Semantic Meta Search Engine Using Semantic Similarity Measure

In order to increase web search effectiveness, Meta search engines are invented to combine results of multiple search engines as a result of larger coverage of indexed web. Meta search engine is a kind of system which is useful for internet users to take advantage of multiple search engines in searching information. Recently several approaches were developed using ontology and ranking measures. Accordingly, Meta search engine is developed here using ontology and semantic similarity measure. In order to bring semantic in keyword matching, a semantic similarity measure (SSM) is developed. Here, every concept sets are matched with the title sets using SSM that consider the hyponyms and hyponyms of the keywords presented in the title sets. Along with three different ranking measures relevant to contents, title sets and raking value given by the standard search engines are effectively combined to improve the effectiveness. Finally, the experimentation is carried out using different set of queries and the performance of the meta-search engine is evaluated using TREC-style average precision (TSAP) measure. The proposed semantic meta-search engine provides 80% TSAP which is high compared with existing search engine and meta-search engine.

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.

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.

2013 International Conference on Cloud & Ubiquitous Computing & Emerging Technologies, 2013

Thinking of today's web search scenario which is mainly keyword based, leads to the need of effective and meaningful search provided by Semantic Web. Existing search engines are vulnerable to provide relevant answers to users query due to their dependency on simple data available in web pages. On other hand, semantic search engines provide efficient and relevant results as the semantic web manages information with well defined meaning using ontology. A Meta-Search engine is a search tool that forwards users query to several existing search engines and provides combined results by using their own page ranking algorithm. SemanTelli is a meta-semantic search engine that fetches results from different semantic search engines such as Hakia, DuckDuckGo, SenseBot through intelligent agents. This paper proposes enhancement of SemanTelli with improved snippet analysis based page ranking algorithm and support for image and news search.

INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY

Semantic Web approach with the assistance of ontology is widely used to give more reliable application in retrieving information and knowledge. It is capable to discover the World Wide Web (WWW) that is presented in natural-language text. Based on previous research, incorporating categorization with ontology concept has proven to give better results. However, performing hybrid of the search engine using another technique that is user profiling has a promising potency in enhancing the searching process. Utilizing searching time and giving relevant results are the contributions of this research. The proposed hybrid techniques integrate ontologies, categorization and user profiling concept. In user profiling, similarity measure is adopted in making comparison between two different ontologies. WordNet and UTHM Onto are the independent ontologies used in this process. The preliminary experimental results have given interesting results in terms of data arrangement and time...

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.

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.

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.

International Journal of Computer Applications, 2015

Current World Wide Web also recognized as Web 2.0 is an immense library of interlinked documents that are transferred by computers and presented to people. Search engine is considered the most important tool to discover any information from WWW. Inspite of having lots of development and novel research in current search engines techniques, they are still syntactic in nature and display search results on the basis of keyword matching without understanding the meaning of query, resulting in the production of list of WebPages containing a large number of irrelevant documents as an output. Semantic Web (Web 3.0), the next version of World Wide Web is being developed with the aim to reduce the problem faced in Web 2.0 by representing data in structured form and to discover such data from Semantic Web, Semantic Search Engines (SSE) are being developed in many domains. This paper provides a survey on some of the prevalent SSEs focusing on their architecture; and presents a comparative study on the basis of technique they follow for crawling, reasoning, indexing, ranking etc.

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.