ontologies
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ontologies [2013/06/26 02:46] pszwed [Open Street Map (OSM) ontology] |
ontologies [2014/05/04 00:41] (current) |
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| for POIs (Points of Interest) and route planning. | for POIs (Points of Interest) and route planning. | ||
| - | See also: [[http://proceedings.fedcsis.org/2012/pliks/61.pdf|PDF]] | + | See also: [[http://proceedings.fedcsis.org/2012/pliks/61.pdf|Szwed, P., Kadłuczka P., Chmiel, W., Głowacz, A., Śliwa, J.: Ontology based integration and decision support in the Insigma Route Planning Subsystem In FedCSIS : proceedings of the Federated Conference on Computer Science and Information Systems 2012 : September 9–12, 2012 Wrocław, Poland. p 141–148]] |
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| + | // Created in 2012// | ||
| ===== Towards software architecture assessment ===== | ===== Towards software architecture assessment ===== | ||
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| *[[http://pszwed.kis.agh.edu.pl/ontologies/soa-atam/QualityAttributes.owl]] | *[[http://pszwed.kis.agh.edu.pl/ontologies/soa-atam/QualityAttributes.owl]] | ||
| * | * | ||
| - | // Created 2012// | + | // Created in 2012// |
| ===== In Polish: GINA guidelines glossary ===== | ===== In Polish: GINA guidelines glossary ===== | ||
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| //Ontologia (słownik) wygenerowana na podstawie polskiej wersji wytycznych dla leczenia astmy (GINA).// | //Ontologia (słownik) wygenerowana na podstawie polskiej wersji wytycznych dla leczenia astmy (GINA).// | ||
| - | The ontology is a result of an experiment (not finished) aiming at unsupervised elicitation of ontology concept candidates from about 100 pages of a narrative medical document. Firstly Polish words were stemmed with Morfologik [[http://morfologik.blogspot.com/]], then they were passed to state machines describing grammar rules. Obtained terms were also clustered according to contexts, in which they appeared. | + | The ontology is a result of an experiment (not finished) aiming at unsupervised elicitation of candidates for ontology concepts from about 100 pages of a narrative medical document. Firstly Polish words were stemmed with Morfologik [[http://morfologik.blogspot.com/]], then they were passed to state machines describing grammar rules. Obtained terms were also clustered according to contexts, in which they appeared. |
| Still waits for refactoring to Hidden Markov Model, which, I hope, would increase the accuracy ... | Still waits for refactoring to Hidden Markov Model, which, I hope, would increase the accuracy ... | ||
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| *[[http://pszwed.kis.agh.edu.pl/ontologies/gina.owl|gina.owl 21MB ]] | *[[http://pszwed.kis.agh.edu.pl/ontologies/gina.owl|gina.owl 21MB ]] | ||
| *[[http://pszwed.kis.agh.edu.pl/ontologies/gina.zip|Zipped ontology]] | *[[http://pszwed.kis.agh.edu.pl/ontologies/gina.zip|Zipped ontology]] | ||
| - | *[[http://pszwed.kis.agh.edu.pl/ontologies/gina.csv|Spreadsheet sorted by terms frequency]] | + | *[[http://pszwed.kis.agh.edu.pl/ontologies/gina.csv|Spreadsheet sorted by terms frequency]]. Terms with higher frequencies are potential candidates for concepts. On the other hand, some terms are just decorators... |
| *[[http://pszwed.kis.agh.edu.pl/ontologies/gina-glossary/index.html|OWL Doc: Browse...]] | *[[http://pszwed.kis.agh.edu.pl/ontologies/gina-glossary/index.html|OWL Doc: Browse...]] | ||
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