{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T18:34:14Z","timestamp":1771698854137,"version":"3.50.1"},"reference-count":71,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,12,16]],"date-time":"2021-12-16T00:00:00Z","timestamp":1639612800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Semantic trajectory analytics and personalised recommender systems that enhance user experience are modern research topics that are increasingly getting attention. Semantic trajectories can efficiently model human movement for further analysis and pattern recognition, while personalised recommender systems can adapt to constantly changing user needs and provide meaningful and optimised suggestions. This paper focuses on the investigation of open issues and challenges at the intersection of these two topics, emphasising semantic technologies and machine learning techniques. The goal of this paper is twofold: (a) to critically review related work on semantic trajectories and knowledge-based interactive recommender systems, and (b) to propose a high-level framework, by describing its requirements. The paper presents a system architecture design for the recognition of semantic trajectory patterns and for the inferencing of possible synthesis of visitor trajectories in cultural spaces, such as museums, making suggestions for new trajectories that optimise cultural experiences.<\/jats:p>","DOI":"10.3390\/bdcc5040080","type":"journal-article","created":{"date-parts":[[2021,12,16]],"date-time":"2021-12-16T11:27:36Z","timestamp":1639654056000},"page":"80","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Semantic Trajectory Analytics and Recommender Systems in Cultural Spaces"],"prefix":"10.3390","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4588-0713","authenticated-orcid":false,"given":"Sotiris","family":"Angelis","sequence":"first","affiliation":[{"name":"Intelligent Systems Lab, Department of Cultural Technology and Communication, University of the Aegean, University Hill, 81100 Mytilene, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7838-9691","authenticated-orcid":false,"given":"Konstantinos","family":"Kotis","sequence":"additional","affiliation":[{"name":"Intelligent Systems Lab, Department of Cultural Technology and Communication, University of the Aegean, University Hill, 81100 Mytilene, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3646-1362","authenticated-orcid":false,"given":"Dimitris","family":"Spiliotopoulos","sequence":"additional","affiliation":[{"name":"Department of Management Science and Technology, University of the Peloponnese, 22100 Tripoli, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.websem.2013.03.001","article-title":"SMARTMUSEUM: A mobile recommender system for the Web of Data","volume":"20","author":"Ruotsalo","year":"2013","journal-title":"J. Web Semant."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1007\/s11257-019-09225-8","article-title":"Enhancing cultural recommendations through social and linked open data","volume":"29","author":"Sansonetti","year":"2019","journal-title":"User Model. User-Adapt. Interact."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Van Hage, W.R., Stash, N., Wang, Y., and Aroyo, L. (June, January 30). Finding your way through the Rijksmuseum with an adaptive mobile museum guide. Proceedings of the 7th Extended Semantic Web Conference, ESWC 2010, Heraklion, Greece.","DOI":"10.1007\/978-3-642-13486-9_4"},{"key":"ref_4","unstructured":"Andrienko, G., Andrienko, N., Fuchs, G., Raimond, A.M.O., Symanzik, J., and Ziemlicki, C. (2013, January 5\u20138). Extracting semantics of individual places from movement data by analyzing temporal patterns of visits. Proceedings of the First ACM SIGSPATIAL International Workshop on Computational Models of Place, Orlando, FL, USA."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.eswa.2017.09.040","article-title":"Hierarchical trajectory clustering for spatio-temporal periodic pattern mining","volume":"92","author":"Zhang","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Ying, J.J.C., Lu, E.H.C., Lee, W.C., Weng, T.C., and Tseng, V.S. (2010, January 2). Mining user similarity from semantic trajectories. Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Location Based Social Networks (LBSN-10), San Jose, CA, USA.","DOI":"10.1145\/1867699.1867703"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"695","DOI":"10.1007\/s00778-011-0244-8","article-title":"Unveiling the complexity of human mobility by querying and mining massive trajectory data","volume":"20","author":"Giannotti","year":"2011","journal-title":"VLDB J."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"898","DOI":"10.1109\/TKDE.2016.2637898","article-title":"Trajectory Community Discovery and Recommendation by Multi-Source Diffusion Modeling","volume":"29","author":"Liu","year":"2017","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2501654.2501656","article-title":"Semantic trajectories modeling and analysis","volume":"45","author":"Parent","year":"2013","journal-title":"ACM Comput. Surv."