**RDFS**

174 Semantics – Advances in Theories and Mathematical Models

W3C has defined five major reasons for developing the standard (Klyne et al., 2004). They focus on automatization of the information processing through serialization. That means the contents inside the documents are machine processable. In order for the documents to be machine processable they need to be machine readable and since the syntax of RDF is based on XML, it provides a mechanism to represent the information in machine readable manner. The RDF (Resource Description Framework) is a graph data model. It is basically a framework to represent information on the Web. It has also been assigned as the standard model for data interchange on the Web by W3C because it can merge different sets of data irrespective to the underlying schema. RDF is conceptualized through graph data model which demonstrates the underlying structure of its expression. The nodes in the graph model are resources which can represent Uniform Resource Identifiers (URI reference or simply URIRef) or literals or even blank. The link in the graph representing properties are generally URI references. The literals within RDF expressions are generally assigned values of certain data types. RDF syntax is primarily based on its predecessor XML and is defined by RDF abstract syntax. This abstract syntax is the syntax over which the formal semantic are defined. It is a set of triples known as RDF graph (Klyne et al., 2004). It consists three parts which are normally called RDF triplet and represent a statement of relationship

Knowledge representation has been described in five distinct roles it plays in (Davis et al.,

 A surrogate for the thing itself used to enable an entity to determine consequences by thinking rather than acting, i.e., by reasoning about the world rather than acting it. A set of ontological commitments, i.e., an answer to the question: In what terms should

A fragmentary theory of intelligent, reasoning, expressed in terms of three components

A medium for pragmatically efficient computation, i.e., the computational environment

A medium for human to express, i.e., a language human expresses things about the

With these roles in view, different languages that represent the knowledge have been conceived over the time. They vary in terms of their characteristics, expressive power and computational complexity. The effectiveness of any representation language can be

 The expressiveness of the language is measured in terms of the range through which the language can use its constructs to describe the components in knowledge

 The strictness in the language is measured through the consistency and satisfiability within the knowledge model. The consistency and satisfiabilty issue is important in any

The representation's fundamental conception of intelligent reasoning

The set of inferences the representation sanctions; and

The set of inferences it recommends

in which thinking is accomplished.

between the objects.

1993). Those roles are

world.

measured in:

model.

**3.2.2 The Knowledge Layer**

I think about the world?

RDFS or the RDF Schema is the semantic extension of RDF. The applications using RDF uses it to describe its resources and those descriptions can be modeled as relationships among Web resources. These models constitute of interrelationships among the resources. They are carried out through the named properties and values. It however lacks the mechanism of defining the relationships between properties and other resources. Furthermore RDF data models do not declare these properties. They are hence information without any semantics. RDFS is designed to address these shortcomings. RDFS provides mechanisms for describing groups of related resources and the relationships between these resources.

## **The Web Ontology Language (OWL)**

OWL or the Web Ontology Language is a family of knowledge representation language to create and manage ontologies. It is in general term an extension of RDFS with addition to richer expressiveness that RDFS lacks through its missing features (Antoniou & Harmelen, 2003). The OWL Working Group has approved two versions of OWL: OWL 1 and OWL 2. This research work uses OWL 1 for the applications of ontology as this version was the most used version at the time of research. The later version of OWL 1 was just evolving during the period. This research work discusses its activities in terms of OWL 1.

The expressiveness of OWL depends upon the level of serialization. The expressiveness of OWL comes at the cost of computational efficiency and reasoning effectiveness. This tradeoff between expressiveness and reasoning support was addressed through classifying OWL into three sub languages by the W3C Web Ontology working group.

OWL Full contains the maximum expressiveness but may lack in computational processing capability. It may also have restricted reasoning efficiency. OWL Full is completely compatible with RDF/RDFS both syntactically and semantically. OWLDL is compatible to the components of description logics and provides the functionalities of DLs. It provides the complete computational efficiency and reasoning capabilities. It is sub language of OWL Full with all OWL language constructs which could be used only through certain restrictions (McGuinness & Harmelen, 2004). This restriction is even more in OWL Lite – the third sublanguage of OWL. The advantage of this language is its easiness to understand and implement but the drawback is it is just a simple and fast migration from thesauri and other taxonomies.

