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During the last several years, the volume of user- generated content on the web has skyrocketed. Today, the major Internet players such as MySpace, Wordpress, and YouTube all provide a variety of Web 2.0 based applications that allow users to post photos, share video, and manage blogs with multimedia content. Keyword
matching of service capabilities using a semantic reasoner. The proposed approach has been evaluated for accuracy using recall and precision. The results are compared with keyword based matching. With a typical test data of 90 services, the proposed method has average recall of 88.2% and average precision of 63.7% in contrast
In this paper, we introduce a novel method based on context awareness, semantic similarities and customized weights for different categories to improve keyword matching. The algorithm is able to weight terms by using category information and semantic relationships with WordNet as a lexical database. To demonstrate the
Information retrieval system is taking an important role in current search engine which performs searching operation based on keywords which results in enormous amount of data available to the user, from which user cannot figure out the essential and most important information. This limitation may be overcome by a new
With the rapid growth of web services, web services discovery becomes exceedingly important and challenging. Currently, many discovery approaches have been proposed such as keyword-based or VSM-based syntactic matching and ontology-based semantic matching. Syntactic matching approaches are clearly insufficient due to
A Max-Probability Density based Clustering (MPDC) algorithm is proposed in this paper to resolve the problem of Word Sense Disambiguation in semantic document. MPDC take the context information of a keyword based on WordNet into account and select the max probability sense by measuring the density of the concept. We
when the sentence is analyzed. The goal is to put each noun and verb of the sentence on the right place on the tree. Taking this information into account, it is possible to solve the ambiguity problem for the query keywords and create the indicative summaries taking into account query words, and semantically related
The Internet is the largest information repository. Most information retrieval systems are based on the premise that users know the keywords for searching subjects. Web services provide a suitable technical framework for making business processes accessible within enterprises and across enterprises, so that they have
Nowadays growing number of popularization in the World Wide Web promotes e-learning via web. During e-learning the users can easily share, reuse, and organize the knowledge. Using the search engine the e-learners search the web pages by set of keywords. But the pages which are unrelated for our tags come frequently
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
subsequently used for evaluating the semantic document similarity and document quality. As such, the precision criterion is employed for efficient evaluations by comparing with keywords-based search method. The experimental results reported that the proposed method was able to outperform the TF/IDF method up to 9% on average
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