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Semantic Web technology is used extensively in the health domain, due to its ability to specify expressive, domain-specific data, as well as its capacity to facilitate data integration between heterogeneous, health-related sources. In the health domain, mobile devices are an essential part of patient self-management approaches, where local clinical decision support is applied to ensure that patients...
Is widely accepted that food supply and quality are major problems in the 21st century. Due to the growth of the world's population, there is a pressing need to improve the productivity of agricultural crops, which hinges on different factors such as geographical location, soil type, weather condition and particular attributes of the crops to plant. In many regions of the world, information about...
Sentence similarity methods are used to assess the degree of likelihood between phrases. Many natural language applications such as text summarization, information retrieval, text categorization, and machine translation employ measures of sentence similarity. The existing approaches for this problem represent sentences as vectors of bag of words or the syntactic information of the words in the phrase...
Many data sets on the web contain analogous data which represent the same resources in the world, so it is helpful to interlink different data sets for sharing information. However, finding correct links is very challenging because there are many instances to compare. In this paper, an interlinking method is proposed to interlink instances across different data sets. The input is class correspondences,...
Due to the increased amount of available biodiversity data, many biodiversity research institutions are now making their databases openly available on the web. Researchers in the field use this databases to extract new knowledge and also share their own discoveries. However, when these researchers need to find relevant information in the data, they still rely on the traditional search approach, based...
In this paper we address the problem of predicting SPARQL query performance. We use machine learning techniques to learn SPARQL query performance from previously executed queries. Traditional approaches for estimating SPARQL query cost are based on statistics about the underlying data. However, in many use-cases involving querying Linked Data, statistics about the underlying data are often missing...
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