Academia.edu no longer supports Internet Explorer.
To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to upgrade your browser.
2005, Third International Conference on Information Technology and Applications (ICITA'05)
Within the emergent Semantic Web framework, the use of traditional web search engines based on keywords provided by the users is not adequate anymore. Instead, new methods based on the semantics of user keywords must be defined to search in the vast Web space without incurring in an undesirable loss of information.
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.
Web Semantics: Science, Services and Agents on the World Wide Web, 2009
Constructing semantic queries is a demanding task for human users, as it requires mastering a query language as well as the schema which has been used for storing the data. In this paper, we describe QUICK, a novel system for helping users to construct semantic queries in a given domain. QUICK combines the convenience of keyword search with the expressivity of semantic queries. Users start with a keyword query and then are guided through a process of incremental refinement steps to specify the query intention. We describe the overall design of QUICK, present the core algorithms to enable efficient query construction, and finally demonstrate the effectiveness of our system through an experimental study.
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.
International journal of Web & Semantic Technology, 2011
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 specialize tools known generically search engine. There are many of search engines available today, retrieving meaningful information is difficult. However to overcome this problem in search engines to retrieve meaningful information intelligently, semantic web technologies are playing a major role. In this paper we present survey on the search engine generations and the role of search engines in intelligent web and semantic search technologies.
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.
International Journal of Computer Applications, 2015
The World Wide Web has grown over the years from simple hypertext documents to highly interactive pages, where users can also contribute to the content by posting comments and so on. However, most data is extremely unstructured and cannot be easily automatically processed by machines. Presently, most search engines are keyword based and searches may also result in irrelevant results due to the mere presence of matching keywords. To eradicate this problem, the concept of semantic web has been introduced in which the data follows a uniform standard. Everything present in the document has a specific meaning attached to it. Such standardized documents can easily be understood by machines. Due to the concept of semantic web, search engines can be made to understand the meaning of the query and thus the most relevant links can be retrieved. To implement semantic web technologies, the concept of ontology is used. In this paper, an attempt is made to explore how semantic web and ontology are being used to implement efficient search engines.
Keyword based Search engines are not able to provide relevant search result because they suffer from the fact that they do not know the meaning of the terms and expression used in the web pages and the relationship between them. This paper compares the semantic search performance of both keyword-based and semantic web based search engines. Initially, two keyword based search engines (Google and Yahoo) and three semantic search engines (Hakia, DuckDuckGo and Bing) are selected to compare their search performance on the basis of precision ratio and how they handle natural language queries. Ten queries, from various topics was run on each search engine, the first twenty documents on each retrieval output was classified as being "relevant" or "non-relevant". Afterwards, precision ratios were calculated for the first 20 document retrieved to evaluate performance of these search engines. Also, comparison of some popular Semantic search engines is provided with their features.
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
The Semantic Web, 2007
Current information retrieval (IR) approaches do not formally capture the explicit meaning of a keyword query but provide a comfortable way for the user to specify information needs on the basis of keywords. Ontology-based approaches allow for sophisticated semantic search but impose a query syntax more diAEcult to handle. In this paper, we present an approach for translating keyword queries to DL conjunctive queries using background knowledge available in ontologies. We present an implementation which shows that this interpretation of keywords can then be used for both exploration of asserted knowledge and for a semantics-based declarative query answering process. We also present an evaluation of our system and a discussion of the limitations of the approach with respect to our underlying assumptions which directly points to issues for future work.
2009
Current keyword-based Web search engines (e.g. Google i ) provide access to thousands of people for billions of indexed Web pages. Although the amount of irrelevant results returned due to polysemy (one word with several meanings) and synonymy (several words with one meaning) linguistic phenomena tends to be reduced (e.g. by narrowing the search using human-directed topic hierarchies as in Yahoo ii ), still the uncontrolled publication of Web pages requires an alternative to the way Web information is authored and retrieved today. This alternative can be the technologies of the new era of the Semantic Web.
Current keyword-based Web search engines (eg Googlei) provide access to thousands of people for billions of indexed Web pages.
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.
2004
Abstract ReQuest is a semantic search system for specialized domains. It aims to offer context based searches by integrating semantic web technology, such as ontologies and resource description files. ReQuest was built to evaluate and compare the relevance of semantic searches and regular searches used in current information retrieval systems with a user survey. Keywords:
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.
2008
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 furthermore permits powerful query and browsing techniques.
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.
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...
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.
Web is the nature of information search. The Semantic Web vision reveals a radical departure from the traditional theories of Information Retrieval (IR) upon which current search engine technology is built. Semantic Web researchers are very articulate about how the pillars of the Semantic Web-semantically aware, intelligent agents, ontologies, and markup languages-will revolutionize the way that we interact with information on the web. They are less articulate about how we will get there from here. While it's true that the traditional assumptions of IR-small, static, homogeneous, centrally located, monolingual document collections-don't hold for the Web, still it is important to note the success of search engines built on IR theory. This paper calls attention to the gap between traditional IR and the more visionary Semantic Web research. We describe a preliminary roadmap bridging the two areas focusing on the concrete contributions and also calling attention to the weak points of both fields.
2005
Search engines have assumed a central role in the World Wide Web's infrastructure as its scale and impact have increased. In the Web's earliest days, people found pages of interest by navigating (quickly dubbed surfing) from pages whose locations they remembered or bookmarked. Rapid growth in the number of pages gave rise to Web directories like Yahoo that manually organized Web pages into a hierarchy of topics.
Loading Preview
Sorry, preview is currently unavailable. You can download the paper by clicking the button above.