**5.1.2 In the geospatial domain**

The 4Ks processing principle is implemented during the integration of spatial technology. The domain ontology is modified to adjust the spatial components into it. The research work considers the advancement in spatial technology in modern database systems. It implements the notations standardized by OGC simple feature specification (Herring, 2010) during the inclusion of the spatial components as axioms into the ontology. The spatial technologies provide spatial functions and operations to perform spatial analysis. These functions and operations are categorized into four major categories as documented in PostGIS documentation. However, the research implements functions under geoprocessing and georelationship functions as these two categories consist of mostly all the spatial functions. Geoprocessing functions are implemented as class axioms which relate to the classes containing features through the respective object properties. Likewise the georelationship functions are treated as object properties relating the classes containing features spatially to each other.

The knowledge acquisition process comprises of acquiring spatial signatures of the object. In general they are acquired during the identification process. However, the spatial signatures are formalized during spatial annotations of the objects which are then stored in database as spatial data type. The spatial operations and functions which are encoded as classes and object properties within the ontology provide the management of spatial knowledge. The ontology was spatially enriched through the spatial operations and functions at the database level. This enriched knowledge base can be inferred spatially through the spatial built-ins for SWRL proposed in the research. The research also proposes the spatial filters for query language of the Semantic Web (SPARQL) (Harris & Seaborne, 2010).

The benefits to geospatial community are prominent. The shift from data oriented to knowledge oriented GIS gives the GIS an edge. The flexibility of knowledge based systems should add the flexibility to GIS in terms of data acquisition, data management and data analysis. The data acquisition process though still remains to the conventional digitization techniques; the possibility of linking it up to its semantics adds knowledge to the whole process. This added knowledge then could be utilized for different purposes including semantic interoperation between other data from other sources. However, this paper discusses in terms of knowledge management and analysis. The knowledge query through 186 Semantics – Advances in Theories and Mathematical Models

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.,

The 4Ks processing principle is implemented during the integration of spatial technology. The domain ontology is modified to adjust the spatial components into it. The research work considers the advancement in spatial technology in modern database systems. It implements the notations standardized by OGC simple feature specification (Herring, 2010) during the inclusion of the spatial components as axioms into the ontology. The spatial technologies provide spatial functions and operations to perform spatial analysis. These functions and operations are categorized into four major categories as documented in PostGIS documentation. However, the research implements functions under geoprocessing and georelationship functions as these two categories consist of mostly all the spatial functions. Geoprocessing functions are implemented as class axioms which relate to the classes containing features through the respective object properties. Likewise the georelationship functions are treated as object properties relating the classes containing features spatially to

The knowledge acquisition process comprises of acquiring spatial signatures of the object. In general they are acquired during the identification process. However, the spatial signatures are formalized during spatial annotations of the objects which are then stored in database as spatial data type. The spatial operations and functions which are encoded as classes and object properties within the ontology provide the management of spatial knowledge. The ontology was spatially enriched through the spatial operations and functions at the database level. This enriched knowledge base can be inferred spatially through the spatial built-ins for SWRL proposed in the research. The research also proposes the spatial filters for query

The benefits to geospatial community are prominent. The shift from data oriented to knowledge oriented GIS gives the GIS an edge. The flexibility of knowledge based systems should add the flexibility to GIS in terms of data acquisition, data management and data analysis. The data acquisition process though still remains to the conventional digitization techniques; the possibility of linking it up to its semantics adds knowledge to the whole process. This added knowledge then could be utilized for different purposes including semantic interoperation between other data from other sources. However, this paper discusses in terms of knowledge management and analysis. The knowledge query through

language of the Semantic Web (SPARQL) (Harris & Seaborne, 2010).

2004). It is the last K, Knowledge Analysis.

**5.1.2 In the geospatial domain**

each other.

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 semantic behind the results provides support to their authenticity.

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 experimentation performed.
