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Existing methods for Blog keyword extraction usually exploit the context in the specified blog. In this paper, we propose to provide a knowledge context by using small number of nearest neighbor blogs to improve keyword extraction performance. Specifically, knowledge context is build by adding several topic related
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
Textual web pages dominate web search engines nowadays. However, there is also a striking increase of structured data on the web. Efficient keyword query processing on structured data has attracted enough attention, but effective query understanding has yet to be investigated. In this paper, we focus on the problem of
expressed in terms of keywords, over several XML streams. However, there are few algorithms that evaluate this kind of query. One of them is MKStream, which is the current state-of-the-art algorithm for processing keyword-based queries over XML streams. In order to improve scalability, in this paper we introduce PMKStream
This paper presents an objective keyword selection method called visualness with Lesk disambiguation (VLD) for describing educational videos with semantic tags. It extends the work on automatically extracting and associating meaningful keywords carried out in ‘semantic tags for lecture videos’ for
Choosing descriptive keywords to best describe digital media content is crucial for many applications, especially those involving content-based indexing or retrieval. Traditionally such keywords are selected manually, which is labor intensive, restrictive to a limited set of words and inherently subjective to the
A common strategy to assign keywords to documents is to select the most appropriate words from the document text. One of the most important criteria for a word to be selected as keyword is its relevance for the text. The tf.idf score of a term is a widely used relevance measure. While easy to compute and giving quite
As a practical and efficient technique to access XML data, keyword query approaches should address two main problems: enable users to express their query intentions accurately and provide matching algorithms to realize users' query intentions faithfully. Most of the existing researches concentrate on the second
Along with the rapid growth of the xml data quantity on the Internet, the xml data retrieval research has attracted more and more attention. The searching algorithm based on key words is a research hotspot in this field. We present a context-based layered intersection scan algorithm (CLISA), which uses the context semantic of key words to filter large amount of redundant information, different from...
paper, we propose a Bayesian approach to region-based image annotation, which integrates the content-based search and context into a unified framework. The content-based search selects representative keywords by matching an unlabeled image with the labeled ones followed by a weighted keyword ranking, which are in turn used
of XML keyword search. Our solution is based on two novel concepts that we introduce: Target Node Type and Distinguishability. Using these concepts, we develop a low-cost post-processing algorithm on the results of query evaluation to detect the MisMatch problem and generate helpful suggestions to users. Our approach
informative keywords not only from the textual content of the target vlog itself but also from external resources which are semantically and visually relevant to it. Sentiment evaluation obtained from comments. In vlog search we adopt saliency based matching to make the search results. We use different ranking strategies are
Big data are generated from a variety of sources having different representation forms and formats, it raises a research question as how important data relevant to a business context can be captured and analyzed more accurately to represent deep and relevant business insight. There is a number of existing big data analytic methods available in the literature that consider contextual information such...
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
Term ambiguity — the challenge of having multiple potential meanings for a keyword or phrase — can be a major problem for search engines. Contextual information is essential for word sense disambiguation, but search queries are often limited to very few keywords, making the available textual context needed for
In an effort to develop effective multi-media learning objects (MLO), we propose a framework to extract and associate semantic tags to temporally segmented instructional videos. These tags serve for the purpose of efficient indexing and retrieval system. We create these semantic tags from potential keywords extracted
approaches to accounting for negation in sentiment analysis, differing in their methods of determining the scope of influence of a negation keyword. On a set of English movie review sentences, the best approach is to consider two words, following a negation keyword, to be negated by that keyword. This method yields a
We introduce in this paper a system called EDGT, which determines the semantic relationships among Gene Ontology terms. EDGT accepts Keyword-based queries with the form Q (“t1”, “t2”, ‥, “tn”) and Loosely Structured queries with the form
search. In this paper, we propose a framework for semantic based information retrieval. Here we find the concepts that user specify in their query by analyzing the semantic equivalencies. The result which is a set of alternate queries to the main search query is then compared with the existing keyword based system's result
main current approaches for semantic discovery of services are the keyword-based approach and the ontology-based approach. The plain simple keyword matching strategy is time-consuming and has inefficient recall and precision. The ontology-based strategy, on the other hand, is efficient, but may not be practical for the
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