**3.2 The knowledge base**

Description Logics supports serialization through the human readable forms of the real world scenario with the classification of concepts and individuals. Moreover, they support the hierarchical structure of concepts in forms of subconcepts/superconcepts relationships of a concept between the concepts of a given terminology. This hierarchical structure provides efficient inference through the proper relations between different concepts. The individual-concept relationship could be compared to instantiation of an object to its class in object-oriented concept. In this manner, the approach DL takes can be related to classification of objects in a real world scenario. Description logics provide a formalization

From Unstructured 3D Point Clouds to Structured Knowledge - A Semantics Approach 223

the language for building the descriptions is independent to each DL system. Thus, statements in the TBox and in the ABox can be identified as first-order logic or, in some

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

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

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

cases, a slight extension of it (Baader, et al., 2008).

logical formalization to the Ontologies (Baader, et al., 2007).

the inverse of another property, here again extending RDFS.

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

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.

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

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 language for building the descriptions is independent to each DL system. Thus, statements in the TBox and in the ABox can be identified as first-order logic or, in some cases, a slight extension of it (Baader, et al., 2008).
