Semantic Web Search: Perspectives and Key Technologies

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.

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.

2006

Web search engines like Google have made people “smarter” by providing ready access to the world's knowledge whenever they need to look up a fact, learn about a topic or evaluate opinions. The W3C's Semantic Web effort aims to make such information more accessible to computer programs by encoding it on the Web in machine understandable form.

2000

Current web search engines return links to documents for user-specified keywords queries. Users have to then manually trawl through lists of links and glean the required information from documents. In contrast, semantic search engines allow more expressive queries over information integrated from multiple sources, and return specific information about entities, for example people, locations, news items. An entity-centric data model

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.

International Journal of Engineering Research and Technology (IJERT), 2015

https://www.ijert.org/semantic-web-search-engine https://www.ijert.org/research/semantic-web-search-engine-IJERTV4IS040908.pdf 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.

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.

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.

2005

Since its emergence in the early 1990s, the World Wide Web has rapidly evolved into a global information space of incomparable size. Keyword-based search engines such as GoogleTM index as many webpages as possible for the benefit of human users. Sophisticated as such search engines have become, they are still often unable to bridge the gap between HTML and the human. Tim Berners-Lee envisions the Semantic Web as the web of machineinterpretable information that complements the existing World Wide Web, providing an automated means for machines to truly traverse the Web on behalf of their human counterparts. A cornerstone application of the emerging Semantic Web is the search engine that is capable of tying components of the Semantic Web together into a traversable landscape. This paper describes both an architecture for and a prototype of a Semantic Web Search Engine (SWSE) using Jena that provides more sophisticated searching with more exacting results. To compare keyword-based searc...

The World Wide Web ("WWW", or simply "Web") is an information space that allows us to share information from global data repositories. To find out user specific data the web uses specialized tools known as web search engines. These search engines are a remotely accessible program that does keyword searches for information on the Internet. As there is tremendous growth in the volume of data or the information it is difficult to get syntactically relevant documents with in less time using conventional search engines. It can be possible with semantic web by providing sufficient context about resources on the web and building the semantic search engines that use the context so that machines can find out the meaningful documents. In this paper we present study on the general search engines and semantic search engines and have done a survey on how the keyword based search engine work for a user query practically and how semantic search engines provides results differently depending upon their specific performance

Advances in Science, Technology and Engineering Systems Journal

Day by day, the data on the web becomes very huge which makes it difficult to find relevant information. Search engines are one of the successful factors that can retrieve information from the Web. The process of seeking information by search engines helps users find information on the internet, however it is not an easy task to find the exact information from this massive data available on the Web. Semantic Web technology has an ability to focus on metadata rather than syntax, which made the semantic search engines to search for the meaning of keywords instead of the keyword syntax. Consequently, an effective role of performance in conventional search engines can be achieved by rising the accuracy of information returned by a search query. In this paper, a survey for syntactic-based search engines and semantic-based search engines are studied, a comprehensive comparison between the two is presented, finally, their technologies are compared and discussed.

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.

International Journal of Information Technology and Computer Science, 2016

Semantic search engines (SSE) are more efficient than other web engines because in this era of busy life everyone wants an exact answer to his question which only semantic engines can provide. The immense increase in the volume of data, trad itional search engines has increased the number of answers to satisfy the user. This creates the problem to search for the desired answer. To solve this problem, the t rend of developing semantic search engines is increasing day by day. Semantic search engines work to extract the best answer of user queries which exactly fits with it. Trad itional search engines are keyword based which means that they do not know the mean ing of the words which we type in our queries. Due to this reason, the semantic search engines super pass the conventional search engines because they give us mean ingful and well-defined informat ion. In this paper, we will discuss the background of Semantic searching, about semantic search engines; the technology used for the semantic search engines and some of the existing semantic search engines on various factors are compared.

2008

Many experts predict that the next huge step forward in Web information technology will be achieved by adding semantics to Web data, and will possibly consist of (some form of) the Semantic Web. In this paper, we present an approach to Semantic Web search, which combines standard Web search with ontological background knowledge. In fact, we show how standard Web search engines can be used as the main inference motor for ontology-based search. To make this possible, lightweight software clients are used for annotation and query decomposition. We develop the formal model behind this approach and also provide an implementation in desktop search. Experiments show that the implementation scales quite well to very large amounts of data.

Digital Ecosystems and …, 2008

In this paper, we make a survey over the primary literature regarding semantic search technologies. By classifying the literature into six main categories, we review their characteristics respectively. In addition, the issues within the reviewed semantic search methods and engines are analysed and concluded based on four perspectives.