Road-traffic congestion is becoming a serious concern in developing countries and impacts the economy of countries gravely. Increasing congestion on urban roads presents a genuine threat to the economic growth and livability of city regions. Most traffic congestions are caused due to unplanned road networks, high volumes of vehicles and presence of critical congestion areas. Traffic congestions not only pose a threat to the economy but also to the environment. Spillover effect from congested main roads to secondary roads and side streets as alternative routes often leads to more congestion; increasing the chances of collisions and accidents due to tight spacing and constant stopping-and-going. The following paper presents a smart congestion avoidance technique by estimating the scope of real-time traffic congestion on urban road networks and predicts an alternate shortest route to the destination. The proposed system uses K-Means Clustering Algorithm to estimate the magnitude of congestion on different roads and then employs Dijkstra's Algorithm to predict the shortest route. Once the user inputs the destination into the system, the system predicts the shortest route from the user's current location. The process is reiterated at every intersection until user reaches the destination.

In present age of computers, there are various resources for gathering information related to given query like Radio Stations, Television, Internet and many more. Among them, Internet is considered as major factor for obtaining any information about a given domain. When a user wants to find some information, he/she enters a query and results are produced via hyperlinks linked to various documents available on web. But the information that is retrieved to us may or may not be relevant. This irrelevance is caused due to huge collection of documents available on web. Traditional search engines are based on keyword based searching that is unable to transform raw data into knowledgeable representation data. It is a cumbersome task to extract relevant information from large collection of web documents. These shortcomings have led to the concept of Semantic Web (SW) and Ontology into existence. Semantic Web (SW) is a well defined portal that helps in extracting relevant information using many Information Retrieval (IR) techniques. Current Information Retrieval (IR) techniques are not so advanced that they can be able to exploit semantic knowledge within documents and give precise result. The terms, Information Retrieval (IR), Semantic Web (SW) and Ontology are used differently but they are interconnected with each other. Information Retrieval (IR) technology and Web based Indexing contributes to existence of Semantic Web. Use of Ontology also contributes in building new generation of web- Semantic Web. With the help of ontologies, we can make content of web as it will be markup with the help of Semantic Web documents (SWD’s). Ontology is considered as backbone of Software system. It improves understanding between concepts used in Semantic Web (SW). So, there is need to build an ontology that uses well defined methodology and process of developing ontology is called Ontology Development

This paper highlights the explicit description about concept of ontology which is concerned with the development and methodology involved in building ontology. The concept of ontologies has contributed to the development of Semantic Web where Semantic Web is an extension of the current World Wide Web in which information is given in a well-defined meaning that translates the given unstructured data into knowledgeable representation data thus enabling computers and people to work in cooperation. Thus, we can say that Semantic Web is information in machine understandable form. It is also called as Global Information Mesh (GIM). Semantic Web technology can be used to deal with challenges including traditional search engines and retrieval techniques within given organizations or for e-commerce applications whose initial focus is on professional users. Ontology represents information in a manner so that this information can also be used by machines not only for displaying, but also for automating, integrating, and reusing the same information across various applications which may include Artificial Intelligence, Information Retrieval (IR) and many more. Ontology is defined as a collection of set of concepts, their definitions and the relationships among them represented in a hierarchical manner that is termed as Taxonomy. There are various tools available for developing ontologies like Hozo, DOML, and AltovaSemantic Works etc. We have used protégé which is one of the most widely used ontology development editor that defines ontology concepts (classes), properties, taxonomies, various restrictions and class instances. It also supports several ontology representation languages, including OWL. There are various versions of protégé available like WebProtege 2.0 beta, Protégé 3.4.8, Protégé 4.1 etc. In this paper, we have illustrated ontology development using protégé 3.1 by giving an example of Computer Science Department of University System. It may be useful for future researchers in making ontology on protégé version 3.1

We are living in the world of computers. This modern era deals with a wide network of information present on web. A huge number of documents present on web have increased the need for support in exchange of information and knowledge. It is necessary that user should be provided with relevant information about given domain. Traditional Information Extraction techniques like Knowledge Management Solutions were not so advanced that they can lead to extraction of precise information form text documents. It leads to the concept of Semantic Web that depends on creation and integration of Semantic data. The Semantic data in turn depends on building of Ontology. Ontology is considered as backbone of Software system. It improves understanding between concepts used in Semantic Web. So, there is need to build an ontology that uses well defined methodology and process of developing ontology is called Ontology Development.