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keyword driven crawling with relevancy decision mechanism and uses Ontology concepts which ensures the best path for improving crawler's performance. This paper introduces extraction of URLs based on keyword or search criteria. It extracts URLs for web pages which contains searched keyword in their content and considers such
Internets are important in everyone's life like searching keyword, college, social network and online shopping, when user using the internet for searching the keyword they getting some problem. That is when user searching for the keyword for some meaning but they will get different meaning for that keyword. Because
Due to the explosive growth of the amount of Web information, the effectiveness of keyword-based searching methods appears to reach a limit. One major reason is that the mixture of content and presentation information hinders machines in understanding the context of Web information and as a result, the performance of
analysis and results ranking. For semantic annotation, we use domain ontology and two bilingual dictionaries to extract keywords for annotation. For query analysis, we present a method which combines lexical relationship and semantic relationship to analyse user's query. And for results ranking, we propose a modulative method
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
Many e-commerce web sites such as online book retailers or specialized information hubs such as online movie databases make use of recommendation systems where users are directed to items of interests based on past user interactions. While keyword based approaches are naive and do not take content or context into
thesauruses, categories, ontologies, and folksonomies. A statistical semantic association model is proposed to integrate different semantic models, represent heterogeneous semantic information, and support semantic relevance computation. A focused crawling framework is developed which adopts both keyword based contents and
Current classification techniques use word matching and clustering techniques to classify webpages. These techniques use ad hoc approach of checking and matching the entire keywords in a webpage for classification. These methods are efficient but not without problems. In general, they suffer from the following
Most of the search engines search for keywords to answer the queries from users. The search engines usually search web pages for the required information. However they filter the pages from searching unnecessary pages by using advanced algorithms. These search engines can answer topic wise queries efficiently and
published on the Web. However general information search engine do not support these kind of query very well. The main reasons are general information search engine treat all the query as keywords and keywords cannot completely express user's need. So in order to support spatial information query in Public health Emergence
keywords, which lack the semantic data. In this paper the semantic based information retrieval methodology is proposed to get data from the web archives in a specific domain by gathering the domain relevant information with web crawler. By utilizing ontology and semantic information matched with a given user's query is used
In this paper we discuss the fundamental problem of information retrieval on the Web. Information on the Web is not semantically categorized and stored. This research focuses on applying semantic capabilities using ontology on search engine. By using ontology, search engine can search keywords that are conceptually
domain special ontology, grammatical knowledge of text could be acquired easily, and the latter is more determinate than the former. In the area of Information Retrieval, it is not enough to search information only based on keywords. Under this situation should we consider some web application can employ grammatical
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