**4.3.1 Spatial SPARQL queries**

FILTERs can be used to compare strings and derive results. The functions like regular expression which matches plain literal with no language tag can be used to match the lexical forms of other literals by using string comparison function. In addition, SPARQL FILTER uses the relational operators as = or > or < for the comparison and restrict to the results that they return. The FILTER principle can hence be extended in order to process the geospatial functions.

#### **Geoprocessing FILTER**

Geoprocessing functions need to be addressed through enriching the knowledge base with the spatial operations which is related to them during the execution of the query. The enrichment process should be rolled back after the results are returned into its original form iff the SELECT statement is used under the filter. The optimization of the SPATIAL\_FILTER is discussed later which highlights the management of the knowledge base during the execution of the SPARQL queries.

The following example demonstrates the syntax of geoprocessing filters in SPARQL. It could be seen that a new spatial filter through the keyword SPATIAL\_FILTER is introduced which helps the translation engine during the parsing process. The SPARQL statement with spatial filters in the example returns names of all the buildings in class feat:Building which are intersecting with the buffer of 2000 meters of the rivers in class feat:River with their respective rivers names.

```
SELECT ?name1 ?name2 
WHERE 
{ 
 ?feat1 feat:name ?name1 
 ?feat2 feat:name ?name2
```
 ?feat1 rdfs:type feat:River ?feat2 rdfs:type feat:Building

> SPATIAL\_FILTER [buffer (?x, 2000,?feat1)] SPATIAL\_FILTER [intersection (?y,?x,?feat2)]

#### **Georelationship FILTER**

}

In case of georelationship filter, it is straightforward as the enrichment process requires enriching the object properties imitating spatial relationship between objects through the results of the spatial operations at the database level. As with the previous case, the georelationship filter uses the keyword SPATIAL\_FILTER. This keyword parses the spatial components from the SPARQL statements. The following example illustrates the execution of SPARQL with these filters. The first feature is a feat:River which is of kind of feat:Feature, and the second feature is a feat:Building which is also of kind of feat:Feature. The SPATIAL\_FILTER selects the rivers and buildings which are touching spatially.

Spatialization of the Semantic Web 185

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

This research attempts to highlight the possibilities to integrate spatial technology in Semantic Web framework. It moves beyond the scope of data interoperability while presenting the concept and makes efforts to utilize the potentiality in other areas of the Semantic Web technologies. The underlying technologies of knowledge processing provide the Semantic Web capabilities to process the semantics of the information through close collaboration with the machine. It makes not only the understanding of data easier for achieving interoperability among different data sources, but it also provides valuable knowledge which could enrich the knowledge base in order to equip it with new knowledge through the knowledge management techniques. This helps the users understand the data

This research benefits from the advancement in Semantic Web technologies and its knowledge representation formalization tools and techniques. The primary principle of 4Ks processing is based on the knowledge formalization techniques. The research uses the case study of the industrial archaeology to demonstrate the possibility of implementation of application based on Semantic Web and utilizes the knowledge possessed by the archaeologists to manage the information recovered. This turns out to be an ideal case for the experimentation as the site for industrial archaeology is available for short duration of time. With the conventional technology it is difficult to manage the information due to share volume of data and the limitation of available time. It is however seen that with 4Ks implemented within the application prototype of the ArchaeoKM framework, the information could be managed. There has always been active involvement of archaeologists in every phase of design and development. The domain ontology and its axioms and theorems are based on their experiences. The enrichments of domain ontology through the

**5. Conclusion**

work in this field.

**5.1 Contribution**

better.

**5.1.1 In the industrial archaeology domain**


#### **4.3.2 Inference rules through SWRL**

}

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 object properties and corresponding spatial functions in database system.

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 carried out which are mentioned in the following sequences:


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) ^ spatialswrlb:Buffer(?x, 50, ?z) ^ spatialswrlb:Intersection(?z, ?y, ?res) FloodingLandParcel(?y) (2)

The result of this rule is that the individuals which respect the rule and belong to LandParcel, belong also to the concept FloodingLandParcel.
