W3C home > Mailing lists > Public > public-geosemweb@w3.org > June 2015

OSMRec - Α tool for automatic annotation of spatial entities in OpenStreetMap [via Geospatial Semantic Web Community Group]

From: W3C Community Development Team <team-community-process@w3.org>
Date: Wed, 10 Jun 2015 14:39:03 +0000
To: public-geosemweb@w3.org
Message-ID: <4f0ff16d2d6a0e1974bf36e084585383@www.w3.org>
GeoKnow has recently introduced OSMRec, a JOSM plugin for automatic annotation
of spatial features (entities) into OpenStreetMap.  OSMRec 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.

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, e.g. the number of nodes of the feature's
geometry, textual similarity, e.g. common important keywords in the names of the
features and semantic similarity (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.

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 “Add Recommendation” button. The recommendation panel opens and
the plugin automatically loads the appropriate recommendation model that has
previously been trained offline.

The recommendation panel provides a list with the top-10 recommended categories
and the user can select from this list and click “Add and continue”. 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). Further, OSMRec provides functionality for
allowing the user to combine several recommendation models, based on (a) a
selected geographic area, (b) user's past editing history on 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.

OSMRec plugin can be downloaded and installed in JOSM following the standard
procedure. Detailed implementation information can be found in the following

	Prototype for Spatial Knowledge Aggregation.

	Context-Sensitive Spatial Knowledge aggregation.


This post sent on Geospatial Semantic Web Community Group

'OSMRec - Α tool for automatic annotation of spatial entities in OpenStreetMap'


Learn more about the Geospatial Semantic Web Community Group: 

Received on Wednesday, 10 June 2015 14:39:12 UTC

This archive was generated by hypermail 2.3.1 : Wednesday, 10 June 2015 14:39:13 UTC