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Linked data continues to grow at a rapid rate, but a limitation of a lot of the data that is being published is the lack of a semantic description. There are tools, such as D2R, that allow a user to quickly convert a database into RDF, but these tools do not provide a way to easily map the data into an existing ontology. This paper presents a semiautomatic(More)
The work on integrating sources and services in the Semantic Web assumes that the data is either already represented in RDF or OWL or is available through a Semantic Web Service. In practice, there is a tremendous amount of data on the Web that is not available through the Semantic Web. In this paper we present an approach to automatically discover and(More)
The Linked Data cloud contains large amounts of RDF data generated from databases. Much of this RDF data, generated using tools such as D2R, is expressed in terms of vocabularies automatically derived from the schema of the original database. The generated RDF would be significantly more useful if it were expressed in terms of commonly used vocabularies.(More)
Automatically assigning semantic class labels such as WindSpeed, Flight Number and Address to data obtained from structured sources including databases or web pages is an important problem in data integration since it enables the researchers to identify the contents of these sources. Automatic semantic annotation is difficult because of the variety of(More)
The Linked Open Data continues to grow rapidly, but a limitation of much of the data that is being published is the lack of a semantic description. While there are tools that help users to quickly convert a database into RDF, they do not provide a way to easily map the data into an existing ontology. This paper presents an approach that allows users to(More)
Automatic semantic annotation of structured data enables unsupervised integration of data from heterogeneous sources but is difficult to perform accurately due to the presence of many numeric fields and proper-noun fields that do not allow reference-based approaches and the absence of natural language text that prevents the use of language-based approaches.(More)
This paper presents an automatic approach to mining collections of maps from the Web. Our method harvests images from the Web and then classifies them as maps or non-maps by comparing them to previously classified map and non-map images using methods from Content-Based Image Retrieval (CBIR). Our approach outperforms the accuracy of the previous approach by(More)
Maps can be a great source of information for a given geographic region, but they can be difficult to find and even harder to process. A significant problem is that many interesting and useful maps are only available in raster format, and even worse many maps have been poorly scanned and they are often compressed with lossy compression algorithms.(More)
Maps are one of the most valuable documents for gathering geospatial information about a region. Yet, finding a collection of diverse, high-quality maps is a significant challenge because there is a dearth of content-specific metadata available to identify them from among other images on the Web. For this reason, it is desirous to analyze the content of(More)
This paper qualitatively, compares MRAC and Modified MRAC scheme when the adaptation laws are designed using different approaches. The controller parameter adaptation laws are designed based on the MIT rule and Lyapunov theory. The effect of augmenting the system with PID has also been studied. The augmented system provided better responses.