**2.4.2 The ArchaeoKM architecture**

The GIS technology performs a group of five tasks to execute the result. These tasks as already been mentioned are acquisition of spatial data, spatial data management, database management, spatial data analysis and the spatial data visualization. The ArchaeoKM project attempts to complement the five major processing steps of a GIS through its four processing activities which it calls the processing steps of 4Ks: knowledge acquisition, knowledge management, knowledge visualization, knowledge analysis.

The knowledge acquisition task consists in general term defines metadata on data acquired during the survey process. The spatial data acquisition process is still involved during the process, but in addition metadata on these data are defined using a knowledge representation language. Actually, an ontology, which defines the semantic of the recovered features, is defined to capture and capitalized the knowledge of archeologists on the archaeological site. Hence the schema of the ontology is defined at this level. This is done by the help of a specialist on ontologies. The relationships and their semantics are stored into the ontology. This semantic could be provided through an example of the relation of "insideOf" which is transitive relationship. In mathematics, a binary relation R over a set X is transitive if whenever an element a is related to an element b, and b is in turn related to an

Spatialization of the Semantic Web 165

RDBMS. The method is reflected by the feature Semantic Annotation within the platform. These annotations are carried out through creating individual Resource Description Framework (RDF) triplets for each annotation process technology. RDF triplets also map

The next level is the semantic level, which manages the extracted knowledge. As stated, it is achieved through the ontological structure established through the descriptions, observations and rules defined by the archaeologists. These descriptions and rules are represented through different axioms in the domain ontology. Archaeologists are involved actively in this phase as they are the one best suited to provide entities and their relationships needed to build up the domain ontology. The semantic annotations from the Syntactic level will be indexed semantically to the entities of the domain ontology in this level. This semantic index through the identification process is the building block of the domain ontology and through semantic annotations provides a semantic view of the data. It also provides a global schema between various data sources making the data integration possible at certain level. This level represents a bridge between interpretative semantics in which users interpret terms and operational semantics in which computers handle symbols (Guarino, 1994). The knowledge is also managed through assigning semantic properties to the objects through proper

Fig. 3. The system architecture of the ArchaeoKM.

relationships with other objects.

the identified objects to the relevant classes in the domain ontology.

element c, then a is also related to c by the same kind relation. The ArchaeoKM platform deals with this issue.

The acquisition process constitutes of generation of knowledge base through enriching the ontology. The knowledge of archaeologists is used again to identify the recovered objects and enrich them in the ontology schema formulated. In short the process consists of populating the ontology with "individual" which represent objects recovered from the archaeological site. This creates a knowledge base from the ontology schema.

The knowledge management task consists of storing and the retrieving data along with its semantics. Knowledge is defined through the relationships and it is the relationships between individuals that create the real knowledge in the knowledge base. These relationships not only imply the relations between objects but also relation to their spatial signatures in spatial database. A specialized tool has to be developed in order to retrieve data from the ontology and from its spatial representation stored in a GIS. The ArchaeoKM platform deals with this issue.

The knowledge analysis task is the ability of the system to perform inferences on datasets. This cannot be undertaken without the help of the semantic definition on the archaeological objects. Usually inference or deduction is conducted on attributive data which are defined in the ontology. Today, no tool is defined to compute inference on the individuals of ontology and its spatial definition store in a spatial database. The ArchaeoKM platform deals with this issue.

The knowledge visualization task provides powerful visualization capabilities used for viewing spatial datasets and its semantics counterparts. Tools for the visualization of ontologies are of benefit to visualize the results of knowledge analysis. The ArchaeoKM platform deals with this issue.

As illustrated in figure 3 the system architecture of the ArchaeoKM platform is a three layered architecture with a structure for spatial component standing parallel against them.

The bottom level is the Syntactic level. This level contains all the information recovered from the site. Most of the data and documents collected during the excavation process are stored in their original formats. Certain data which needs to be stored in database system such as GIS data are stored in the RDBMS. This level basically performs as the repository of the dataset. One of the main tasks of the syntactic level is to explain the data. For a proper identification, the data needs to be analyzed with reference to the objects illustrated in the index. One of the first features within the application is the identification process.

