Ontology engineering; Remote sensing; NoSQL Database; natural language processing; Semantic
Abstract :
[en] This paper proposes a solution to reduce the semantic gap between final users and data/processing providers in a web market place dedicated to remote sensing products. Nowadays, search engine are common tools on the Internet. Users are accustomed to use them and used to get tabular classification of provided answers. These smart agents are set up to answer basic questions using automatic pages redirection or chitchat. In this research, to ensure coherence between user’s requests and platform answers, natural language processing algorithms and knowledge graphs are integrated within a web platform thanks to a NoSQL graph database connected to open thesauri and Geographic Information Systems (GIS). Therefore, the most pertinent services can be proposed based on input sentences including non-technical vocabulary but also geographical components (the user interface includes a text area and an interactive map). While processing chains and remote sensing ontologies were presented in one of our previous studies, this article focuses on natural languages algorithms and knowledge mining.
Research Center/Unit :
Geomatics Unit
Disciplines :
Computer science
Author, co-author :
Nys, Gilles-Antoine ; Université de Liège - ULiège > Département de géographie > Unité de Géomatique - Topographie et géométrologie
Kasprzyk, Jean-Paul ; Université de Liège - ULiège > Département de géographie > Serv. d'étude en géographie éco. fond. et appliquée (Segefa)
Hallot, Pierre ; Université de Liège - ULiège > Département d'Architecture > Relevé, étude et représentation géom. et du patrimoine bâti
Billen, Roland ; Université de Liège - ULiège > Département de géographie > Unité de Géomatique - Topographie et géométrologie
Language :
English
Title :
A Semantic Retrieval System for Remote Sensing Web Platforms
Publication date :
05 June 2019
Event name :
ISPRS GeoSpatial Week 2019
Event organizer :
ISPRS
Event place :
Enschede, Netherlands
Event date :
from 10-06-25019 to 14-06-2019
By request :
Yes
Audience :
International
Journal title :
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.
Bibliography
Ahlers, D. 2017. Linkage Quality Analysis of GeoNames in the Semantic Web. Proceedings of the 11th Workshop on Geographic Information Retrieval-GIR'17, pp. 1-2. https://doi.org/10.1145/3155902.3155904
Arvor, D., Belgiu, M., Falomir, Z., Mougenot, I., & Durieux, L. 2019. Ontologies to interpret remote sensing images: why do we need them? GIScience & Remote Sensing, pp. 1-29. https://doi.org/10.1080/15481603.2019.1587890
Bogdanović, M., Stanimirović, A., & Stoimenov, L. 2015. Methodology for geospatial data source discovery in ontology-driven geo-information integration architectures. Journal of Web Semantics, 32, pp. 1-15. https://doi.org/10.1016/j.websem.2015.01.002
Buckley, C., Salton, G., & Allan, J. 1994. The Effect of Adding Relevance Information in a Relevance Feedback Environment. Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. Presented at the SIGIR94, Dublin, Ireland.
Cavnar, W. B., & Trenlke, J. M. 1994. N-Gram-Based Text Categorization. Proceedings of the 3rd Annual Symposium on Document Analysis and Information Retrieval. Presented at the SDAIR-94.
Damashek, M. 1995. Gauging Similarity with n-Grams: Language-Independent Categorization of Text. Science, 267(5199), pp. 843-848. https://doi.org/10.1126/science.267.5199.843
Fernández, J. D., Umbrich, J., Polleres, A., & Knuth, M. 2018. Evaluating query and storage strategies for RDF archives. Semantic Web, pp. 1-45. https://doi.org/10.3233/SW-180309
Ghazouani, F., Farah, I. R., & Solaiman, B. 2018. Semantic Remote Sensing Scenes Interpretation and Change Interpretation. In C. Thomas (Ed.), Ontology in Information Science. https://doi.org/10.5772/intechopen.72730
Grootjen, F. A., & van der Weide, T. P. 2006. Conceptual query expansion. Data & Knowledge Engineering, 56(2), pp. 174-193. https://doi.org/10.1016/j.datak.2005.03.006
Hirschberg, J., & Manning, C. D. 2015. Advances in natural language processing. Science, 349(6245), pp. 261-266. https://doi.org/10.1126/science.aaa8685
Klien, E., Lutz, M., & Kuhn, W. 2006. Ontology-based discovery of geographic information services-An application in disaster management. Computers, Environment and Urban Systems, 30(1), pp. 102-123. https://doi.org/10.1016/j.compenvurbsys.2005.04.002
Laurini, R. 2017. Gazetteers and Multilingualism. In Geographic Knowledge Infrastructure (pp. 157-182). https://doi.org/10.1016/B978-1-78548-243-4.50008-6
Lewis, D. D., & Jones, K. S. 1993. Natural language processing for information retrieval. Communications of the ACM, 39(1), pp. 92-101. https://doi.org/10.1145/234173.234210
Lillesand, T. M., Kiefer, R. W., & Chipman, J. W. 2015. Remote sensing and image interpretation (Seventh edition). Hoboken, N.J: John Wiley & Sons, Inc.
