| content relevance engineering research |
| Content Relevancy Quantification Model |
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Semantic coverage approach to sigma deviations for search queries is suggested. Presented are the estimations of minimum needed content semantic coverage to assure the achievement of sigma content relevance for a given example content. These estimations are based on the evaluation results yielded by the Content Relevancy Quantification Model (CRQM), approaching the relevance problem in content engineering. This model integrates qualitative subject matter data and quantitative content relevance metrics, providing continuous relevance evaluations throughout content engineering process, thus, making possible to trace content relevance requirements from customer to product.
The CRQM improves latent semantic indexing, especially for unknown and (or) heterogenous collections, by increasing relevance, precision, and recall of content search, including the full text search. The CRQM can be used for data exploration and data integration tasks (due to its potential to quantify the contents semantics), to solve heterogeneity problems, and to provide varied levels of Querying services, that facilitates knowledge discovery at different levels of granularity. The CRQM provides new research and development points in semantic quantification (Sequantic) content engineering , content relevance modeling, query language grammar (e.g., XQuery/Sequantic, XML/Sequantic), or web languages (e.g., RDF, DAML+OIL, OWL) improvement. The CRQM fits the Quantitative Paradigm of Software Reliability as Content Relevance (see http://arxiv.org/ftp/arxiv/papers/0807/0807.0070.pdf) Copyright by Yuri Arkhipkin, 2006 - 2008 |
