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

222 Semantics – Advances in Theories and Mathematical Models

to knowledge representation of real world situations. This means it should provide the logical replies to the queries of real world situations. This is currently most researched topic in this domain. The results are highly sophisticated reasoning engines which utilize the capabilities of expressiveness of DLs to manipulate the knowledge. A Knowledge Representation system is a formal representation of a knowledge described through different technologies. When it is described through DLs, they set up a Knowledge Base (KB), the contents of which could be reasoned or infer to manipulate them. A knowledge base could be considered as a complete package of knowledge content. It is, however, only a subset of a Knowledge Representation system (KR) that contains additional components.

Fig. 4. The Architecture of a knowledge representation system based on DLs.

(equivalent to methods or properties in OO concept) to those instances.

Baader (Baader, 2006) sketches the architecture of any KR system based on DLs. It could be seen the central theme of such a system is a Knowledge Base (KB). The KB constitutes of two components: the TBox and the ABox. Where **TBox** statements are the *terms* or the *terminologies* that are used within the system domain. In general they are statements describing the domain through the controlled vocabularies. For example in terms of a social domain the TBox statements are the set of concepts as *Rail, train, signal* etc. or the set of roles as *hasGeometry, hasDetectionAlg, hasCharacteristics* etc. ABox in other hand contains assertions to the TBox statements. In object oriented concept, **ABox** statements compliant **TBox** statements through instantiating what is equivalent to classes in TBox and relating the roles

The DLs are expressed through the *concepts* and *roles* of a particular domain. This complements well with the fact how knowledge is expressed in the general term. Concepts are sets of classes of individual objects. Where classes provide an abstraction mechanism for grouping resources with similar characteristics (Horrocks, et al., 2008). The concepts can be organized into superclass-subclass hierarchy which is also known as taxonomy. It shares the object-oriented concepts in managing the hierarchy of superconcept-subconcept. The subconcepts are specialized concepts of their super-concepts and the super-concepts are generalized concepts of their sub-concepts. For an example all individuals of a class must be individuals of its superclass. In general all concepts are subsumed by their superclass. In any graphical representation of knowledge, concepts are represented through the nodes. Similarly the roles are binary relationship between concepts and eventually the relationships of the individuals of those concepts. They are represented by links in the graphical representation of knowledge. The description language has a model-theoretic semantics as The association of knowledge with Semantic Web has provided a scope for information management through the knowledge management. Since both the technologies use ontology to conceptualize the scenarios, Semantic Web technology could provide a platform for developments of knowledge management systems (Uren, et al., 2006). The ontologies are core to both the technologies in whichever methods they are defined. The Semantic Web defines ontologies, (Gruber, 2008) through XML based languages and with the advancements in these languages. Within the computer science domain, ontologies are seen as a formal representation of the knowledge through the hierarchy of concepts and the relationships between those concepts. In theory ontology is a "*formal, explicit specification of shared conceptualization"* (Gruber, 2008)*.* In any case, ontology can be considered as formalization of knowledge representation where the Description Logics (DLs) provide logical formalization to the Ontologies (Baader, et al., 2007).

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, et al., 2009). The OWL Working Group has approved two versions of OWL: OWL 1 and OWL 2, (Grau, et al., 2008). The Web Ontology Language (OWL) is intended to be used when the information contained in documents needs to be processed by applications and not by human (Antoniou, et al., 2009). The OWL language has direct influence from the researches in Description Logics and insights from Description Logics particularly on the formalization of the semantics. OWL takes the basic fact-stating ability of RDF (Allemang, et al., 2008) and the class- and property-structuring capabilities of RDF Schema and extends them in important ways. OWL own the ability to declare classes, and organise these classes in a subsumption ("subclass") hierarchy, as can RDF Schema. OWL classes can be specified as logical combinations (intersections, unions, or complements) of other classes, or as enumerations of specified objects, going beyond the capabilities of RDFS. OWL can also declare properties, organize these properties into a "subproperty" hierarchy, and provide domains and ranges for these properties, again as in RDFS. The domains of OWL properties are OWL classes, and ranges can be either OWL classes or externally-defined datatypes such as string or integer. OWL can state that a property is transitive, symmetric, functional, or is the inverse of another property, here again extending RDFS.

