{"id":50,"date":"2015-06-10T14:39:03","date_gmt":"2015-06-10T14:39:03","guid":{"rendered":"https:\/\/www.w3.org\/community\/geosemweb\/?p=50"},"modified":"2015-06-10T14:39:03","modified_gmt":"2015-06-10T14:39:03","slug":"osmrec-%ce%b1-tool-for-automatic-annotation-of-spatial-entities-in-openstreetmap","status":"publish","type":"post","link":"https:\/\/www.w3.org\/community\/geosemweb\/2015\/06\/10\/osmrec-%ce%b1-tool-for-automatic-annotation-of-spatial-entities-in-openstreetmap\/","title":{"rendered":"OSMRec – \u0391 tool for automatic annotation of spatial entities in OpenStreetMap"},"content":{"rendered":"

GeoKnow has recently introduced OSMRec, a JOSM plugin<\/a>\u00a0for automatic annotation of spatial features (entities) into OpenStreetMap. \u00a0OSMRec trains on existing OSM data and is able to recommend to users OSM categories, in order to annotate newly inserted spatial entities. This is important for two reasons. First, users may not be familiar with the OSM categories; thus searching and browsing the OSM category hierarchy to find appropriate categories for the entity they wish to insert may often be a time consuming and frustrating process, to the point of users neglecting to add this information. Second, if an already existing category that matches the new entity cannot be found quickly and easily (although it exists), the user may resort instead to using his\/her own term, resulting in synonyms that later need to be identified and dealt with.<\/p>\n

The category recommendation process takes into account the similarity of the new spatial entities to already existing (and annotated with categories) ones in several levels: spatial similarity<\/i>, e.g. the number of nodes of the feature’s geometry, textual similarity<\/i>, e.g. common important keywords in the names of the features and semantic similarity<\/i> (similarities on the categories that characterize already annotated entities). So, for each level (spatial, textual, semantic) we define and implement a series of training features that represent spatial entities into a multidimensional space. This way, by training the aforementioned models, we are able to correlate the values of the training features with the categories of the spatial entities, and consequently, recommend categories for new features. To this end, we apply multiclass SVM classification, using LIBLINEAR<\/a>.<\/p>\n

The following figure represents a screen of OSMRec within JOSM. The user can select an entity or draw a new entity on the map and ask for recommendations by clicking the \u201cAdd Recommendation\u201d button. The recommendation panel opens and the plugin automatically loads the appropriate recommendation model that has previously been trained offline.<\/p>\n

\"6.<\/a><\/p>\n

The recommendation panel provides a list with the top-10 recommended categories and the user can select from this list and click \u201cAdd and continue\u201d. As a result the selected category is added to the OSM tags. By the time the user adds a new tag at the selected object, a new vector is computed for that OSM instance in order to recalculate the predictions and display an updated list of recommendations (taking into account the previously selected categories\/tags, as extra training information).\u00a0Further, OSMRec provides functionality for allowing the\u00a0user to combine several recommendation models, based on\u00a0(a) a selected geographic area, (b) user’s past editing history\u00a0on OSM and (c) combination of (a) and (b). This way, personalized category recommendations can be provided that take into account the user’s editing history and\/or the specific characteristics of a geographic area of OSM.<\/p>\n

OSMRec plugin can be\u00a0<\/span>downloaded<\/a>\u00a0and installed in JOSM following the\u00a0<\/span>standard procedure<\/a>. Detailed implementation information can be found in the following documents:<\/span><\/p>\n