A collaborative learning approach for geographic information retrieval based on social networks. (October 2015)

• Record Type: Journal Article
• Authors: Mata-Rivera, Felix; Torres-Ruiz, Miguel; Guzmán, Giovanni; Moreno-Ibarra, Marco; Quintero, Rolando
• Is Part Of: Computers in human behavior. Volume 51:Part B(2015)
• Page Start: 829
• Page End: 842
• Publication Date: 2015-10
• Subjects: Geographic information retrieval -- GIScience collaborative learning -- Query contextualization -- Matching-query layers -- Ontology; Interactive computer systems -- Periodicals; Man-machine systems -- Periodicals; 004.019
• DOI: 10.1016/j.chb.2014.11.069
• Languages: English
• ISSNs: 0747-5632

Abstract

Highlights: Method for geographic information retrieval based on matching-query layers is defined. The approach builds an ontology that defines the time, space and social features. The work defines particular cases in GIR for GIScience collaborative learning. Application of the work establishes adequate user profiles for information retrieval. 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.