Study of "Semantic Web" For Finding Relevant Information on Web

International Journal of Engineering Research and, 2015

Search engines are design for to search particular information for a large database that is from World Wide Web. There are lots of search engines available. Google, yahoo, Bing are the search engines which are most widely used search engines in today. The main objective of any search engines is to provide particular or required information with minimum time. The semantics web search engines are the next version of traditional search engines. The main problem of traditional search engines is that information retrieval from the database is difficult or takes long time. Hence efficiency of search engines is reduced. To overcome this intelligent semantic search engines are introduced. The main target of semantic search engines is to give the required information within small time with high accuracy. Many search engines will provide result from blogs or various websites. The user can not have a trust on the results because the information on blogs or websites is does not necessarily true. For this purpose we use xml meta-tags and its features .The xml page will contain built in and user defined tags. The metadata info of the pages expected from this XML into resource description framework (RDF).

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2019

Search engines play important role in the success of the Web. Search engine helps the users to find the relevant information on the internet. Due to many problems in traditional search engines has led to the development of semantic web. Semantic web technologies are playing a crucial role in enhancing traditional search, as it work to create machines readable data and focus on metadata. However, it will not replace traditional search engines. In the environment of semantic web, search engine should be more useful and efficient for searching the relevant web information. It is a way to increase the accuracy of information retrieval system. This is possible because semantic web uses software agents; these agents collect the information, perform relevant transactions and interact with physical devices. This paper includes the survey on the prevalent Semantic Search Engines based on their advantages, working and disadvantages and presents a comparative study based on techniques, type of results, crawling, and indexing.

The Semantic Web is an extension of the current web in which information is given well-defined meaning.The perspective of Semantic Web is to promote the quality and intelligence of the current web by changing its contents into machine understandable form. Therefore, semantic level information is one of the cornerstones of the Semantic Web. The World Wide Web (WWW) allows the people to share the information (data) from the large database repositories globally. The amount of information grows billions of databases. We need to search the information will specialized tools known as generically available search engines. There are many of search engines available today, retrieving meaningfull information is difficult. However to overcome this problem in search engines to retrieve meaningful information intelligently based upon the user requirements semantic web technologies are playing a crucial role. semantic web is working to create machine readable data. Semantic search has the power to enhance traditional web search, but it will not replace it. Semantic web information is described using RDF(S), OWL, XML In this paper we present survey on the search engine generations and the role of search engines in intelligent web and semantic search engine technologies and paper we present the role of semantic web search engines and describes different technologies in semantic search engines and also deals with analysis and comparison of various semantic web search engines based on various parameter to find out their advantages and limitations. Based on the analysis of different search engines, a comparative study is done to identify relative strengths in semantic web search engines.

2015

The World Wide Web (WWW) allows people to share information or data from the large database repositories globally. We need to search the information with specialized tools known generically as search engines. There are many search engines available today, where retrieving meaningful information is difficult. However to overcome this problem of retrieving meaningful information intelligently in common search engines, semantic web technologies are playing a major role. In this paper we present a different implementation of semantic search engine and the role of semantic relatedness to provide relevant results. The concept of Semantic Relatedness is connected with Wordnet which is a lexical database of words. We also made use of TF-IDF algorithm to calculate word frequency in each and every webpage and Keyword Extraction in order to extract only useful keywords from a huge set of words. These algorithms are used to retrieve much optimized and useful results to the user.

The word wide web is a rapidly going and changing information source. Its growth and change rate make the task of finding relevant information harder. With the dynamic nature of WWW, for a given query the set of relevant web pages web pages is also dynamic, it leads to problem of scalability the assumption of accurate sufficient static image of the web is reduced with its change. Most of the search engines failed to user satisfaction for relevant, complete and updated information. On the part of search desirable to generate the searching technique to get the improvement in the regency and coverage of search engines. In this paper architecture of a search engine is proposed which may lead the user relevant web pages. This architecture uses ontology, semantic based web so as to help user to draw relevant information through search engines.

The Search engines plays an important role in the success of the Web, Search engines helps any Internet user to rapidly find relevant information. But the unsolved problems of current search engines have led to the development of the Semantic Web. In the environment of Semantic Web, the search engines are more useful and efficient in searching the relevant web information., and our work shows how the fundamental elements of the meta search engine can be used in retriving the information resources in a more efficient way. Meta search engines that utilizes the power of a traditional search engine and enriches the search result using the knowledge base to produce better results, In this paper we made a brief survey on the concept of meta search engines, and focused on the architecture and their key technologies involve,. finally summarized various semantic meta search engines developed so far such as SavvySearch, Metacrawler,hot bot, harvest42,dogpile e.t.c, in their own strategy.

The promise of semantic technology presents a better search scenario than traditional/keyword based search engines for applications and knowledge discovery. As the society is transforming into knowledge society, the power of information is increasingly becoming crucial for decision making process and business solutions. Thus, semantic technology is applied to enhance the visibility of content created in different form and publishing them, data coupling through semantic links, forming communities, provenance of concepts and validation, disambiguation and authentication of terms with semantic etc. Feature based analysis has been done with examples on two different semantic web search engines.

https://www.ijrrjournal.com/IJRR_Vol.6_Issue.10_Oct2019/Abstract_IJRR0012.html, 2019

Semantic Search is a search technique that improves searching precision by understanding the purpose of the search and the contextual significance of words as they appear in the searchable data space, whether on the web to generate more relevant result. We highlight here about Semantic Search, Semantic Web and discuss about different type of Semantic search engine and differences between keyword base search and Semantic Search and the advantage of Semantic Search. We also give a brief overview of the history of semantic search and its feature scope in the world.

2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation, 2014

The semantic web is a technology to save data in a machine-readable format that facilitates machines to intelligently match that data with related data based on meanings. Whilst this approach is being adopted and implemented by some large organisations there is a need for an effective semantic search engine to maximise the full potential of that semantic web. A major difficulty is that the search experience is dependent on a number of elements including a user-friendly interface, a strong query language processor, a result optimiser, result ranking and the use of appropriate data structures to store data. Apart from the technical aspects related to implementation, a strategy to prioritise these elements is needed to optimize and enhance the search experience over the semantic web. The purpose of this work is to investigate some relevant issues on querying the semantic web in a context of semantic search engines, and propose a framework that facilitates an effective search over the semantic web.

Nowadays the volume of the information on the Web is increasing dramatically. Facilitating users to get useful information has become more and more important to information retrieval systems. While information retrieval technologies have been improved to some extent, users are not satisfied with the low precision and recall. With the emergence of the Semantic Web, this situation can be remarkably improved if machines could “understand” the content of web pages. The existing information retrieval technologies can be classified mainly into three classes.The traditional information retrieval technologies mostly based on the occurrence of words in documents. It is only limited to string matching. However, these technologies are of no use when a search is based on the meaning of words, rather than onwards themselves.Search engines limited to string matching and link analysis. The most widely used algorithms are the PageRank algorithm and the HITS algorithm. The PageRank algorithm is based on the number of other pages pointing to the Web page and the value of the pages pointing to it. Search engines like Google combine information retrieval techniques with PageRank. In contrast to the PageRank algorithm, the HITS algorithm employs a query dependent ranking technique. In addition to this, the HITS algorithm produces the authority and the hub score. The widespread availability of machine understandable information on the Semantic Web offers which some opportunities to improve traditional search. If machines could “understand” the content of web pages, searches with high precision and recall would be possible.