#### **The SPARQL language**

It has been stated before that RDF statements store data in the form of informative contents. In this manner, it could be easily argued RDF documents are datasets complimenting the data storage capability of its conventional counterparts as database systems. As database systems provide efficient retrieval of the data through its query language in form of

Spatialization of the Semantic Web 177

abstract syntax for Horn-like rules. The SWRL as the form, antecedentconsequent, where both antecedent and consequent are conjunctions of atoms written a1^ ... ^ an. Atoms in rules can be of the form C(x), P(x,y), Q(x,z), sameAs(x,y), differentFrom(x,y), or builtIn(pred, z1, …, zn), where C is an OWL description, P is an OWL individual-valued property, Q is an OWL data-valued property, pred is a datatype predicate URIref, x and y are either individual-valued variables or OWL individuals, and z, z1, … zn are either data-valued variables or OWL data literals. An OWL data literal is either a typed literal or a plain literal. Variables are indicated by using the standard convention of prefixing them with a question mark (e.g., ?x). URI references (URIrefs) are used to identify ontology elements such as classes, individual-valued properties and data-valued properties. For instance, the following rule asserts that one's parents' brothers are one's uncles where parent, brother and uncle are

The set of built-ins for SWRL is motivated by a modular approach that will allow further extensions in future releases within a (hierarchical) taxonomy. SWRL's built-ins approach is also based on the reuse of existing built-ins in XQuery and XPath, which are themselves based on XML Schema by using the Datatypes. This system of built-ins should also help in the interoperation of SWRL with other Web formalisms by providing an extensible, modular built-ins infrastructure for Semantic Web Languages, Web Services, and Web applications. Many built-ins are defined and some of most common built-ins can be found in (Horrocks et al., 2004). These built-ins are keys for any external integration. The research work develops

The Semantic Web, a set of technologies complementing the conventional Web tools proposed by Sir Tim Berners-Lee is seen as the most probabilistic approach to reach the goal of semantic interoperability. The Semantic Web is envisaged as an extension to the existing Web from a linked document repository into the platform where information is provided with the semantic allowing better cooperation between people and their machines. This is to be achieved by augmenting the existing layout information with semantic annotations that add descriptive terms to Web content, with meaning of such terms being defined in ontologies (Horrocks et al., 2004). Ontologies play crucial role in conceptualizing a domain and thus play an important role in enabling Web-based knowledge processing, sharing and

This research takes advantages of the tools of Semantic Web technology to make a case of information management through knowledge. The case study of Industrial Archaeology fits perfectly to put forward the concept of information handling through knowledge as the domain generates huge and heterogeneous dataset. In addition the sites are not preserved for continuing excavation as in case of the conventional archaeology, making it ideal for utilizing knowledge techniques to manage the information because of the flexibility in knowledge techniques to handle information long after they are collected. The definition of a domain ontology representing the site is sketched out by the archaeologists. It is again their task to fill in knowledge in the domain ontology to make it a knowledge base where one can reason to derive new knowledge. Archaeologists use collaborative Web platform

parent(?x, ?p) ^ brother(?p, ?u) uncle(?x, ?u) (1)

all individual-valued properties.

**3.3 Discussion**

reuse between applications.

spatial built-in for the integration of spatial data structure.

Structured Query Language (SQL), the dataset within a RDF document can be retrieve through the query language called SPARQL. As with its counterpart SPARQL is also used to manage the RDF document. It is a key component of Semantic Web technology. As a query language, SPARQL is "data-oriented" in that it only queries the information held in the models; there is no inference in the query language itself. SPARQL does not do anything other than taking the description of what the application wants, in the form of a query, and returns that information in the form of a set of bindings or an RDF graph. In addition, the SPARQL is able to query OWL ontologies which use RDF graphs to structure them. However, no inferences are possible on that structure. SWRL is used for that purpose.