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.datak.2007.10.008","article-title":"A conceptual view on trajectories","volume":"65","author":"Spaccapietra","year":"2008","journal-title":"Data Knowl. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Nanni, M., Trasarti, R., Renso, C., Giannotti, F., and Pedreschi, D. (2010, January 22\u201326). Advanced knowledge discovery on movement data with the GeoPKDD system. Proceedings of the 13th International Conference on Extending Database Technology, Lausanne, Switzerland.","DOI":"10.1145\/1739041.1739129"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1007\/s10707-014-0220-8","article-title":"Recommendations in location-based social networks: A survey","volume":"19","author":"Bao","year":"2015","journal-title":"Geoinformatica"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1016\/j.eswa.2017.10.004","article-title":"FrameSTEP: A framework for annotating semantic trajectories based on episodes","volume":"92","author":"Nogueira","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1109\/JIOT.2016.2587060","article-title":"Semantic Reasoning for Context-Aware Internet of Things Applications","volume":"4","author":"Maarala","year":"2017","journal-title":"IEEE Internet Things J."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1057\/PALGRAVE.IVS.9500182","article-title":"Towards a taxonomy of movement patterns","volume":"7","author":"Dodge","year":"2008","journal-title":"Inf. Vis."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Kembellec, G., Chartron, G., and Saleh, I. (2014). Recommender Systems, John Wiley & Sons.","DOI":"10.1002\/9781119054252"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.culher.2018.06.003","article-title":"Recommender systems, cultural heritage applications, and the way forward","volume":"35","author":"Pavlidis","year":"2019","journal-title":"J. Cult. Herit."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.knosys.2013.03.012","article-title":"Recommender systems survey","volume":"46","author":"Bobadilla","year":"2013","journal-title":"Knowl.-Based Syst."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ricci, F., Rokach, L., and Shapira, B. (2011). Recommender Systems Handbook, Springer.","DOI":"10.1007\/978-0-387-85820-3"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1007\/978-3-642-30864-2_15","article-title":"A context-aware mobile recommender system based on location and trajectory","volume":"171 AISC","author":"Barranco","year":"2012","journal-title":"Adv. Intell. Syst. Comput."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Chicaiza, J., and Valdiviezo-Diaz, P. (2021). A comprehensive survey of knowledge graph-based recommender systems: Technologies, development, and contributions. Information, 12.","DOI":"10.3390\/info12060232"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3447772","article-title":"Knowledge graphs","volume":"54","author":"Hogan","year":"2021","journal-title":"ACM Comput. Surv."},{"key":"ref_23","first-page":"29","article-title":"Knowledge Graphs: New Directions for Knowledge Representation on the Semantic Web (Dagstuhl Seminar 18371)","volume":"8","author":"Bonatti","year":"2019","journal-title":"Dagstuhl Rep."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Kejriwal, M. (2019). What Is a Knowledge Graph. Domain-Specific Knowledge Graph Construction, Springer. SpringerBriefs in Computer Science.","DOI":"10.1007\/978-3-030-12375-8"},{"key":"ref_25","unstructured":"Lassila, O., and Swick, R.R. (2021, November 16). Resource Description Framework (RDF) Model and Syntax Specification. World Wide Web Consortium Recommendation. Available online: https:\/\/www.w3.org\/TR\/1999\/REC-rdf-syntax-19990222\/."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"De Graaff, V., De By, R.A., and De 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_27","doi-asserted-by":"crossref","unstructured":"Chen, Z., Wang, X., Li, H., and Wang, H. (2020, January 13\u201317). On Semantic Organization and Fusion of Trajectory Data. Proceedings of the 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC), Madrid, Spain.","DOI":"10.1109\/COMPSAC48688.2020.0-130"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1109\/TVCG.2016.2598416","article-title":"SemanticTraj: A New Approach to Interacting with Massive Taxi Trajectories","volume":"23","author":"Wu","year":"2017","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"540","DOI":"10.1016\/j.future.2018.07.007","article-title":"SPARTAN: Semantic integration of big spatio-temporal data from streaming and archival sources","volume":"110","author":"Santipantakis","year":"2020","journal-title":"Futur. Gener. Comput. Syst."