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depend probabilistically both on other properties of that object and on properties of related objects. In this paper an attempt is made to heed keywords extraction. The keywords are not only essential for academic papers but also important for web page retrieval, text mining, and document classification. In this paper, a C
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
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
The problem of automatically extracting the most interesting and relevant keyword phrases in a document has been studied extensively as it is crucial for a number of applications. These applications include contextual advertising, automatic text summarization, and user-centric entity detection systems. All these
and completeness through sense disambiguation and contextual meta-data prepossessing. Our schemes exploits a linguistic ontology identifying query relevant homographs used to construct sense specific keyword sets allowing for enhanced image search and result ranking via the calculation of relatedness between query
approach. To generate the concept, keywords are extracted from the documents but the extracted set is very large. So for dimensionality reduction, SVD is applied. This paper proposes a novel approach for document clustering based on Formal Concept Analysis (FCA). Concept generation and dimensionality reduction are the two
clustering genes is done in two steps: First, keywords corresponding to all genes of interest from a subset of MEDLINE database were extracted automatically using TF-IDF and Z-scores. In the second step, the classic K-means algorithm was used to group genes into clusters of genes based on the keyword features.
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
One of the most serious problems that conventional knowledge management (KM) encompasses has been pointed out tardy and ineffective acquisition of knowledge. To resolve this problem, knowledge must be autonomously acquired according to its context of use by applying the technique of keyword extraction in machine
The aim of service discovery is to discover services based on preferences given by service consumers. Many approaches are using keyword based syntactic methods and recent approaches are using semantic Web technology to enhance service discovery. Traditional service discovery mechanism acts like a black box which
generally have problems on keyword-search problem. In this paper, we proposed an initial model to solve the problem by using Case-Based Reasoning (CBR) and Formal Concept Analysis (FCA). For the proposed model, a case base is created to represent design patterns. FCA is used to be case organization that analyze case base for
XML employs a tree-structured model for representing data, and queries over XML documents are typically represented as twig patterns. At the same time, keyword search over XML documents has been well studied because of its intuitive and friendly query interface. Consequently, XQuery Full-Text emerges as a full-text
Automatic image annotation is the process of assigning keywords to digital images depending on the content information. In one sense, it is a mapping from the visual content information to the semantic context information. In this study, we propose a novel approach for automatic image annotation problem, where the
With the increased demand for English communication, various styles of learning support methods have been proposed and provided to the Japanese learners. However, there are still many learners finding it hard to read, write and speak in English. Regardless of language difference, understanding the other's intention and emotional status accurately and expressing what they think or feel to the others...
Software developers currently find design patterns through search tools for solving software design problem. However, these search tools still have keyword-search problem. In this paper, we introduce the elementary idea to improve the design pattern retrieval tool. We propose the combination of case based reasoning
context information and semantic similarity together. We searched a series of context structures for keywords in a sentence. Experiment has been carried out to show the effectiveness of our method.
A data element specifies one of the characteristics of its parent element. Therefore, the context of a data element is determined by its parent. Non context driven search engines build relationships between data nodes based solely on their labels and proximity to one another while overlooking their contexts. Therefore, they may return faulty answers. This paper investigates the pitfalls and limitations...
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
As personalization technologies are widely used, preference extraction is becoming important. In this work, we propose a preference extraction method on the basis of applications that are installed on a user's smart device. In this method, keywords are extracted from descriptions of the installed applications on an
align two keywords respectively, while KWIC aligns one keyword. This helps to find collocations among words. Furthermore, KWISC is able to expand and collapse bunsetsus. It can shorten distances of collocating words since it shows only the main structure of sentences by collapsing the bunsetsus. Therefore, collocating
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