The query language has been standardized by W3C and has been recommended as official query language to retrieve RDF data (Prud'hommeaux & Seaborne, 2008).

SPARQL queries the RDF data in four distinct forms.


#### **The SWRL language**

An inference process consists of applying logic in order to derive a conclusion based on the observations and hypothesis. In computer science Inferences are applied through inference engines. These inference engines are basically computer applications which derive answers from a knowledge base. These engines depend on the logics through logic programming.

The horn logic more commonly known Horn clause is a clause with at most one positive literal. It has been used as the base of logic programming and Prolog languages (Sterling & Shapiro, 1994) for years. These languages allow the description of knowledge with predicates. Extensional knowledge is expressed as facts, while intentional knowledge is defined through rules (Spaccapietra et al., 2004). These rules are used through different Rule Languages to enhance the knowledge possess in ontology. The Horn logic has given a platform to define Horn-like rules through sub languages of RuleML (Boley, 2009). There have been different rule languages that have emerged in last few years. Some of these languages that have been evolving rapidly are Semantic Web Rule Language (SWRL) and JenaRule. Both have their own built-ins to support the rules. This research work uses SWRL to demonstrate the concepts but it could be applied to others rule language based on Horn clauses.

Semantic Web Rule Language (SWRL (Horrocks et al., 2004)) is a rule language based on the combination of the OWL-DL (SHOIN(D)) with Unary/Binary Datalog RuleML which is a sublanguage of the Rule Markup Language. One restriction on SWRL called DL-safe rules was designed in order to keep the decidability of deduction algorithms. This restriction is not about the component of the language but on its interaction. SWRL includes a high-level 176 Semantics – Advances in Theories and Mathematical Models

Structured Query Language (SQL), the dataset within a RDF document can be retrieve through the query language called SPARQL. As with its counterpart SPARQL is also used to manage the RDF document. It is a key component of Semantic Web technology. As a query language, SPARQL is "data-oriented" in that it only queries the information held in the models; there is no inference in the query language itself. SPARQL does not do anything other than taking the description of what the application wants, in the form of a query, and returns that information in the form of a set of bindings or an RDF graph. In addition, the SPARQL is able to query OWL ontologies which use RDF graphs to structure them. However, no inferences are possible on that structure. SWRL is used for that purpose.

The query language has been standardized by W3C and has been recommended as official

SELECT returns the resulted dataset from this form. The results could be used accessed

CONSTRUCT form constructs a RDF graph through running the query to derive the

ASK form is used to ask the authenticity of the query pattern. That means whether

An inference process consists of applying logic in order to derive a conclusion based on the observations and hypothesis. In computer science Inferences are applied through inference engines. These inference engines are basically computer applications which derive answers from a knowledge base. These engines depend on the logics through logic

The horn logic more commonly known Horn clause is a clause with at most one positive literal. It has been used as the base of logic programming and Prolog languages (Sterling & Shapiro, 1994) for years. These languages allow the description of knowledge with predicates. Extensional knowledge is expressed as facts, while intentional knowledge is defined through rules (Spaccapietra et al., 2004). These rules are used through different Rule Languages to enhance the knowledge possess in ontology. The Horn logic has given a platform to define Horn-like rules through sub languages of RuleML (Boley, 2009). There have been different rule languages that have emerged in last few years. Some of these languages that have been evolving rapidly are Semantic Web Rule Language (SWRL) and JenaRule. Both have their own built-ins to support the rules. This research work uses SWRL to demonstrate the concepts but it could be applied to others rule language based on Horn

Semantic Web Rule Language (SWRL (Horrocks et al., 2004)) is a rule language based on the combination of the OWL-DL (SHOIN(D)) with Unary/Binary Datalog RuleML which is a sublanguage of the Rule Markup Language. One restriction on SWRL called DL-safe rules was designed in order to keep the decidability of deduction algorithms. This restriction is not about the component of the language but on its interaction. SWRL includes a high-level

query language to retrieve RDF data (Prud'hommeaux & Seaborne, 2008).

by the APIs as well could be serialized into XML or RDF graph.

solution in solution sequence and then combines these triplets.

