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The production and use of geo-referenced digital resources is expanding rapidly. In order to exploit their contents, the documents are annotated, indexed and analyzed according to data models dedicated to the description of particular domains. The multiple dimensions of data descriptors can be divided into three categories: location (spatial dimension), date/time (temporal dimension), and theme (thematic dimension). We call geographical data such multidimensional representations. In recent years, a variety of works have highlighted the potential of the extraction, analysis and retrieval of geographic information in corpora composed of textual documents, images, maps, ... A number of engines or services dedicated to the search for geographical information have been proposed: they cover spatial information for the vast majority, but also spatio-temporal and thematic information, for others.

The purpose of this Track is to bring together the growing community of professionals and researchers of the field of geographic information extraction and analysis, and of the corresponding applications. KEGeoD track is at the crossroads of several disciplines: of course geomatics, but also Knowledge Engineering (KE), natural languages processing (NLP), data mining (DM) and information extraction (IE).

How to effectively exploit the power of geographical information available on the Web, through the thematic, spatial and temporal dimensions? How to use the complementarity of external knowledge resources? These questions highlight a non-exhaustive list of themes considered for KEGeoD track:

1. Preparation of Geographical Data
   1. Identification of resources and of data (texts, images, etc.);
   2. Data Modeling (spatial, temporal, spatio-temporal and thematic data);
   3. Taking into account specific characteristics: heterogeneity, volumetry, mono or multi-dimensionality.
2. Geographical knowledge Extraction
   1. Construction and Acquisition of spatial/temporal/thematic Knowledge;
   2. Qualitative approaches (expert annotation, etc.) and quantitative approaches (data mining, NLP, etc).
3. Geographical Data Analytics
   1. Single or multi-Dimensional Data Analysis (interpretation and data enrichment);
   2. Quality measurements on spatial and temporal data;
   3. Evaluation of spatial and temporal tools, resources and knowledge.
4. Uses, methods and issues in Humanities and Social Sciences topics: geography, history, archeology, sociology, etc.