**5. Conclusion**

184 Semantics – Advances in Theories and Mathematical Models

?feat2 feat:name ?name2

In an attempt to define the built-ins for SWRL, a list of eight built-ins was proposed during the research work. These eight built-ins reflect four geoprocessing functions and four georelationship functions that are discussed previously. The built-ins reflecting geoprocessing functions are built up in combinations with the spatial classes adjusted in the ontology and their relevant object properties. The built-ins for georelationship functions are

The domain of archaeology benefits from this work and could surely be of benefit for lot of other domains. To show this we present a simple example to determine the location of possible flooding zone when the river bank bursts with excessive water during rainy season. This is a very common exercise for a flood management system in hydrology and it gives interesting clues for archaeology. In general with a common GIS, a set of activities are

 ?feat2 rdfs:type feat:Building

object properties and corresponding spatial functions in database system.

carried out which are mentioned in the following sequences:

LandParcel, belong also to the concept FloodingLandParcel.

Determine the elevation of land parcel inside the buffer zone

 Check whether the land parcel elevation is above the threshold (e.g. 25 meters) Select areas below the threshold area and determine them as flood liable zone.

It should be understood that this example is provided just as a proof of the concept. Hence details on other hydrological factors are ignored on purpose. For a simple location analysis as such requires at least four steps of spatial analyses. This paper provides an alternative through the spatial extension of SWRL in one step. We combine the existing built-ins in existing SWRL and the spatial built-in mentioned in this paper to execute this analysis.

River(?x) ^ LandParcel(?y) ^ hasElevation(?y, ?Elv) ^ swrlb:lessThan(?Elv, 25) ^

FloodingLandParcel(?y) (2)

spatialswrlb:Buffer(?x, 50, ?z) ^ spatialswrlb:Intersection(?z, ?y, ?res)

The result of this rule is that the individuals which respect the rule and belong to

Buffer the river by certain distance (e.g. 100 meters)

?feat1 feat:name ?name1

SPATIAL\_FILTER [touches (?feat1, ?feat2)]

?feat1 rdfs:type feat:River

SELECT ?name1 ?name2

WHERE

{

}

**4.3.2 Inference rules through SWRL**

This research has made an attempt to contribute through including the functionalities of spatial analysis within the Semantic Web framework. Moving beyond the semantic information, it has opened the chapter of inclusion of other form of information. It is important for the development of the technology itself. The world is witnessing a shift in technology and the Semantic Web is the direction the shift is moving towards. This would mean that the technology including that of GIS is moving towards the flexible solutions through knowledge based systems from static solution through current database systems. Hence, it is important to raise issues of integrating non-typical semantic data into it. This research work at least provides certain vision towards the direction the technology is taking to integrate these forms of data. It discusses the direction in terms of spatial integration. There are other data patterns like temporal data which need to be addressed too.

This concluding chapter begins with summarizing the work contribution that has been presented in previous chapters. It then discusses the contribution made to different related discipline. Lastly, the chapter concludes the future prospect and the direction of the research work in this field.
