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Natural Language Generation (NLG) takes structured data in a knowledge base as input and produces natural language text, tailored to the pre-sentational context and the target reader (Reiter and Dale, 2000). NLG techniques use and build models of the context, and the user and use them to select appropriate presentation strategies, for example to deliver short summaries to the user s WAP phone or a longer multimodal text to the user s desktop PC. In the context of the semantic web and knowledge management, NLG is required to provide automated documentation of ontologies and knowledge bases. Unlike human-written texts, an automatic approach will constantly keep the text up-to-date which is vitally important in the semantic web context where knowledge is dynamic and is updated frequently. The NLG approach also allows generation in multiple languages without the need for human or automatic translation (see (Aguado et al., 1998)). Generation of natural language text from ontologies is an important problem. Firstly, because textual documentation is more readable than the corresponding formal notations and thus helps users who are not knowledge engineers to understand and use ontologies. Secondly, a number of applications have now started using ontologies for knowledge representation, but this formal knowledge needs to be expressed in natural language in order to produce reports, letters etc. In other words, NLG can be used to present structured information in a userfriendly way. asp.net code 128 reader C# Code 128 Reader SDK to read, scan Code 128 in C#.NET class ...
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NET Application. Use KA.Barcode Reader for .NET to Scan and Read Linear & 2D Barcode Images in .NET. Completely integrated into Visual Studio .NET, ASP. learning scenario 5 in Section 2.5). Collecting data relevant for the existing ontology can also be used in some other phases of the semiautomatic ontology construction process, such as ontology evaluation or ontology re nement (phases 5 and 6, Section 2.4), for instance, via associating new instances to the existing ontology in a process called ontology grounding (Jakulin and Mladenic, 2005). In the case of topic ontologies (see 7), where the concepts correspond to topics and documents are linked to these topics through an appropriate relation such as hasSubject (Grobelnik and Mladenic 2005a), one can use the Web to collect documents on a prede ned topic. In Knowledge Discovery, the approaches dealing with collecting documents based on the Web data are referred in the literature under the name Focused Crawling (Chakrabarti, 2002; Novak, 2004b). The main idea of these approaches is to use the initial seed information given by the user to nd similar documents by exploiting (1) background knowledge (ontologies, existing document taxonomies, etc.), (2) web topology (following hyperlinks from the relevant pages), and (3) document repositories (through search engines). The general assumption for most of the focused crawling methods is that pages with more closely related content are more inter-connected. In the cases where this assumption is not true (or we cannot reasonably assume it), we can still use the methods for selecting the documents through search engine querying (Ghani et al., 2005). In general, we could say that focused crawling serves as a generic technique for collecting data to be used in the next stages of data processing, such as constructing (ontology learning scenario 4 in Section 2.5) and populating ontologies (ontology learning scenario 3 in Section 2.5). 1 (1 t,1) message = new byte[textLen]; for(int i = 0 ; i < textLen ; i++) {message[i] = (byte)text[i];} myStream.Write(message); asp.net code 128 reader .NET Barcode Reader Software | Code 128 Scanning DLL Library ...
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