The 4th Edition of Know@LOD was held at ESWC 2015 in Portoroz, Slovenia. The workshop was a success, featuring a highly inspiring keynote by Marko Grobelnik, a number of interesting research paper presentation, a very competitive Linked Data Mining Challenge, and a social dinner.
The GeoKnow project was presented at the recently finished international science and technology fair in Belgrade, Serbia, the largest event of its kind in the region, held from May 11 through 15. The Institute Mihajlo Pupin team used the opportunity to present the goals and accomplishments of the project, as well as PUPIN’s own results, such as GEM, the mobile semantic geospatial browser, and ESTA-LD, the exploratory spatiotemporal analytics tool for Linked Data, both outcomes of the GeoKnow Work Package 4 efforts, to a wider audience.
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 documents:
A typical tourist scenario is hard to picture without a map. Yet, such a scenario implies you are not familiar with your surroundings and, therefore, often not sure how to find the things that are of interest to you. Typical geospatial browsers will provide you with common exploration tools that will most often include a slippy map combined with keyword search, categorized points of interest (POIs) and a fixed set of filters. But, all of these imply either that you know what it is you’re looking for, or that the preset collection of POIs and criteria will be enough to satisfy your needs. In real life, however, those needs will often be affected by the given context, which is, in turn, dependent on multiple, dynamic factors, such as the place you’re visiting, your mood, interests, background etc. Imagine using your favorite geospatial browser to answer the following question:
“Where are the nearest buildings designed by Frank Lloyd Wright, typical of the Prairie School movement?”
GEM (Geospatial-semantic Exploration on the Move) is the very first geospatial exploration tool that offers a rich mobile experience and overcomes the abovementioned limitations of conventional solutions by exploiting all strengths of the Linked Open Data paradigm, such as built-in semantics in open, crowd-sourced knowledge found in publicly available sources, such as DBpedia, loaded and filtered on-demand, according to user’s needs, in order to prevent maps from overpopulating.
The Workbench also provides Single Sign On functionality, user and role management and data access control for the different users. The Workbench is comprised of a front-end and back-end implementations. The front-end provides GUIs for software components where a REST API is available (LimesService, GeoLiftService and TripleGeoService). Components that provide their own GUI, are integrated using containers (FAGI-gis, OntoWiki, Mappify, Sparqlify and Virtuoso SPARQL query interface). The front-end also provides GUIs for the administrative features like users and roles management, data source management and graphs management, as well as the Dashboard GUI. The Dashboard provides a visual feedback to the user with the registered jobs and the status of executions. The Workbench back-end provides REST interfaces for management of users, roles, graphs, datasources and batch jobs, for retrieving the system configuration, and for importing RDF data. All system information is stored in Virtuoso RDF store.
The second year of GeoKnow has passed and we have several new releases to announce. Among new software tools there are:
FAGI aims to provide data fusing on geometries of linked entities.
This latest version provides several optimisations that increased
the scalability and efficiency. It also provides a map-based interface
for facilitating the fusion actions through visualisation and
filtering of linked entities.
RDF Data Cube Validation Tool 0.0.1
Validation tool aims to ensure the quality of statistical datasets.
It is based primarily on the integrity constraints defined by the
RDF Data Cube vocabulary, and it can be used to detect violations
of the integrity constraints, identify violating resources, and
fix detected issues. Furthermore, to ensure the proper use of
vocabularies other than the RDF Data Cube vocabulary, it relies
on RDFUnit. It can be configured to work any SPARQL endpoint,
which needs to be writeable in order to perform fix operations.
However, if this is not the case, user is provided with the SPARQL
Update query that provides the fix, so that it can be executed
manually. Main purpose of the tool within the GeoKnow project
is to ensure the quality of input data that is to be processed and
visualized with ESTA-LD.
The spring-batch-admin-geoknow is the first version of batch processing
component that functions as the backend of the Workbench’s.
Besides brand new components, there are also new releases also available as Debian packages:
Virtuoso 7.1.0 includes improvements in the Engine (SQL Relational Tables and RDF Property/Predicate Graphs); Geo-Spatial support; SPARQL compiler; Jena and Sesame provider performance; JDBC Driver; Conductor CA root certificate management; WebDAV; and the Faceted Browser.
The LinkedGeoData package contains scripts and mapping files
for converting spatial data from non-RDF (currently relational)
sources to RDF. OpenStreetMap is so far the best covered data
source. Recently, initial support for GADM and Natural Earth were
Added an alternative lgd load script which improves
throughput by inserting data into a different schema first
followed by a conversion step.
Optimized export scripts by using parallel version of pbzip.
Added rdfs:isDefinedBy triples providing licence information
for each resource.
Facete2 is a web application for exploring (spatial) data in SPARQL
endpoints. It features faceted browsing, auto detection of relations
to spatial data, export, and customization of which data to
Context menus are now available in the result view enabling
one to conveniently visit resources in other browser
tabs, create facet constraints from selected items and copy
values into the clipboard.
Improved Facete’s autodetection when counting facets is
infeasible because of the size of the data
Suggestions of resources related to the facet selection that
can be shown on the map are now sortable by the length
of the corresponding property path.
This package is a helper package and is mainly responsible for
the Facete database setup. There were no significant changes.
This package provides a web admin interface for Sparqlify. The
system supports running different mappings simultaneously under
different context paths. Minor user interface improvements.
This package is a helper package and is mainly responsible for
the Sparqlify database setup. There were no significant changes.
This package provides a command line interface for Sparqlify.
Sparqlify is an advanced scalable SPARQL-to-SQL rewriter and the
main engine for the LinkedGeoData project.
Fixed some bugs that caused the generation of invalid SQL.
Added improvements for aggregate functions that make
Sparqlify work with Facete.
Added initial support for Oracle 11g database.
Limes-services updated to the latest LIMES library. The main enhancement
this year was refactoring the service to provide RESTful
First public release of the GeoKnow Generator Workbench extends
the initial prototype by including user and role management,
graph access control management, processing monitoring
within a dashboard.
GeoLift has been renamed to DEER. The functionalities provided
in GeoLift have been generalised to not only support geospatial,
but generally structured data.
On January 6, 2015 the W3C has officially launched the Open Geospatial Consortium (OGC) and the W3C working group for spatial data. Ontos has voted and supported the charter on behalf of the GeoKnow project. This is the result of the joint workshop held in March 2014 together with the SmartOpenData project. The GeoKnow team is looking forward to support this activity and future standards for spatial data on the web. As an example, the GeoKnow team (represented by Valentina Janev and Jens Lehmann) co-organises a spatial data session with W3C (represented by Phil Archer) at the ICIST conference in March: http://www.yuinfo.org/icist2015/icist_odagis.html.
Ontos was selected as an implementation partner at SECO to implement a linked data stack platform. Based on the GeoKnow Generator Ontos will develop a data management and search platform that will allow the management of linked open government data. The GeoKnow generator will used as the backend system that orchestrates the various tools. In a first version triplification of data and the interlinking will be implemented.