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As the amount of data increases and the relations among them get more complex, access to information implicit in data appears more difficult, and the role of methods of getting data from diverse texts, and analyzing them becomes more significant. Of such methods is the highly effective technique of keyword extraction
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
keywords query and the common structure information of XML datasets. We can evaluate the generated structured queries over the XML data sources with any existing structure search engine.
An extracting method of research trend in the field of computer network via analysis of the keywords contained in the published paper of related conferences is presented. The recent trends related to the field of IT are extracted by exploiting the Netminer which is one of the software for analysis of social networks
databases are termed as Web Databases (WDB). Web databases have been frequently employed to search the products online for retail industry. They can be private to a retailer/concern or publicly used by a number of retailers. Whenever the user queries these databases using keywords, most of the times the user will be deviated
called the Associated Keyword Space(ASKS) which is effective for noisy data and projected clustering result from a three-dimensional (3D) sphere to a two dimensional(2D) spherical surface for 2D visualization. One main issue, which affects to the performance of ASKS algorithm is creating the affinity matrix. We use semantic
structure. Afterwards, our system recognizes text content and extracts keywords from the slides, which can be used for keyword-based video retrieval and browsing. Experimental results show that our system is able to generate more stable and accurate screen localization results than commonly-used object tracking methods. Our
Clustering Web services into functionally similar clusters is a very efficient approach to service discovery. A principal issue for clustering is computing the semantic similarity between services. Current approaches use similarity-distance measurement methods such as keyword, information-retrieval or ontology based
keyword, ontology and information-retrieval-based methods. Problems with these approaches include a shortage of high quality ontologies and a loss of semantic information. In addition, there has been little fine-grained improvement in existing approaches to service clustering. In this paper, we present a new approach to
The World Wide Web has brought us a vast amount of online information. When we search with a keyword, data feedback from many different websites and the user cannot read all the information. So that, text summarization has become a hot topic, it has attracted experts in data mining and natural language processing
explosion has became the main character of this age. Searching and making use of network information becomes more difficult. Therefore, automatically extraction on keyword is required. This paper uses the idea of classification to complete the task of Key-Phrase extraction, which uses SVM to build classification model and uses
Spatial Co-location patterns are similar to association rules but explore more relying spatial auto-correlation. They represent subsets of Boolean spatial features whose instances are often located in close geographic proximity. Existing co-location patterns mining researches only concern the spatial attributes, and few of them can handle the huge amount of non-spatial attributes in spatial datasets...
Automatic image annotation is the process by which a computer system automatically assigns metadata in the form of captioning or keywords to a digital image. This application of computer vision technique is used in image retrieval system to organize and locate images of interest from a database. Many techniques have
objects of non-interest. Then, with an inter-object attribute recognition technique, the relationships between objects are analyzed in terms of the degree, scope and nature of such relationships. As a result, the analysis of relevance between the information was based on certain keywords and used an inter-object relationship
recurrent networks. Experimental results showed that multi-Layer preception provided better classification results in conjunction with the empirical mode decomposition. It is also concluded that a small set of features is sufficient to classify galaxy images and provide a fast classification. Keywords: Hubble Sequence
system, classification of keywords by higher ranking of topics has contributed to an active role for the extraction of summarization, the results of summarization ratio in social web is 40%-50%.
present the experiment design to capture and extract the viewing patterns in Twitter using the eye-tracking technology. We show a set of experiment results based on the analysis of eye gazing data, in order to demonstrate how the subjects look for specified keywords in the Twitter timeline, which can further contribute to
Feature location is a human-oriented and information-intensive process. When performing feature location tasks with existing tools, developers often feel it difficult to formulate an accurate feature query (e.g., keywords) and determine the relevance of returned results. In this paper, we propose a feature location
A user's location information is commonly used in diverse mobile services, yet providing the actual name or semantic meaning of a place is challenging. Previous works required manual user interventions for place naming, such as searching by additional keywords and/or selecting place in a list. We believe that applying
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