Matching heterogeneous textual data using spatial features

Fize Jacques, Roche Mathieu, Teisseire Maguelonne. 2018. Matching heterogeneous textual data using spatial features. In : Proceedings of IEEE International Conference on Data Mining Workshops (ICDMW). IEEE Computer Society. Singapore : IEEE Computer Society Press, 1389-1396. International Workshop on Spatial and Spatiotemporal Data Mining, Singapore, Singapour, 17 Novembre 2018/20 Novembre 2018.

Url - jeu de données - Dataverse Cirad : https://doi.org/10.18167/DVN1/KH7YTO

Résumé : An increasing amount of textual data is made avail-able through different medium (e.g., social networks, company, data catalog, etc.). These new resources are highly heterogeneous, therefore new methods are needed to extract information. Here, we propose a text matching process based on spatial features and compatible with heterogeneous textual data. Besides being compatible with heterogeneous data, we introduce two contri-butions. First, to be compared, spatial information is extracted then stored in a dedicated representation: STR, or Spatial Textual Representation. Second, to improve the approximation of the spatial similarity, we propose two transformations to apply on STR. To support our contributions, we evaluate the different aspects of the process using two corpora, including one corpus that is highly heterogeneous. Results obtained on both corpora demonstrate that relevant spatial matches can be obtained between the most similar STRs with an improvement due to STR transformation.

Mots-clés libres : Heterogeneous data, Spatial data mining

Auteurs et affiliations

• Fize Jacques, CIRAD-ES-UMR TETIS (FRA) ORCID: 0000-0003-1783-934X
• Roche Mathieu, CIRAD-ES-UMR TETIS (FRA) ORCID: 0000-0003-3272-8568
• Teisseire Maguelonne, IRSTEA (FRA)

Source : Cirad-Agritrop (https://agritrop.cirad.fr/589684/)

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