A proper identification mechanism allows defining the identified objects. The ArchaeoKM platform utilizes the knowledge of archaeologists to identify the object. The identification is carried out by tagging the objects in the orthophoto of the site provided in the application. Attaching the semantic characteristics through semantic analysis on these objects generates knowledge. Different methods are used for the associating the semantic information according to the data pattern. Three distinct methods are applied to associate the semantic information which depends on the nature of the datasets with which it is associating with: Minimum Bounding Rectangles (MBRs) for the spatial data set, Uniform Resource Identifier (URI) for images and archive data and mapping to the data tables for datasets stored within 164 Semantics – Advances in Theories and Mathematical Models

element c, then a is also related to c by the same kind relation. The ArchaeoKM platform

The acquisition process constitutes of generation of knowledge base through enriching the ontology. The knowledge of archaeologists is used again to identify the recovered objects and enrich them in the ontology schema formulated. In short the process consists of populating the ontology with "individual" which represent objects recovered from the

The knowledge management task consists of storing and the retrieving data along with its semantics. Knowledge is defined through the relationships and it is the relationships between individuals that create the real knowledge in the knowledge base. These relationships not only imply the relations between objects but also relation to their spatial signatures in spatial database. A specialized tool has to be developed in order to retrieve data from the ontology and from its spatial representation stored in a GIS. The ArchaeoKM

The knowledge analysis task is the ability of the system to perform inferences on datasets. This cannot be undertaken without the help of the semantic definition on the archaeological objects. Usually inference or deduction is conducted on attributive data which are defined in the ontology. Today, no tool is defined to compute inference on the individuals of ontology and its spatial definition store in a spatial database. The ArchaeoKM platform deals with

The knowledge visualization task provides powerful visualization capabilities used for viewing spatial datasets and its semantics counterparts. Tools for the visualization of ontologies are of benefit to visualize the results of knowledge analysis. The ArchaeoKM

As illustrated in figure 3 the system architecture of the ArchaeoKM platform is a three layered architecture with a structure for spatial component standing parallel against them. The bottom level is the Syntactic level. This level contains all the information recovered from the site. Most of the data and documents collected during the excavation process are stored in their original formats. Certain data which needs to be stored in database system such as GIS data are stored in the RDBMS. This level basically performs as the repository of the dataset. One of the main tasks of the syntactic level is to explain the data. For a proper identification, the data needs to be analyzed with reference to the objects illustrated in the

A proper identification mechanism allows defining the identified objects. The ArchaeoKM platform utilizes the knowledge of archaeologists to identify the object. The identification is carried out by tagging the objects in the orthophoto of the site provided in the application. Attaching the semantic characteristics through semantic analysis on these objects generates knowledge. Different methods are used for the associating the semantic information according to the data pattern. Three distinct methods are applied to associate the semantic information which depends on the nature of the datasets with which it is associating with: Minimum Bounding Rectangles (MBRs) for the spatial data set, Uniform Resource Identifier (URI) for images and archive data and mapping to the data tables for datasets stored within

index. One of the first features within the application is the identification process.

archaeological site. This creates a knowledge base from the ontology schema.

deals with this issue.

platform deals with this issue.

platform deals with this issue.

this issue.

Fig. 3. The system architecture of the ArchaeoKM.

RDBMS. The method is reflected by the feature Semantic Annotation within the platform. These annotations are carried out through creating individual Resource Description Framework (RDF) triplets for each annotation process technology. RDF triplets also map the identified objects to the relevant classes in the domain ontology.

The next level is the semantic level, which manages the extracted knowledge. As stated, it is achieved through the ontological structure established through the descriptions, observations and rules defined by the archaeologists. These descriptions and rules are represented through different axioms in the domain ontology. Archaeologists are involved actively in this phase as they are the one best suited to provide entities and their relationships needed to build up the domain ontology. The semantic annotations from the Syntactic level will be indexed semantically to the entities of the domain ontology in this level. This semantic index through the identification process is the building block of the domain ontology and through semantic annotations provides a semantic view of the data. It also provides a global schema between various data sources making the data integration possible at certain level. This level represents a bridge between interpretative semantics in which users interpret terms and operational semantics in which computers handle symbols (Guarino, 1994). The knowledge is also managed through assigning semantic properties to the objects through proper relationships with other objects.