SPARQL queries the RDF data in four distinct forms.

certain query pattern returns a solution or not.

**The SWRL language** 

programming.

clauses.

DESCRIBE forms describe the RDF data about its resources.

abstract syntax for Horn-like rules. The SWRL as the form, antecedentconsequent, where both antecedent and consequent are conjunctions of atoms written a1^ ... ^ an. Atoms in rules can be of the form C(x), P(x,y), Q(x,z), sameAs(x,y), differentFrom(x,y), or builtIn(pred, z1, …, zn), where C is an OWL description, P is an OWL individual-valued property, Q is an OWL data-valued property, pred is a datatype predicate URIref, x and y are either individual-valued variables or OWL individuals, and z, z1, … zn are either data-valued variables or OWL data literals. An OWL data literal is either a typed literal or a plain literal. Variables are indicated by using the standard convention of prefixing them with a question mark (e.g., ?x). URI references (URIrefs) are used to identify ontology elements such as classes, individual-valued properties and data-valued properties. For instance, the following rule asserts that one's parents' brothers are one's uncles where parent, brother and uncle are all individual-valued properties.

$$\text{parent(?x, ?p)} \land \text{ brother(?p, ?u)} \to \text{uncle(?x, ?u)} \tag{1}$$

The set of built-ins for SWRL is motivated by a modular approach that will allow further extensions in future releases within a (hierarchical) taxonomy. SWRL's built-ins approach is also based on the reuse of existing built-ins in XQuery and XPath, which are themselves based on XML Schema by using the Datatypes. This system of built-ins should also help in the interoperation of SWRL with other Web formalisms by providing an extensible, modular built-ins infrastructure for Semantic Web Languages, Web Services, and Web applications. Many built-ins are defined and some of most common built-ins can be found in (Horrocks et al., 2004). These built-ins are keys for any external integration. The research work develops spatial built-in for the integration of spatial data structure.

#### **3.3 Discussion**

The Semantic Web, a set of technologies complementing the conventional Web tools proposed by Sir Tim Berners-Lee is seen as the most probabilistic approach to reach the goal of semantic interoperability. The Semantic Web is envisaged as an extension to the existing Web from a linked document repository into the platform where information is provided with the semantic allowing better cooperation between people and their machines. This is to be achieved by augmenting the existing layout information with semantic annotations that add descriptive terms to Web content, with meaning of such terms being defined in ontologies (Horrocks et al., 2004). Ontologies play crucial role in conceptualizing a domain and thus play an important role in enabling Web-based knowledge processing, sharing and reuse between applications.

This research takes advantages of the tools of Semantic Web technology to make a case of information management through knowledge. The case study of Industrial Archaeology fits perfectly to put forward the concept of information handling through knowledge as the domain generates huge and heterogeneous dataset. In addition the sites are not preserved for continuing excavation as in case of the conventional archaeology, making it ideal for utilizing knowledge techniques to manage the information because of the flexibility in knowledge techniques to handle information long after they are collected. The definition of a domain ontology representing the site is sketched out by the archaeologists. It is again their task to fill in knowledge in the domain ontology to make it a knowledge base where one can reason to derive new knowledge. Archaeologists use collaborative Web platform

Spatialization of the Semantic Web 179

research. It could be conceived from earlier discussions that the integration process requires adjustments of the spatial components within the ontological framework. This chapter is dedicated to discuss the steps and process of this adjustment. The spatial signature of objects plays an important role in determining them. The identification of objects is the process of signing these spatial signatures on them. These signatures should be integrated within the semantics of the objects seamlessly in order to process the spatial knowledge through the knowledge technology. It should be noted however that the Semantic Web technologies are in the maturation process and hence there exists certain processing problems within especially for the non-conventional data type as that of spatial data. Thus, it needs to be sorted out through the existing tested techniques. The research in GIS systems uses the capabilities of existing RDBMS to process the spatial data through spatial