},{"key":"ref_30","unstructured":"Soares, A., Times, V., Renso, C., Matwin, S., and Cabral, L.A.F. (2018, January 25\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_31","doi-asserted-by":"crossref","unstructured":"Vassilakis, C., Kotis, K., Spiliotopoulos, D., Margaris, D., Kasapakis, V., Anagnostopoulos, C.N., Santipantakis, G., Vouros, G.A., Kotsilieris, T., and Petukhova, V. (2020). A semantic mixed reality framework for shared cultural experiences ecosystems. Big Data Cogn. Comput., 4.","DOI":"10.3390\/bdcc4020006"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Ghosh, S., and Ghosh, S.K. (2017, January 3\u20137). Modeling of human movement behavioral knowledge from GPS traces for categorizing mobile users. Proceedings of the 26th International Conference on World Wide Web Companion, Perth, Australia.","DOI":"10.1145\/3041021.3054150"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.ins.2020.05.107","article-title":"Semantic trajectory representation and retrieval via hierarchical embedding","volume":"538","author":"Gao","year":"2020","journal-title":"Inf. Sci. (NY)"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1007\/s10707-020-00430-x","article-title":"Towards a semantic indoor trajectory model: Application to museum visits","volume":"25","author":"Kontarinis","year":"2021","journal-title":"GeoInformatica"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Karatzoglou, A., Schnell, N., and Beigl, M. (2018, January 4\u20137). A convolutional neural network approach for modeling semantic trajectories and predicting future locations. Proceedings of the 27th International Conference on Artificial Neural Networks, Rhodes, Greece.","DOI":"10.1007\/978-3-030-01418-6_7"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Zhang, W., Wang, X., and Huang, Z. (2019). A system of mining semantic trajectory patterns from GPS data of real users. Symmetry, 11.","DOI":"10.3390\/sym11070889"},{"key":"ref_37","unstructured":"Khoroshevsky, F., and Lerner, B. (2016, January 4). Human mobility-pattern discovery and next-place prediction from GPS data. Proceedings of the 4th IAPR TC 9 Workshop, MPRSS 2016, Cancun, Mexico."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1016\/j.patrec.2020.01.005","article-title":"An agent-based approach for recommending cultural tours","volume":"131","author":"Amato","year":"2020","journal-title":"Pattern Recognit. Lett."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"4266","DOI":"10.1109\/TII.2019.2908056","article-title":"An Edge Intelligence Empowered Recommender System Enabling Cultural Heritage Applications","volume":"15","author":"Su","year":"2019","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1007\/s10209-019-00657-y","article-title":"Cultural heritage visits supported on visitors\u2019 preferences and mobile devices","volume":"19","author":"Cardoso","year":"2020","journal-title":"Univers. Access Inf. Soc."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1007\/s00779-016-0990-0","article-title":"Context-based infomobility system for cultural heritage recommendation: Tourist Assistant\u2014TAIS","volume":"21","author":"Smirnov","year":"2017","journal-title":"Pers. Ubiquitous Comput."},{"key":"ref_42","first-page":"1","article-title":"Cross-cultural contextualisation for recommender systems","volume":"10","author":"Hong","year":"2019","journal-title":"J. Ambient Intell. Humaniz. Comput."},{"key":"ref_43","unstructured":"Loboda, O., Nyhan, J., Mahony, S., Romano, D.M., and Terras, M. (2019, January 21\u201322). Content-based Recommender Systems for Heritage: Developing a Personalised Museum Tour. Proceedings of the DSRS-Turing 2019: 1st International \u2018Alan Turing\u2019 Conference on Decision Support and Recommender Systems, London, UK."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1007\/s00779-016-0985-x","article-title":"Social recommendation service for cultural heritage","volume":"21","author":"Hong","year":"2017","journal-title":"Pers. Ubiquitous Comput."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"714","DOI":"10.3897\/jucs.70330","article-title":"Towards a semantic graph-based recommender system. A case study of cultural heritage","volume":"27","author":"Qassimi","year":"2021","journal-title":"J. Univers. Comput. Sci."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Zhou, S., Dai, X., Chen, H., Zhang, W., Ren, K., Tang, R., He, X., and Yu, Y. (2020, January 25\u201330). Interactive Recommender System via Knowledge Graph-enhanced Reinforcement Learning. Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event, China.","DOI":"10.1145\/3397271.3401174"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1911","DOI":"10.1002\/asi.23837","article-title":"Graph-based recommendation integrating rating history and domain knowledge: Application to on-site guidance of museum visitors","volume":"68","author":"Minkov","year":"2017","journal-title":"J. Assoc. Inf. Sci. Technol."