Geospatial Meaning

We have entried in the last year of CINeSPACE project.

Mans Shapshak has written an excellent article in Directions Magazine about the project entitled Combining GIS and Semantic Technology to Create a Cultural Visualizer, which has been indexed by the ACM TechNews. Our work has also been reviewed by Roland Piquepaille in his Emerging Tech blog, with the article Discovering Venice with a CINeSPACE device.

Who doesn’t want to experience a CINeSPACE device???

The early attempts to store geographic data in standard relational tables failed. This issue gave rise to spatial databases, which extent the relational model providing specific representations for geospatial data.

As the number of geographic features in a database grew, it became evident that it was imposible to store all the relationships between geographic entities in a large GIS. So distances, intersections, centroids, etc. are easyly computed with built-in functions. Questions like the depicted in the image could be seen as real-time spatial inferencing.

Is featureA within a 5km distance from the border of any water body feature?

When developing spatial reasoning support for Semantic Web we find the same problems. Description logic standards as OWL don’t provide specific spatial built-ins to enable real-time spatial inferencing or querying.

So we need the preprocessing of all spatial-related statements using spatial specific algorithms (or GIS) before adding them to the semantic KB or we need the adding of some kind of spatial layer that combines spatial queries with semantic queries to extent DL expressivity.

We use both approaches in our spatial model for CINeSPACE project:

• A preprocessing of fuzzy spatial relationships such as mereology or neighbourhood, that are added to the KB. This is an feasible approach while the number of spatial elements remains low (hundreds of elements for each city) and the spatial extent of polygons remains constant (non-monotonicity will be reviewed in a future post).
• A spatial layer used for dynamic queries, such as “Retrieve me Geoconcepts I am looking at”, where geoconcept is used to name any entity with an inherently or indirectly associated spatial dimension. SPARQL queries are used over the spatial results to refine the answer.

The need of extra spatial computation limits the success of Geospatial Semantic Web in terms of GIS professionals, who see Semantic Web as a complex world with not great benefits for dealing with space. A step forward spatial integration with DL logics. We are looking for researchers and people interested in this integration. Feel free to contact!!!

We attended at the 5th Geographic Information Systems conference at Istanbul the 2-5 July, where we were introducing our Geoconcepts architecture. One of the main themes of the conference was Geographic Information Science Education. Robert S. Bednarz and Sarah W. Bednarz introduced us some key points in spatial thinking skills and explicit spatial training. They consider three main scenarios in spatial thinking:

• Thinking IN space. This happens in our daily activities, when we move from one place to another, or when we arrange things in space, as these big amount of unread papers in my desk
• Thinking ABOUT space. This is more related to geographic science, and to the study of how reality is organized spatially. Maps and 3D models are convinient ways to think about space.
• Thinking WITH space. Sometimes we use spatial representations for abstract or complex concepts or theories. An example could be a hierarchy diagram of an ontology classes. Also graphs, concept maps etc. are powerful tools to think with space.

These three contexts overlap so often. For instance, in Geospatial ontologies modeling, we use space (WITH space) to model how we think spatially (IN space), and try to approach our traditional ways to explain space (ABOUT space).

Spatial information, or knowledge, has unique and special problems and characteristics. Since the 90s, many people consider Geographic Information Systems (GIS) as a set of technologies that try to solve spatial problems. But the academic theory behind the development, use, and application of GIS is widely named Geographic Information Science or GIScience.

That is, a Science of Geographic or Spatial Information.In 2006, the University Consortium for Geographic Information Science, formed by some tens of USA Universities published the Geographic Information Scicence & Technology Body of Knowledge, which represents an effort to compile an inventory of the skills, concepts and knowledge related to Geographic Information Science and Technology.

It is worth a look, and it is quite cheap, just 25$. The next release or version is planned for 2010. It will necessary have to update all knowledge about Geospatial Data, Standards and Metadata that have emerged in the last years. Also the new challenges arised from Location Based Services and Mobile Geoweb have to be taken into account.

We have developed a new Geospatial ontology within the CINeSPACE project. The main idea behind this ontology is a more ample model of the spatial dimension. Some concepts, such as country, mountain or building are inherently spatial. But there exists an important number of concepts that are not physically geographic, although they are commonly related to certain areas. Some examples could be human activities as going shopping. Also events and parties or famous personalities are often related to a spatial extent. We have introduced the term “geoconcept” to name any entity with an inherently or indirectly associated spatial dimension. Some characteristics of the ontology are:

• It reuses existing standards, such as GeoOWL and Geonames feature type hierarchy.
• Its expressivity is OWL Lite. Fuzzy logic extensions were planned for spatial relationships, so simplicity is important to guarantee the decidability.
• A rough set of spatial relationships is defined. As the spatial extent of abstract concepts is vague or uncertain, the RCC8 model can’t help with this modeling. Mereologic and neighbourhood properties are defined.
• Spatial common reasoning is strongly related to sight sense. Many times we reference our position with what we see, although we don’t be exactly in that place. This effect is clearly seen in Geotagging processes, when sometimes the position of the photo’s target is provided, but other times the point where the photo was taken is recorded. We call this the source-target problem and we have added a new spatial relationship, hasNiceViewsOf, to model this problem.
• Polygons centroid is added to the geometry part.
• While complete fuzzy reasoners are developed, a simple model is provided to deal with different degrees of uncertainty, adding several fuzzy subproperties to spatial relationships. A set of SWRL rules is optionally added to manage fuzzy transitivity.

An skeleton of the ontology is depicted in the following figure:

Two versions can be used: Geoconcepts Ontology v1.2, and v1.2 SWRL enhanced model.