POI Verilerinin Semantik Tanımlarının Oluşturulması ve Görselleştirilmesi

Year 2023, , 213 - 230, 28.09.2023

Gülten Kara , Huriye Akcan

https://doi.org/10.48123/rsgis.1254438

Abstract

POI verileri, navigasyon, turizm, sosyal ağ, lojistik, çevrimiçi harita yapımı, arttırılmış gerçeklik, akıllı şehir çözümleri ve konum tabanlı oyunlar gibi birçok alanda kullanılmaktadır. Son yıllarda bu alanlardaki uygulamaların yaygınlaşmasıyla birlikte ilgi çekici nokta verilerinin toplanması ve güncellenmesi için kitle kaynak ve gönüllü coğrafi bilgi girişimleri ile üretilen veri kaynaklarına yönelim artmıştır. Bu veri kaynakları, ilgi çekici nokta verileri açısından zengin ve değerli bir veri kaynağıdır. Ancak bu veri kaynakları farklı şemalara sahiptir ve farklı ayrıntı düzeyinde veriler içermektedir. Bu durum, farklı veri kaynaklarından çıkarılan ilgi çekici nokta verilerinin eşleştirilmesinde veya analiz edilmesinde problemlere neden olmaktadır. Farklı veri kaynaklarındaki ilgi çekici nokta verilerinin kullanılabilmesi, sözdizimsel veya semantik ortak bir şemanın tanımlanmasına bağlıdır. Bu çalışmada farklı veri kaynaklarındaki ilgi çekici nokta verilerinin eşleştirilmesi problemi ele alınmıştır. Bu bağlamda, ilgi çekici nokta verilerinin Semantik Web uygulamalarında kullanılabilirliğini sağlamak amacıyla POI Ontolojisi geliştirilmiştir ve ilgi çekici nokta verilerinin semantik tanımları oluşturulmuştur. İlgi çekici nokta verileri, Karma ara yüzünde ontoloji ile ilişkilendirilmiştir ve RDF veri görselleştirme aracı olan Sextant kullanılarak görselleştirilmiştir.

Keywords

İlgi çekici noktalar, Ontoloji, RDF, Konumsal veri kaynakları

Generating Semantic Definitions and Visualization of POI Data

Abstract

Points of interest data is used in several areas such as navigation, tourism, social network, logistics, online mapping, augmented reality, smart city solutions, and location based games. In recent years, with the spread of applications in these areas the tendency to data sources produced by crowd sourced and volunteered geographic information initiatives has increased for the gathering and updating of points of interest data. These data sources are a rich and valuable source of POI data. Nevertheless, these data sources have different schemas and contain data at different levels of detail. This causes problems in matching or analyzing points of interest data extracted from different data sources. Usability of points of interest data from different data sources depends on defining a common syntactic or semantic schema. In this study, the problem of matching points of interest data from different data sources is reviewed. In this context, POI Ontology has been developed to ensure the usability of point of interest data in Semantic Web applications and the semantic definitions of points of interest data have been created. Points of interest data is associated with ontologies in the Karma interface and visualized using Sextant, the RDF data visualization tool.

