Efficient Proposed Framework for Semantic Search Engine using New Semantic Ranking Algorithm (original) (raw)
Related papers
New Framework for Semantic Search Engine
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.
Search Engine Based on Semantic Web
2019
With the rapid development of the World Wide Web, one of the main tools for people to get network information is search engine. However, the search results are widely condemned due to the lack of accuracy and redundancy disadvantages. The semantic web is a technology to save data in a machine-readable format that makes it possible for the machines to intelligently match that data with related data based on its semantics. This paper starts from the traditional search engine, and firstly introduces its classification, popular technology, advantages, disadvantages, and deep Knowledge on semantic-technology, thus leads to the semantic search engine model.
Novel Semantic Approach Based Ranking Algorithm Framework For Advancing Search Engines
2022
Now a days, many of the internet users require efficient search engines in order to get the facility of faster web page searching and processes of information retrieval. But the conventional web search engines facing primary challenges of retrieving accurate outcomes for given particular query taking lowest time for response. Conventional search engines also face the challenges of expanding conflicting queries depending on the semantic link of each keyword. Therefore, in this paper, we proposed a novel model for web page ranking using semantic web page retrieval approach for the classification of significant results of queries which are not clear by making use of semantic relations. Experiments are conducted, by evaluating the proposed model with different four scenarios of inputs developed. The results are compared with existing web page ranking mechanisms including the real time search engines. The comparisons of results demonstrated that, the proposed model is in better place.
2016
A Web Crawler is an internet bot that downloads data from World Wide Web for search engine and Indexing. Web information is constantly changing and is updated without prior notice. The Web Crawler checks the World Wide Web for the updated information. People visiting the website frequently are actually the familiar websites and this makes it high ranked website. In this paper the network traffic solution is used to get desired information. This paper will be actualize Ontology Based Topic Specific Search Using Semantic Web. The strategy for web crawling with filters is utilized. It is a query based approach with Jena API. The proposed approach takes care of the issue of re-visiting web pages by crawler. The Semantic Web is an extended version of the present Web that permits the meaning of data to be decisively described regarding all around characterized vocabularies that are comprehended by individuals and computers. As Topic based pursuit is an inquiry interface paradigm taking in...
An Enhanced Indexing and Ranking Technique on The Semantic Web
2011
With the fast growth of the Internet, more and more information is available on the Web. The Semantic Web has many features which cannot be handled by using the traditional search engines. It extracts metadata for each discovered Web documents in RDF or OWL formats, and computes relations between documents. We proposed a hybrid indexing and ranking technique for the Semantic Web which finds relevant documents and computes the similarity among a set of documents. First, it returns with the most related document from the repository of Semantic Web Documents (SWDs) by using a modified version of the ObjectRank technique. Then, it creates a sub-graph for the most related SWDs. Finally, It returns the hubs and authorities of these document by using the HITS algorithm. Our technique increases the quality of the results and decreases the execution time of processing the user's query.
Investigation and Analysis of New Approach of Intelligent Semantic Web Search Engines
2012
AS WE KNOW THAT WWW IS ALLOWING PEOPLES TO SHARE THE HUGE INFORMATION GLOBALLY FROM THE BIG DATABASE REPOSITORIES. THE AMOUNT OF INFORMATION GROWS BILLIONS OF DATABASES. HENCE TO SEARCH PARTICULAR INFORMATION FROM THESE HUGE DATABASES WE NEED THE SPECIALIZED MECHANISM WHICH HELPS TO RETRIEVE THAT INFORMATION EFFICIENTLY. NOW DAYS VARIOUS TYPES OF SEARCH ENGINES ARE AVAILABLE WHICH MAKES INFORMATION RETRIEVING IS DIFFICULT. BUT TO PROVIDE THE BETTER SOLUTION TO THIS PROBLEM, SEMANTIC WEB SEARCH ENGINES ARE PLAYING VITAL ROLE. BASICALLY MAIN AIM OF THIS KIND OF SEARCH ENGINES IS TO PROVIDING THE REQUIRED INFORMATION IS SMALL TIME WITH MAXIMUM ACCURACY. BUT THE PROBLEM WITH SEMANTIC SEARCH ENGINES IS THAT THOSE ARE VULNERABLE WHILE ANSWERING THE INTELLIGENT QUERIES. THESE KINDS OF SEARCH ENGINES DON’T HAVE MUCH EFFICIENCY AS PER EXPECTATIONS BY END USERS, AS MOST OF TIME THEY ARE PROVIDING THE INACCURATE INFORMATION’S. THUS IN THIS PAPER WE ARE PRESENTING THE NEW APPROACH FOR SEMANTIC ...
Enhanced Search Engine Using Proposed Framework and Ranking Algorithm Based on Semantic Relations
IEEE Access, 2019
Today, most users need search engines to facilitate search and information retrieval processes. Unfortunately, traditional search engines have a significant challenge that they should retrieve high-precision results for a specific unclear query at a minimum response time. Also, a traditional search engine cannot expand a small, ambiguous query based on the meaning of each keyword and their semantic relationship. Therefore, this paper proposes a comprehensive search engine framework that combines the benefits of both a keyword-based and a semantic ontology-based search engine. The main contributions of this work are developing an algorithm for ranking results based on fuzzy membership value and a mathematical model of exploring a semantic relationship between different keywords. In the conducting experiments, eight different test cases were implemented to evaluate the proposed system. Executed test cases have achieved a precision rate of 97% with appropriate response time compared to the relevant systems.
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.
A Survey on Semantic Web Search Engine
2012
The tremendous growth in the volume of data and with the terrific growth of number of web pages, traditional search engines now a days are not appropriate and not suitable anymore. Search engine is the most important tool to discover any information in World Wide Web. Semantic Search Engine is born of traditional search engine to overcome the above problem. The Semantic Web is an extension of the current web in which information is given well-defined meaning. Semantic web technologies are playing a crucial role in enhancing traditional web search, as it is working to create machine readable data. but it will not replace traditional search engine. In this paper we made a brief survey on various promising features of some of the best semantic search engines developed so far and we have discussed the various approaches to semantic search. We have summarized the techniques, advantages of some important semantic web search engines that are developed so far.The most prominent part is that how the semantic search engines differ from the traditional searches and their results are shown by giving a sample query as input.