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This system proposes Indian-logic ontology based Context-aware Query Refinement model to support context-sensitive semantic search in keyword based search engine. This is by formulating effective query using Indian logic based Ontology for Context identification to overcome ambiguous query terms and increase the
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
The study of traditional text filtering with keywords retrieval ignores the semantic relations between keywords, and it results in the bottleneck of the further development of text filtering. To solve this problem, three steps are adopted. Firstly, concept lattice theory should be introduced into traditional text
Semantic search promises to provide more accurate result than present-day keyword matching-based search by using the knowledge represented logically (i.e., knowledge base). But, the ordinary users don't know well the complex formal query language and schema of the knowledge base. So, the system should interpret the
propose to make out the best from the vital knowledge present over the internet intelligently, anywhere in the world through better searching methodologies. The current information retrieval system uses the keyword matching technique for fetching results from the web-repository. In this paper, we propose a structural
Social bookmarking tools are rapidly emerging on the Web as it can be witnessed by the overwhelming number of participants. In such spaces, users annotate resources by means of any keyword or tag that they find relevant, giving raise to lightweight conceptual structures aka folksonomies. In this respect, needless to
Currently, Web of Things is based on keyword matching which is not beneficial to the development regarding Web of Things. Accordingly, "Semantic Web of Things" is proposed. As far as Semantic Web of Things concerned, the information of things should be represented as ontology-based semantic annotation
Most of the current focused crawling approaches perform syntactic matching, that is, they retrieve documents that contain particular keywords from the user's query. This often leads to poor discovery results, because the keywords in the query can be semantically similar but syntactically different, or vice-versa
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
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
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
General purpose search engines provide users with lists of retrieved documents in response to their queries. The common structure of list elements includes the title of a document, its URL, and small snippet from the text. Snippets are evidence of occurrences of query's keywords in the document. The length of each
index texts. Traditional BOW matrix is replaced by ldquoBag of Conceptsrdquo (BOC). For this purpose, we developed fully automated methods for mapping keywords to their corresponding ontology concepts. Support vector machine a successful machine learning technique is used for classification. Experimental results shows that
ignores important semantic relationships between key terms. In this paper, we proposed a system that uses ontologies and Natural Language Processing techniques to index texts. Traditional BOW matrix is replaced by "Bag of Concepts" (BOC). For this purpose, we developed fully automated methods for mapping keywords to their
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