Spatialization of the Semantic Web 167

systems and their spatial extension are evident of the ability of database systems to manage spatial data. It however lacks the flexibility to adapt itself into new scenarios that might arise through generation of new information or changes in the contexts. This research carries these capabilities forward by using the spatial knowledge processing through knowledge tools which provides the proper data management in archaeology that addresses the

This chapter has presented the concept of the inclusion of spatial knowledge in handling the spatial nature of data recovered. This is new domain of research and probably one of its kinds. Hence it is important to understand the current state of art in both spatial and Semantic Web technologies. The next chapter thus discusses the state of art in the Semantic

The World Wide Web (WWW or the Web) is the single largest repository of information. The growth of Web has been tremendous since its evolvement both in terms of the content and the technology. The first generation Webs were mainly presentation based. They provided information through the Web pages but did not allow users to interact with them. In short they contained read only information. Moreover, the early pages were text only pages and do not contain multimedia data. These Web sites have higher dependency on the presentation languages as Hypertext Markup Languages (HTML) (Horrocks et al., 2004). With the introduction of eXtensible Markup Language (XML), the information within the pages became more structured. Those XML based pages could hold up the contents in more structured method but still lack the proper definition of semantics within the contents (Berners-Lee T., 1998). Needs of intelligent systems which could exploit the wide range of information available within the Web are widely felt. Semantic Web is envisaged to address the need. The term "Semantic Web" is coined by Tim Berners-Lee in his work (Berners-Lee et al., 2001) to propose the inclusion of semantic for better enabling machine-people cooperation for handling the huge information that exists

The term "Semantic Web" has been defined numerous time. Though there is no formal

The Semantic Web is not a separate Web but an extension of the current one, in which information is given well-defined meaning, better enabling computers and people to work in cooperation. It is a source to retrieve information from the Web (using the Web spiders from RDF files) and access the data through Semantic Web Agents or Semantic Web Services. Simply Semantic Web is data about data or metadata (Berners-Lee et al., 2001).

 A Semantic Web is a Web where the focus is placed on the meaning of words, rather than on the words themselves: information becomes knowledge after semantic analysis is performed. For this reason, a Semantic Web is a network of knowledge compared with what we have today that can be defined as a network of information (Mazzocchi,

 The Semantic Web provides a common framework that allows data to be shared and reused across application, enterprise and community boundaries (Herman et al., 2010).

definition of Semantic Web, some of its most used definitions are.

limitation in adaptation of the conventional technologies.

Web.

in the Web.

2000).

**3. The Semantic Web**

The top most level is the most concrete one as this level represents the organization of the knowledge on the semantic map through different visualizing tools. This level provides the user interfaces and they are visualized in form of Web pages as illustrated in figure 3. These Web pages represent knowledge which are generated through the knowledge management process discussed above. The pages are interrelated and can be used according to their relevance. The main representation of the knowledge is, however, demonstrated through Detail View pages. These pages are not only designed to illustrate the knowledge that has been generated and to manage it through the bottom two levels, but to also perform semantic research in order to gain new knowledge. Various techniques of the Semantic Web technology are being integrated within ArchaeoKM structure for acquiring new knowledge. Domain rules through inference engine provide one of those features in ArchaeoKM structure. In archaeology it is sometimes not possible to analyze the finding immediately and needs some properties or relationships to support them later. These inference rules provide the archaeologists such functionalities within the application.

In addition to the three levels, the system architecture contains components that facilitate the acquisition, validation, upgrade, management and analysis of the spatial knowledge. These components are packaged into the Spatial Facilitator as illustrated in figure 3. This component is responsible for analyzing the spatial data and providing results; either to update the current ontological structure in the semantic level or to populate the knowledge base. Through the inference capabilities in Semantic Web technology, new theories could be explored.
