This document is also available in these non-normative formats: PDF version. along with information written in RDF, and OWL 2 ontologies . Each OWL 2 ontology represented as an instance of this conceptual structure can . such statements are captured using the functional-style syntax, which is. ontology language to a known logical formalism, and by using automated reasoners that RDF and RDFS allow the representation of some ontological knowledge. The .. information to indicate earlier versions of the current ontology . OWL is an ontology language designed for the Semantic. Web. – It provides a rich collection of operators for forming The Web was made possible through established standards to represent the extra semantic information needed for.
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However, the Web Ontology Working Group of W3C  identified a number of . ontology language to a known logical formalism, and by using automated .. and predicate logic (Antoniou and Van Harmelen ) to represent information in. Reasoning on the Semantic Web: OWL, reasoning tools. Page 6. Representing knowledge. There are a As XML, using the industry-standard structured mark- up language. • As graphs RDF describes the semantics of information in a. OWL 2 ontologies can be used along with information written in RDF, can be used to represent first simple information and then more complex The W3C OWL 2 Web Ontology Language (OWL) is a Semantic Web language . syntaxes are shown throughout the document by using the buttons below.
Suitable procedures for visualization, and different structures, information systems are becoming navigation, knowledge discovery, integration with online increasingly complex. The Semantic Web aims to find, share databases, multilingual context sensitive user interface, and refine the information of current web more easily. The reasoning, querying, decision support and the system Semantic Web is a solution for representing the data in a machine understandable manner. Ontology is a powerful management tools are also incorporated. For the sake of technology for representing the data in semantic web. This paper proposes a unified properties?
It can read, create and save knowledge treasure will instantiate its own domain specific ontologies in different ontologies such as RDF, RDFs and database and work upon it.
Swoop is easier to extend for ontology developers and also useful and accessible by them. Various tools, methodologies and techniques [6, 7] exist for TopBraid Composer is a free edition and professional tool for ontology development.
Knowledge models of these tools map developing ontologies. It domain specific ontology into a knowledge treasure of the has its own language for storing the ontologies. Apollo uses system which automatically learns concepts and instances by Java for implementation. It has all primitives for creating ontology learning. The consistency is also checked knowledge-bases while database store all instances.
For at the time of editing. Reasoner ensures the quality, integrated framework for representing  real world consistency and certainty of the ontologies. There are various knowledge, maintaining knowledge treasures, performing reasoners [6, 7, 9] available which have some asserted axioms or reasoning [11, 13] on it and learning thereof.
Pellet knowledge treasure is treated as distributed directory, which is an OWL-DL based reasoner which supports expressive consists of ontology, knowledge base and rules , databases description logics.
It is one of the reasoner that supports all and procedures and is application independent. It was the first OWL reasoner.
Horrocks and currently efforts are going to tune it as an intelligent was presented a reasoner known as FaCT Fast Classification of framework for knowledge representation in the Semantic Web. It can be used as a description logic classifier and for modal logic satisfiability testing.
Hermit reasoner was E. Knowledge Representation developed to check the consistency of the ontologies.
It was first It is envisioned in that the representational unit of an publicly-available reasoner. EHCPR is parallel to the unit of ontology . The complete D. Languages ontology i. The complete structure of Ontology languages  are used from the beginning of s EHCPR is explained in table 1 and the structure of instance is for the evolution of knowledge representation languages. Basically knowledge representation formalisms were based on first order-logic e.
G Generality. From all of them, of the ancestor to the concept A. All the above-mentioned features make the EHCPRs system more Operator Description close to human intelligence, more realistic, all natural and hence Head Name of the instance.
It started from the instance. Need of EHCPR Framework minimal, necessary and sufficient possible constructs and contain all symbolic representation at once. EHCPR maintains the familiar to store the information in tabular format.
RDBMS clarity by defining the knowledge base and the database represents the information in multiple tables which are related separately. It may be depicted as a complete system for possible with each other on a common key and facilitates for futuristic intelligent agents in Artificial Intelligence.
The aspect information retrieval by using SQL queries. Tables are formed of context sensitive response of the system makes it unique and on the basis of normalization which has domains, keys, multi- near to human experts.
But RDBMS has some the reasoning and representation by using defaults and limitations such as semantic overloading, poor support for constraints for minimizing redundancy and inconsistency. To reduce the problems of all together and demerits of none as a single scheme. All the symbolic representation of any Hence semantic data models were introduced in mid- concept is only once during its first specification.
