**1. Introduction**

The abstraction of the real world melds the semantics of its objects with the spatial characteristics seamlessly. This is visible in a way the human perceives the real world where it is often difficult to pin point the spatial characteristics of the objects from their semantics. In other words the spatial characteristics are generally hidden with the semantics of the objects. As for example, describing relations of objects the terms near, far or touching are often used which are spatial relations but in general considered as semantic properties which is not true. Hence, it is a trend to consider that the spatial behaviors of objects are parts of its semantics. Similar approaches where the spatial properties are considered as part of semantics have been translated in technical advancements made by the technologies. There is a general trend to mix up spatial components in the semantics or the semantics in the spatial components within technologies. For instance, a classic GIS ignores semantics of objects to focus on the spatial components whereas a non GIS uses spatial components as the semantic parameters of the objects. As the technology is getting matured, it is moving closer to the human perception of the real world. Today, the knowledge management is being researched in real sense to model and to manage knowledge possessed by humans which is basically the perception of the real world.

The emergence of Internet technologies has provided a strong base to share the information in a wider community. As the needs of information have grown it has become necessary to represent them in a proper and meaningful way. It involves attesting semantics to the documents. The major approach to attach semantics to documents involves first to categorize them properly and then to index them with the relevant semantics for efficient retrieval. This categorization and indexing of the Web documents have become important topic for research. These researches focus on the use of knowledge management to structure documents which involves ontologies to conceptualize knowledge of a specific domain. Then, there is knowledge representation which is a vital part of knowledge management. It provides the possibilities to represent knowledge in order to be inferred. Knowledge representations and reasonings have traditionally been a domain within Artificial Intelligence. However, the recent growth in Semantic Web technologies has added fuel to the use of knowledge explicitly in a Web environment. The XML-based knowledge

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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

Semantic Web technologies use these roles to represent knowledge. The first and the last roles are primarily theoretical roles through which knowledge could be better understood. The remaining roles are conceptual roles which are being implemented within the technology. If those roles are carefully evaluated, it could be seen that knowledge representation begins with ontological commitments. That is selecting a representation means and making a set of ontological commitments (Brachman et al., 1978). Thus defining

The term Ontology is being used for centauries to define an object philosophically. The core theme of the term remains the same in the domain of computer. Within the computer science domain, ontology is 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, 1993) In any case, ontology can be considered as formalization of knowledge representation and Description Logics (DLs)

Description logics (DLs) [(Calvanese et al., 2001); (Baader & Sattler, 2000)] are a family of knowledge representation languages that can be used to represent knowledge of an application domain in a structured and formally well-understood way. The term "Description Logics" can be broken down into the terms description and logic. The former would describe the real world scenario with the real world objects and the relationships between those concepts. More formally these objects are grouped together through unary predicates defined by atomic concepts within description logics and the relationships through binary predicates defined by atomic roles. The term logic adds the fragrance of logical interpretations to the description. Through these logics one could reason the

As the Semantic Web technologies matured, the need of incorporating the concepts behind description logic within the ontology languages was realized. It took few generations for the ontology languages defined within Web environment to implement the description language completely. The Web Ontology Language (OWL) (Bechhofer, et al., 2004); (Patel-Schneider et al., 2004)] is intended to be used when the information contained in documents needs to be processed by applications and not by human (McGuinness & Harmelen, 2004). The OWL language has direct influence from the researches in Description Logics and insights from Description Logics particularly on the formalization of the semantics

The horn logic more commonly known the Horn clauses 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).

in which thinking is accomplished.

ontology is a major activity with the process of the Semantic Web.

provide logical formalization to the ontologies (Baader et al., 2003).

description for generating new knowledge from the existing one.

world.

(Horrocks et al., 2004).

languages could be inferred through different inference mechanisms in order to infer knowledge.
