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In this paper, we propose a novel image search scheme is contextual image search with keyword input. It is different from conventional image search schemes. it consist of three step process, first one is context extraction to distinguish the image entities of the same name, second step is conceptualization to convert
technique called WebPagePrev through context and content keywords. Underlying techniques for context and content memories’ acquisition, storage, decay, and utilization for page re-finding are discussed. A relevance feedback mechanism is also involved to tailor to individual's memory strength and
In the current diversity and complexity of the network information environment, the technology of web page sensitive keywords detection is an important and immediate way to manage public opinion online. We propose a system for web page sensitive keywords detection. This system can detect sensitive keywords in the web
solution helps in reducing the time to write documents by 42% as compared to the traditional methods of writing documents. Sophisticated statistical algorithms along with natural language processing technology are used to continuously determine the keywords and concepts from the content in the document. A web search is
Keyword based search scheme imposes the problem of representing a lot of web pages in the search engines. Query expansion with relevant words increases the performance of search engines, but finding and using the relevant words is an open problem. In this research we describe a new model for query expansion which
relevant matched results to be presented to the user. The quality of the matched result depends on the information stored in the index. The more efficient is the structure of index, more efficient the performance of search engine. Generally, inverted index are based solely on the frequency of keywords present in number of
(MWE) and they do not scale very well. This paper proposes a clustering and classification algorithm for semantic similarity using sample web pages. Further improvement is to analyze the short text for classification and labeling the short text according to the keyword and producing the result for the end user. This type
(MWE) and they do not scale very well. This paper proposes a clustering and classification algorithm for semantic similarity using sample web pages. Further improvement is to analyze the short text for classification and labeling the short text according to the keyword and producing the result for the end user. This type
The World Wide Web contains vast amount of interlinked web documents. Retrieving information from such a huge collection is easy using various search engines, but retrieving relevant information is still a challenging task. Since the traditional search engines are based upon keyword matching, therefore semantics of
page next to the keyword that motivated the user to launch an ancillary search. In order to demonstrate the feasibility of our approach we have developed a tool that embeds an egocentric information visualization technique in the Web page. This tool supports nested queries and allows the display of multiple data
visualize the lattice structure of web pages and keywords as line diagram. This system is implemented on the computer (CPU=2.83GHz! $MM=2GB), by using Python, which is an object-oriented programming language, Application Program Interface (API), and one of the GUI libraries, Tkinter. Through the subjective evaluation and sign
keywords, the number of communities, the average clustering coefficient, and the average similarities of web pages. These five impact factors contain statistic and content information of an event. Empirical experiments on real datasets including Google Zeitgeist and Google Trends show that that the number of web pages and the
searching. The distribution of tag types differs greatly across different systems. Also the distribution shows large difference between publishers and searchers. In order to expand tags of resources for publishers and keywords for searchers reasonable, this paper shows a comparison of the distributions of both kinds of users
The wide diffusion of community tagging sites and related folksonomies has made the knowledge discovery and retrieval still much more urgent topic. If tagging systems allow users to add freely keywords to web resources, clicking on a tag has the side effect of a tag-based query, since enables the users to explore
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.