The result of the Semantic Web  depends subclasses. The Unless operator stores any exceptions to the rule heavily on quick and cheap construction of web ontologies. It and is helpful in blocking the decision if its value is found to be is required to convert the web document model into a web of true.
Ontologies make efforts in this direction.
In non- languages  and the development of various OWL aware monotonic logic, later information may invalidate previous ontology tools. Variable to store the knowledge. Ontologies are able to infer the implicit In this paper, a new knowledge representation scheme: an information which is not possible in databases.
Although Extended Hierarchical Censored Production Rule EHCPR OWL is most accepted solution to construct ontology and do has been introduced to develop a generalized knowledge reasoning, but can be improved by adding some more representation, reasoning and learning system which exhibits attributes to the structure of OWL.
In EHCPRs Framework, knowledge database into a single file which reduces the is structured in its most general form with minimal level of performance during reasoning.
If a query is put up for redundancy and inconsistency. This will provide a scalable APIs. Figure 1. The example depicted in this section creates OWL information.
This incomplete information limits the ontology vehicle. OWL representation of vehicle. The characteristic features are allowed not to hold for an instance of the particular concept.
Representing Vehicle ontology in Protege Editor and reason the ontology smoothly. OWL is incapable of representing non-monotonic reasoning. They are well suited ontologies due to enhanced Step2: Traverse the input file for getting various classes.
Relations between classes are categorized in two types such as if temp is the root hierarchical and non-hierarchical. In hierarchical relation, knowledgebase. As a sample public static String root; vehicle.
As we click on browse button an open dialog box will appear which has a list of files figure 2c. All entities are stored in the EHCPRs knowledge base with their data properties and object properties. All data and object properties as well as instances of all entities are stored in the 2.
The contributions of this paper are two-fold. At the outset, it Figure 2. In addition, it also provides an As we select the vehicle. Our approach has been knowledge base with their appropriate location. As a future direction an upper ontology should be created using EHCPR as the structure of each concept.
This upper ontology should be extended to various domain ontologies as per the requirement of the application. The domain specific database can then be instantiated and be used for any purpose at application level. McDermott D. McGuinness and F. Harmelen Eds. Michalski and P. Mishra, S. Engineering, Ghaziabad, , pp. Malik, N. Jain and S. Auer, C. Bizer, G. This means that when you build a system using a data modeling approach: You can only enter data that you know to be valid.
There are no other data. The data model entity classes and their derived tables are templates.
With an ontology database: You can enter what you know to be true. You can enter incomplete information. You and the computer can infer other things. Ontology classes are simply sets of things. This is a profoundly different view of the world, as we shall see, below. If all validation of data is in a program, the program acts as a filter, the way we discussed before.
If, on the other hand, data are stored with the semantics visible to a wide range of processors, then the data are more powerful, and the opportunities for discovering new things in them is greater. Michael Daconta and his colleagues describe four stages in the smart data continuum: Text and databases pre-XML —Most data are proprietary to an application. XML documents in a single domain-Here data achieve application independence within a domain.
For example XML could describe standard semantics within the health care industry, the insurance industry, and so forth. Taxonomies and documents with mixed vocabularies-In this stage data can be collected from multiple domains and accurately classified.
This classification can then be used for discovery of data. Simple relationships between categories in the taxonomy can be used to relate and thus combine data. Data are now smart enough to be easily discovered and sensibly combined with other data. Ontologies and rules-in this stage, new data can e inferred from existing data by following logical rules. Data are not smart enough to be described with concrete relationships and sophisticated formalisms. In this stage data no longer exist as a blob but as a part of a sophisticated microcosm.
But we can address the others. Specifically, RDF and OWL represent structured languages for representing ontologies that we can map back to what we are used to doing with data models. Uniform Resource stuff In order to talk about something, it is necessary to name it. The semantic web provides a scheme for naming things in two layers. First of all, the general concept of a Uniform Resource Identifier URI is simply a formatted identifier that identifies anything.
The name is in two parts: A scheme name, and A scheme-specific name. There is no outside control over URIs, so they can be whatever you want them to be, such as: hay:david Note, of course, that within the context of a particular ontology, all URIs must be unique. The scheme name and the first elements of the scheme-specific name are regulated to insure uniqueness across the World-wide Web.
As you will see, in this context XML is the language that is used to describe an ontology. Since the description of an XML namespace can be lengthy, a prefix is usually assigned to each, in order to simplify referring to a term.
These are similar to the tags in HTML, but where HTML tags describe formatting components of a document, these tags describe the semantic content of it. For example, As you can see, the tag describes the text that follows.