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.procs.2017.08.355","article-title":"Towards Trajectory-Based Recommendations in Museums: Evaluation of Strategies Using Mixed Synthetic and Real Data","volume":"113","author":"Ilarri","year":"2017","journal-title":"Procedia Comput. Sci."},{"key":"ref_49","first-page":"1","article-title":"Adversarial Human Trajectory Learning for Trip Recommendation","volume":"32","author":"Gao","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.eswa.2017.10.049","article-title":"Itinerary recommender system with semantic trajectory pattern mining from geo-tagged photos","volume":"94","author":"Cai","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Xu, M., and Han, J. (2020, January 28\u201330). Next Location Recommendation Based on Semantic-Behavior Prediction. Proceedings of the 2020 5th International Conference on Big Data and Computing, Chengdu, China.","DOI":"10.1145\/3404687.3404699"},{"key":"ref_52","unstructured":"(2021, November 16). Semantic Trajectory Episodes\u2014Report Generated by Parrot. Available online: http:\/\/talespaiva.github.io\/step\/."},{"key":"ref_53","unstructured":"(2021, November 16). OpenStreetMap. Available online: https:\/\/www.openstreetmap.org\/#map=16\/37.9704\/23.7300&layers=H."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Santipantakis, G.M., Vouros, G.A., Doulkeridis, C., Vlachou, A., Andrienko, G., Andrienko, N., Fuchs, G., Garcia, J.M.C., and Martinez, M.G. (2017, January 11\u201314). Specification of semantic trajectories supporting data transformations for analytics: The datacron ontology. Proceedings of the 13th International Conference on Semantic Systems, Amsterdam, The Netherlands.","DOI":"10.1145\/3132218.3132225"},{"key":"ref_55","unstructured":"(2021, November 16). IndoorGML OGC. Available online: http:\/\/indoorgml.net\/."},{"key":"ref_56","first-page":"47","article-title":"A spatiotemporal extent pattern based on semantic trajectories","volume":"32","author":"Krisnadhi","year":"2017","journal-title":"Adv. Ontol. Des. Patterns"},{"key":"ref_57","unstructured":"Pei, J., Han, J., Mortazavi-Asl, B., Pinto, H., Chen, Q., Dayal, U., and Hsu, M.C. (2001, January 2\u20136). PrefixSpan: Mining sequential patterns efficiently by prefix-projected pattern growth. Proceedings of the 17th International Conference on Data Engineering, Heidelberg, Germany."},{"key":"ref_58","unstructured":"(2021, November 16). Graph Data Platform|Graph Database Management System|Neo4j. Available online: https:\/\/neo4j.com\/."},{"key":"ref_59","unstructured":"(2021, November 16). Home\u2014DBpedia Association. Available online: https:\/\/www.dbpedia.org\/."},{"key":"ref_60","unstructured":"(2021, November 16). Discover Inspiring European Cultural Heritage|Europeana. Available online: https:\/\/www.europeana.eu\/en."},{"key":"ref_61","unstructured":"(2021, November 16). Home\u2014LinkedGeoData. Available online: http:\/\/linkedgeodata.org\/."},{"key":"ref_62","unstructured":"(2021, November 16). SPARQL 1.1 Query Language. Available online: https:\/\/www.w3.org\/TR\/sparql11-query\/."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Haveliwala, T.H. (2002, January 7\u201311). Topic-sensitive PageRank. Proceedings of the Eleventh International Conference on World Wide Web\u2014WWW \u201902, Honolulu, HI, USA.","DOI":"10.1145\/511511.511513"},{"key":"ref_64","unstructured":"(2021, November 16). WebPlotDigitizer\u2014Extract Data from Plots, Images, and Maps. Available online: https:\/\/automeris.io\/WebPlotDigitizer\/."},{"key":"ref_65","unstructured":"(2021, November 16). DataGenCARS. Available online: http:\/\/webdiis.unizar.es\/~silarri\/DataGenCARS\/."},{"key":"ref_66","unstructured":"(2021, November 16). Find Your Inspiration.|Flickr. Available online: https:\/\/flickr.com\/."},{"key":"ref_67","unstructured":"(2021, November 16). Europeana Data Model|Europeana Pro. Available online: https:\/\/pro.europeana.eu\/page\/edm-documentation."},{"key":"ref_68","unstructured":"(2021, November 16). Home|CIDOC CRM. Available online: http:\/\/www.cidoc-crm.org\/."},{"key":"ref_69","unstructured":"(2021, November 16). FOAF Vocabulary Specification. Available online: http:\/\/xmlns.com\/foaf\/spec\/."},{"key":"ref_70","unstructured":"(2021, November 16). User Profile Ontology. Available online: http:\/\/iot.ee.surrey.ac.uk\/citypulse\/ontologies\/up\/up.html."},{"key":"ref_71","unstructured":"(2021, November 16). Karma: A Data Integration Tool. Available online: https:\/\/usc-isi-i2.github.io\/karma\/."}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/5\/4\/80\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:50:13Z","timestamp":1760169013000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/5\/4\/80"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,16]]},"references-count":71,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["bdcc5040080"],"URL":"https:\/\/doi.org\/10.3390\/bdcc5040080","relation":{},"ISSN":["2504-2289"],"issn-type":[{"value":"2504-2289","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,16]]}}}