Lutz, M., & Klien, E. 2006. Ontology-based retrieval of geographic information. International Journal of Geographical Information Science, 20(3), pp. 233-260. https://doi.org/10.1080/13658810500287107
Mandala, R., Tokunaga, T., & Tanaka, H. 1999. Combining multiple evidence from different types of thesaurus for query expansion. Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval-SIGIR '99, pp. 191-197. https://doi.org/10.1145/312624.312677
Marcus, M., Marcinkiewicz, M. A., & Santorini, B. 1993. Building a large annotated corpus of English: the penn treebank. In Computational Linguistics-Special issue on using large corpora: II (Vol. 19, pp. 313-330). Cambridge, USA: MIT Press.
Mauro, N., Ardissono, L., & Savoca, A. 2017. Concept-aware geographic information retrieval. Proceedings of the International Conference on Web Intelligence-WI '17, pp. 34-41. https://doi.org/10.1145/3106426.3106498
Munir, K., & Sheraz Anjum, M. 2018. The use of ontologies for effective knowledge modelling and information retrieval. Applied Computing and Informatics, 14(2), pp. 116-126. https://doi.org/10.1016/j.aci.2017.07.003
Nys, G.-A., Kasprzyk, J.-P., Hallot, P., & Billen, R. 2018. Towards an ontology for the structuring of remote sensing operations shared by different processing chains. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-4, pp. 483-490. https://doi.org/10.5194/isprs-archives-XLII-4-483-2018
Rebele, T., Suchanek, F., Hoffart, J., Biega, J., Kuzey, E., & Weikum, G. 2016. YAGO: A Multilingual Knowledge Base from Wikipedia, Wordnet, and Geonames. In P. Groth, E. Simperl, A. Gray, M. Sabou, M. Krötzsch, F. Lecue, ... Y. Gil (Eds.), The Semantic Web-ISWC 2016 (Vol. 9982, pp. 177-185). https://doi.org/10.1007/978-3-319-46547-0-19
Rocchio, J. J. 1971. Relevance Feedback in Information Retrieval. The SMART Retrieval System-Experiments in Automatic Document Processing.
Schmid, H. 1994. Probabilistic Part-of-Speech Tagging Using Decision Trees. Proceedings of the International Conference on New Methods in Language Processing. Presented at the International Conference on New Methods in Language Processing, Manchester, United Kingdom.
Schmid, H. 1995. Improvements In Part-of-Speech Tagging With an Application To German. Proceedings of the ACL SIGDAT-Workshop, 47-50.
Shvaiko, P., Ivanyukovich, A., Vaccari, L., Maltese, V., & Farazi, F. 2010. A semantic geo-catalogue implementation for a regional SDI. Proceedings of the INPIRE Conference 2010. Presented at the INPIRE conference 2010, Krakow, Poland.
Stein, A., & Schmid, H. 1995. Etiquetage morphologique de textes français avec un arbre de décisions. TAL. Traitement Automatique Des Langues, 36(1-2), 23-35.
Young, T., Hazarika, D., Poria, S., & Cambria, E. 2018. Recent Trends in Deep Learning Based Natural Language Processing. IEEE Computational Intelligence Magazine, 13(3), 55-75. https://doi.org/10.1109/MCI.2018.2840738
Zhong, V., Xiong, C., & Socher, R. 2017. Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning. ArXiv:1709.00103 [Cs]. Retrieved from http://arxiv.org/abs/1709.00103
Similar publications
Sorry the service is unavailable at the moment. Please try again later.
This website uses cookies to improve user experience. Read more
Save & Close
Accept all
Decline all
Show detailsHide details
Cookie declaration
About cookies
Strictly necessary
Performance
Strictly necessary cookies allow core website functionality such as user login and account management. The website cannot be used properly without strictly necessary cookies.
This cookie is used by Cookie-Script.com service to remember visitor cookie consent preferences. It is necessary for Cookie-Script.com cookie banner to work properly.
Performance cookies are used to see how visitors use the website, eg. analytics cookies. Those cookies cannot be used to directly identify a certain visitor.
Used to store the attribution information, the referrer initially used to visit the website
Cookies are small text files that are placed on your computer by websites that you visit. Websites use cookies to help users navigate efficiently and perform certain functions. Cookies that are required for the website to operate properly are allowed to be set without your permission. All other cookies need to be approved before they can be set in the browser.
You can change your consent to cookie usage at any time on our Privacy Policy page.