Keywords

Points of interest, Ontology, RDF, Spatial data sources

References

• Bellini, P., Benigni, M., Billero, R., Nesi, P., & Rauch, N. (2014). Km4City ontology building vs data harvesting and cleaning for smart-city services. Journal of Visual Languages & Computing, 25(6), 827-839.
• Berners-Lee, T. (1998). Semantic web road map. Retrieved from https://www.w3.org/DesignIssues/Semantic.html
• Braun, M., Scherp, A., & Staab, S. (2010). Collaborative creation of semantic points of interest as linked data on the mobile phone. Arbeitsberichte des Fachbereichs Informatik. Retrieved from https://kola.opus.hbz-nrw.de/frontdoor/deliver/index/docId/395/file/ 2010_01_Arbeitsberichte.pdf
• Cai, L., Zhu, L., Jiang, F., Zhang, Y., & He, J. (2022). Research on multi-source POI data fusion based on ontology and clustering algorithms. Applied Intelligence, 52, 4758–4774.
• Čerba, O., Charvát, K., Mildorf, T., Bērziņš, R., Vlach, P. & Musilová, B., (2016). SDI4Apps Points of Interest Knowledge Base. In G. Gartner, M. Jobst, & H. Huang (Eds.), Progress in Cartography (pp. 229-237), Switzerland: Springer.
• Gala, A. P. D. L., Cardinale, Y., Dongo, I., & Ticona-Herrera, R. (2021, March). Towards an ontology for urban tourism. In 36th Annual ACM Symposium on Applied Computing, 2021. Proceedings. (pp. 1887-1890). ACM.
• Gao, J., Cao, B. & Fan, H. (2016, November). Point of interest data storage using ontology. In 3rd International Conference on Systems and Informatics, 2016. Proceedings. (pp. 1122-1126). IEEE.
• Gao, Y., Huang, L., Feng, J., & Wang, X. (2020). Semantic trajectory segmentation based on change-point detection and ontology. International Journal of Geographical Information Science, 34(12), 2361-2394.
• GeoDeg. (2022, Eylül 10). GeoDeg Beta. Retrieved from http://geodeg.com.
• Gurav, R., De, D., Thakur, G., & Fan, J. (2021, November). Conflation of geospatial POI data and ground-level imagery via link prediction on joint semantic graph. In 4th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, 2021. Proceedings. (pp. 5-8). ACM.
• KARMA. (2021, Ekim 22). KARMA - A data integration tool. Retrieved from https://usc-isi-i2.github.io/karma/.
• KR-Suite. (2021, Kasım 07). KR-Suite. Retrieved from https://github.com/GiorgosMandi/KR-Suite-docker.
• OGC. (2022, Kasım 11). Open geospatial consortium glossary of terms-P. Retrieved from https://www.ogc.org/ogc/ glossary/p.
• OSM. (2022, Ekim 12). Map features. Retrieved from https://wiki.openstreetmap.org/wiki/Map_features.
• Özdikiş, O., Orhan, F., & Danismaz, F. (2011, June). Ontology-based recommendation for points of interest retrieved from multiple data sources. In International Workshop on Semantic Web Information Management, 2011. Proceedings. (pp. 1-6). ACM.
• Palumbo, R., Thompson, L., & Thakur, G. (2019, November). SONET: a semantic ontological network graph for managing points of interest data heterogeneity. In 3rd ACM SIGSPATIAL International Workshop on Geospatial Humanities, 2019. Proceedings. (pp. 1-6). USDOE.
• Patroumpas, K., Skoutas, D., Mandilaras, G., Giannopoulos, G., & Athanasiou, S. (2019, August). Exposing points of interest as linked geospatial data. In 16th International Symposium on Spatial and Temporal Databases, 2019. Proceedings. (pp. 21-30). ACM.
• Ranjgar, B., Sadeghi-Niaraki, A., Shakeri, M., & Choi, S. M. (2022). An ontological data model for points of interest (POI) in a cultural heritage site. Heritage Science, 10, 13. doi: 10.1186/s40494-021-00635-9.
• SDI4Apps. (2022, Nisan 19). SDI4Apps: Project information. Retrieved from https://sdi4apps.eu/project-information/.
• SEXTANT. (2021, Kasım 07). Sextant. Retrieved from http://sextant.di.uoa.gr.
• Spangenberg, T. (2013). Standardization, modeling and implementation of points of interest - a touristic perspective. International Journal of u-and e-Service, Science and Technology, 6(6), 59-70.
• Tomai, E., Michael, S., & Prastacos, P. (2006, September). An ontology-based Web-portal for tourism. In 2nd International workshop on web portal-based solutions for tourism and other business areas, 2006.
• W3C. (2012). Points of Interest Core. Retrieved from https://www.w3.org/2010/POI/documents/Core/core-20111216.html#pois.
• W3C. (2022a, Eylül 10). Points of Interest (POI) Working Group. Retrieved from https://www.w3.org/2010/POI/.
• W3C. (2022b, Ekim 09). Points of interest working group. Retrieved from https://www.w3.org/2010/POI/wiki/ Main_Page.
• Yılmaz, Ö., & Erdur, R. C. (2012). iConAwa–An intelligent context-aware system. Expert Systems with Applications, 39(3), 2907-2918.
• Yingchen, X., Junzhong, G., Jing, Y., & Zhengyong, Z. (2009, July). An ontology-based approach for mobile personalized recommendation. In 2009 IITA International Conference on Services Science, Management and Engineering, 2009. Proceedings. (pp. 336-339). IEEE.
• Yu, F., West, G., Arnold, L., McMeekin, D., & Moncrieff, S. (2016, February). Automatic geospatial data conflation using semantic web technologies. In Proceedings of the Australasian Computer Science Week Multiconference, 2016. (pp. 1-10). ACM.
• Yu, F., McMeekin, D. A., Arnold, L. & West, G. (2018, January). Semantic web technologies automate geospatial data conflation: conflating points of interest data for emergency response services. In LBS 2018: 14th International Conference on Location Based Services, 2018. (pp. 111-131). Springer.
• Zhou, Y., Wang, M., Zhang, C., Ren, F., Ma, X., & Du, Q. (2021). A points of interest matching method using a multivariate weighting function with gradient descent optimization. Transactions in GIS, 25(1), 359-381.