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learns topics and keywords from a domain data stream. The conceptualization enriches a UIP, consisting of user interests modeled as terms and term-weight, by providing contextual information of the UIP. For this, the topic hierarchy extracts topic-deterministic keywords and their semantic associations with domain topics
Abstract-the existing distributed ontology evolutionapproaches are not scaled to dynamic environments likeSemantic service architecture (SSOA). As the SSOA-basedsystem grows in size, the complexity of ontology changemanagement increases, especially if the services ontologies areheterogeneous. In this paper, a novel agent-based ontologyevolution framework is developed for services which consumeontologies...
The existing search engines retrieve information only based on the keywords. The incapability to search on the basis of the relation between the keywords and the user concepts, generates noise and hence, results in irrelevant retrieval. This leads to the idea of performing Semantic information processing by mapping
phrases as keywords rather than words. It is context preserving in the sense that it indulges the importance of those keywords that are importuned in nature. Hence it helps a user to gain the actual insight and deduce ideas after examining the relatedness among the keywords. This paper introduces an approach where a concept
A method to automatically annotate video items with semantic metadata is presented. The method has been developed in the context of the Papyrus project to annotate documentary- like broadcast videos with a set of relevant keywords using automatic speech recognition (ASR) transcripts as a primary complementary resource
and standards of educational metadata. However, this solution does not solve completely the problem. Previously, traditional information retrieval systems rely on indexing by keywords for representing pedagogical resources and queries content. This process, based on lexical matching, allows selecting pedagogical
The inability of the present search engines to map the retrieved result set using semantics of the query keywords has been discussed. The present study suggests a framework to improve the mapping of Concept and Context of the query keywords and thereby remove noise from the query. This ensures more relevant and
The information world WWW has more than 3 billion HTML pages and these web pages gain access through search engines only. Search engine is a program that searches the document for specified set of keywords and returns a list of documents where any or all of the specified keywords were found. As more information
Automatic annotating images by equipment is of great interest as it meets one's common need for retrieving image content. Usually image content description with keywords is regarded as a visual-word correlation process. However, in view of the viewer's psychology, image to words is a kind of cognition process, which
The scale of the social web has integrated users in order to organize shared resources. Users freely associate keywords (tags) to resources. This collection of tags creates a folksonomy. Folksonomy is a collaborative tagging system, which has grown popular with its simplicity of free tagging. However, it rises up a
Traditional Information Retrieval (IR) models are based on bag-of-words paradigm, where relevance scores are computed based on exact matching of keywords. Although these models have already achieved good performance, it has been shown that most of dissatisfaction cases in relevance are due to term mismatch between
generalized concepts representation of text (1) overcomes surface level differences (which arise when different keywords are used for related concepts) without drift, (2) leads to a higher-level semantic network representation of related stories, and (3) when used as features, they yield a significant 36% boost in performance
relation extraction based on bootstrapping, but it did not consider the role of the keywords in the semantic relation. This paper presents an improved context pattern, which has a stronger semantic expressiveness, which is used to extract semantic relations and makes the semantic relation extraction more accurate. First of
The number of available Web services, nowadays, is growing rapidly due their potential in many fields. As a result, the discovery process becomes a challenging issue. Traditional syntactic keywords based discovery techniques are inefficient as they fail to recognize similarities between Web services capabilities. Thus
emerged as one successful approach to tackle the problem of information overload. Traditional recommender systems suggest research items using well-known text mining techniques, however they fail when there are no identical keywords to match searches. In order to overcome this and other limitations, several studies have been
The ineffectiveness of information retrieval systems is mostly caused by the inaccurate query formed by a few keywords that reflect actual user information need. One well known technique to overcome this limitation is Automatic Query Expansion (AQE), whereby the user's original query is improved by adding new features
Classical IR systems are often based on lexical matching using approaches that rely on purely statistical methods founded on distributions of keywords to calculate the similarity between the query and the documents of the corpus. The relevance of a document according to a query is based on the similarity of vocabulary
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 LIGVID system is designed for online interactive video shots retrieval and annotation. It uses a user-controlled combination of multiple criteria: keywords, phonetic string, similarity to example images, semantic categories, and relevance feedback strategies: visual and temporal similarity to already identified
Traditionally information retrieval consists mainly of determining which documents of a collection contain the keywords in the user query. However, a growing number of tasks, especially those related to Semantic Web technologies and applications rely on accurately measuring the similarity between documents and online
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