A collaborative learning approach for geographic information retrieval based on social networks |
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Affiliation: | 1. Unidad Profesional Interdisciplinaria en Ingeniería y Tecnologías Avanzadas, Instituto Politécnico Nacional, Barrio La Laguna Ticomán, 07340, Mexico;2. Centro de Investigación en Computación, Instituto Politécnico Nacional, UPALM – Zacatenco, 07320, Mexico;1. The American College of Greece;2. College of Computer and Information Sciences;3. Instituto Politécnico Nacional;4. Department of Applied Informatics;5. University of Science and Technology of China (USTC);6. King Abdulaziz University;1. Center of Excellence in Information Assurance (CoEIA), King Saud University (KSU), Riyadh, Saudi Arabia;2. Center of Excellence in Information Assurance (CoEIA), College of Computer and Information Sciences (CCIS), King Saud University (KSU), Riyadh, Saudi Arabia;3. College of Computer and Information Sciences (CCIS), King Saud University (KSU), Riyadh, Saudi Arabia;4. Intelligent Systems Group (ISG), Department of Computing, Macquarie University, NSW 2109, Australia;1. Center of Excellence in Information Assurance (CoEIA), King Saud University (KSU), Riyadh, Saudi Arabia;2. Center of Excellence in Information Assurance (CoEIA), College of Computer and Information Sciences (CCIS), King Saud University (KSU), Riyadh, Saudi Arabia;3. Intelligent Systems Group (ISG), Department of Computing, Macquarie University, NSW 2109, Australia |
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Abstract: | Nowadays, spatial and temporal data play an important role in social networks. These data are distributed and dispersed in several heterogeneous data sources. These peculiarities make that geographic information retrieval being a non-trivial task, considering that the spatial data are often unstructured and built by different collaborative communities from social networks. The problem arises when user queries are performed with different levels of semantic granularity. This fact is very typical in social communities, where users have different levels of expertise. In this paper, a novelty approach based on three matching-query layers driven by ontologies on the heterogeneous data sources is presented. A technique of query contextualization is proposed for addressing to available heterogeneous data sources including social networks. It consists of contextualizing a query in which whether a data source does not contain a relevant result, other sources either provide an answer or in the best case, each one adds a relevant answer to the set of results. This approach is a collaborative learning system based on experience level of users in different domains. The retrieval process is achieved from three domains: temporal, geographical and social, which are involved in the user-content context. The work is oriented towards defining a GIScience collaborative learning for geographic information retrieval, using social networks, web and geodatabases. |
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Keywords: | Geographic information retrieval GIScience collaborative learning Query contextualization Matching-query layers Ontology |
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