Add to that, OWL pocess the ability to specify which objects (also called "individuals") belong to which classes, and what the property values are of specific individuals. Equivalence statements can be made on classes and on properties, disjointness statements can be made on classes, and equality and inequality can be asserted between individuals.

However, the major extension over RDFS is the ability in OWL to provide restrictions on how properties behave that are local to a class. OWL can define classes where a particular property is restricted so that all the values for the property in instances of the class must belong to a certain class (or datatype); at least one value must come from a certain class (or

From Unstructured 3D Point Clouds to Structured Knowledge - A Semantics Approach 225

These built-ins are keys for any external integration. They help in the interoperation of SWRL with other formalism and provide an extensible infrastructure knowledge based applications. Actually, *Comparisons Built-Ins, Math Built-Ins and Built-Ins for Strings* are already implemented within lots of platform for ontology management like protégé. In the actual work, new processing and topological built-in for the integration of 3D processing

Semantic Web technology is slowly modernizing the application of knowledge technologies, and though they existed before the Semantic Web, the implementation in their fullness is just being realized. Our actual research, materialized by WiDOP project relay on the above mentioned concept and technologies. In fact, this research benefits from the existing OWL languages, the existent inference engines through the inference rules and reasoning engines to reason the knowledge. However, the actual research works moves beyond semantic reasoning and semantic rule processing and attempts to implement new 3D processing and topological rule inference integrating the correspondent processing and topological built-Ins components in its structure to resolve the problem of object detection and annotation in 3D

The problem of automatic object reconstruction remains a difficult task to realize in spite of many years of research. Major problems result from geometry and appearance of objects and their complexity, and impact on the collected data. For example, variations in a viewpoint may destroy the adjacency relations inside the data, especially when the object surface shows considerable geometrical variations. This dissimilarity affects geometrical or topological relations inside the data and even gets worse, when partial occlusions result in a disappearance of object parts. Efficient strategies therefore have to be very flexible and in principle need to model almost all factors having impact of the representation of an object in a data set. That leads to the finding, that at first a semantic model of a scene and the objects existing therein is required. Such a semantic description should be as close to the reality as possible and as necessary to take most relevant factors into account, which may have impact on later analysis steps. At least this comprises the objects to be extracted with their most characteristic features (geometry, shape, texture, orientation,...) and topological relations among each other. The decision upon features to be modelled should be affected by other important factors in an analysis step like characteristics of the data, the algorithms and their important features. Such a model might be supported by a DL-OWL ontology structure formed out of RDFS nodes and properties where the nodes represent classes or objects as their instances and the links show relationships of various characteristics. Such a network then contains the knowledge of that type of scene, which has to be processed. This knowledge base will act as basis for further detection and annotation activities and has to

Up to this point, the new conception is still in concordance to other knowledge related set ups, although the degree of modelling goes farther because all relevant scene knowledge

and topological knowledge are integrated respectively.

point clouds based on semantic knowledge.

**4. Overview of the general WiDOP model** 

work in cooperation with numerical algorithms.

**3.5 Swrl built-ins** 

**3.6 Discussion** 

datatype); there must be at least certain specific values; and there must be at least or at most a certain number of distinct values.

#### **3.4 Semantic Web Rule Language (SWRL)**

An inference process consists of applying logic in order to derive a conclusion based on observations and hypothesis. In computer science, interferences 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, et al., 2009) 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, et al., 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 Jena Rule. Both have their own built-ins to support the rules. With the actual work, SWRL language is used to rich the target concepts but it could be applied to others rule language based on Horn clauses.

Semantic Web Rule Language (Valiente-Rocha, et al., 2010) is a rule language based on the combination of the OWL-DL 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 abstract syntax for Horn-like rules. The SWRL as the form, *antecedent*→*consequent*, 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 URI ref, x and y are either individualvalued 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 (URI refs) are used to identify ontology elements such as classes, individual-valued properties and data-valued properties. For instance, the following rule, equation 1, asserts that one's parents' brothers are one's uncles where parent, brother and uncle are all individual-valued properties.

$$\text{Parent(?x, ?p)} \land \text{Brother(?p, ?u)} \to \text{Unclle(?x, ?u)} \tag{1}$$

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 (OConnor, et al., 2008).
