**5.1 Contribution**

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 better.

## **5.1.1 In the industrial archaeology domain**

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

Spatialization of the Semantic Web 187

SPARQL or knowledge inference through SWRL to the spatially rich knowledge base generates new knowledge which is more authentic in a sense that this new result is the manipulation of knowledge base through the existing one. It is not just data any more. The

This research has provided GIS community an alternative to conventional spatial data analysis through spatial rules. It can be opined that the proposed approach of knowledge analysis is apparent and less complicated to the conventional one. As the spatial rules could be combined with general rules they have wider implications. Additionally, the rules are based on formal logics which relate to day-to-day human interpretations; they should be easy to understand and implement. Consequently, the research proposes a rule based approach for spatial analysis and provides an evidence of possibilities through the

A spatial layer in the Semantic Web stack presented through this paper is not enough to address the overall problems of non-semantic data within the framework but at least there is something to start with. The full potential of underlying knowledge techniques through the reasoning or inferring capabilities within Semantic Web has not been identified in Geospatial community. The primary focus on these technologies is to achieve data interoperability within different data sources (Cruz, 2004; Cruz et al., 2004) Even W3C concentrated its priority in proposing comprehensive geospatial ontology acceptable to all through its Geospatial Incubator Group (Lieberman et al., 2007). All these research works show that the emphasis on using geospatial ontology lie in achieving data interoperability and thus ignores the capabilities of underlying knowledge techniques for carrying out complex spatial analysis. This research presented a concept to carry out spatial analysis

The realization of spatial integration into Semantic Web framework is demonstrated through a demonstration application. The application demonstrates that through a suitable translation engine, it is possible to infer the spatially enriched knowledge base in order to deduce spatial knowledge. The translation engine developed within the demonstration application translates the spatial built-ins and enriches the knowledge base through results of spatial operations of these built-ins making the knowledge base ready to be inferred.

This research work has highlighted the benefits of tools and techniques of the Semantic Web and especially underlying knowledge technologies and their usages with the spatial technologies for the efficient management of spatial information. It has also been discussed that the approach presented here benefits both the Semantic Web and spatial technology. The research activities has just initiated the integration of spatial technology into the Semantic Web framework and still has long way to go. This section presents few areas

Researches in the field of spatial technology within the Semantic Web framework have not moved beyond geospatial ontology and the possibility of semantic interoperability between

semantic behind the results provides support to their authenticity.

experimentation performed.

**5.1.3 In the Semantic Web domain**

through inferring knowledge base spatially.

where the research work could be continued in this area.

**5.2 Way forward**

identification of objects are carried out by them. It is the first K, Knowledge Acquisition. The knowledge acquired through the identification process is managed through defining relationships. It is again the archaeologists with the ArchaeoKM platform to manage knowledge through adjuring proper relationships (which reflects archaeologists view of the world) to the objects and semantically annotating them to the data and documents collected. The process is second K: Knowledge Management. The third K is Knowledge Visualization which generally means that knowledge identified and managed could be visualized through the interfaces of the ArchaeoKM platform. The knowledge base enriched and managed through the collaborative approach of archaeologists could be analyzed through inferring the knowledge base with rules formulated by archaeologists. These rules are inferred through SWRL – a rule language for Semantic Web standardized by W3C (Horrocks et al., 2004). It is the last K, Knowledge Analysis.