The Semantic Web stack discussed in the previous chapter can be updated to address the inclusion of a spatial component. Every tangible object has its spatial signature and thus it becomes indispensable to address the spatial component within its semantic framework. The Semantic Web technologies and its architecture are mostly influenced by the nature of information available on the Internet. Hence, these levels deals mostly with managing the semantic based information through knowledge technologies. However, in recent years there has been huge surge of other forms of information on Web platform and they need to be managed as well. With the advancement in spatial technologies, the trend of disseminating spatial information through Web based environment is rapidly growing. This has raised the issue of the integration of spatial component into the Semantic Web

A layer representing geospatial data in the Semantic Web stack can be placed just above the knowledge layers as could be seen in figure 7. As the technologies within knowledge level are standardized by W3C, the geospatial layer needs to be above the level. However, the technologies within knowledge level needs to blend spatial components seamlessly both syntactically and semantically to maintain the satisfiability required for the consistency of the ontology. This integration procedure should be adjusted within the knowledge tool within the knowledge level of the stack. This approach thus uses the knowledge techniques through adding the spatial structures within them and implementing the spatial knowledge processing along with semantic knowledge processing. The first Semantic Web tool that comes direct in contact with the integration procedure is the structural schema of the knowledge base which is termed as top level ontology in general sense. The top level ontology is the structural schema that represents the nature of knowledge the ontology possesses. It should include the components to adjust the behavior of the knowledge base. Hence the initial task that needs to be adjusted within any top level ontology to perform

The top level ontology is the structural schema that represents the nature of knowledge the ontology possesses. It should be noted that the top level ontology is syntactically presented through OWL/RDF and contains the top level concepts of the domain. Among these top level concepts, the concepts presenting the spatial components for storage, retrieval and processing of the spatial knowledge should be present. Moving down to the enrichment process, the spatial signatures are mapped to the objects within the knowledge base is again

spatial knowledge processing is to include spatial components within it.

operations and functions and use the results of these processes.

framework.

based on Semantic Web technology to identify the objects and define them in the ontology. These objects once defined, performs as common schemas between data sources to achieve a sense of data interoperability. The definitions of objects add semantics to the objects and thus adding knowledge about the objects. Knowledge techniques based on Description Logics (DLs) exploit these semantics to manipulate implicit knowledge within the knowledge base. Inference engines utilize the definition of DLs to infer the knowledge base through Horn based rules. The knowledge base stored in OWL syntactic structure is inferred through SWRL to infer the rules. This inference is complimented through querying with SPARQL.

Carrying the discussion from last chapter, this research attempts to use the Semantic Web techniques to perform spatial analysis in form of spatial SPARQL and spatial SWRL. The spatial analysis through Semantic Web can only be possible through providing spatial signatures to the defined objects in the ontology. This will allow the knowledge techniques to process spatial solutions. The spatial integration is carried out through OWL/RDF again and the spatial management is carried out again through tools as SWRL and SPARQL. This simplistic yet but effective approach of spatial integration into Semantic Web technologies provides the possibility to include different modes of data into its framework.

The Semantic Web stack shown in figure 5 and 6 can adjust a layer of spatial information into it. The research proposes such an arrangement in the stack. A layer of spatial data mixing seamlessly with the semantic proposition in the layer Ontology through its OWL/RDF based syntax can be envisaged. This layer since uses the standard syntax of OWL/RDF can perform spatial queries through SPARQL or infer rules through standards as SWRL. The next chapter discusses this integration process of spatial technology and Semantic Web technology which is undertaken by defining spatial FILTERs for SPARQL queries and spatial Built-ins for SWRL rules. Ideally the layer should be the top most layer of knowledge level but spatial layer does not yet possess any standards that are standardized by W3C so could not be placed there. It is hence placed as the bottom layer in the certificate level. The next chapter discusses this adjustment in stack in detail and how to apply spatial queries and rules on any existing ontology.
