]]},"references-count":37,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2019,3]]}},"alternative-id":["ijgi8030107"],"URL":"https:\/\/doi.org\/10.3390\/ijgi8030107","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,2,27]]}}}