The reusability of induced knowledge for the automatic semantic markup of taxonomic descriptions

To automatically convert legacy data of taxonomic descriptions into extensible markup language (XML) format, the authors designed a machine‐learning‐based approach. In this project three corpora of taxonomic descriptions were selected to prove the hypothesis that domain knowledge and conventions aut...

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Vydané v:	Journal of the American Society for Information Science and Technology Ročník 58; číslo 1; s. 133 - 149
Hlavní autori:	Cui, Hong, Heidorn, P. Bryan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Hoboken Wiley Subscription Services, Inc., A Wiley Company 01.01.2007 Wiley Wiley Periodicals Inc
Predmet:	Automatic classification Classification Content analysis Conventions Descriptions Document markup languages Exact sciences and technology Experiments Extensible Markup Language Formats. Codification. Conversion Formats. Markup languages. Codification. Conversion Indexing. Classification. Abstracting Indexing. Classification. Abstracting. Syntheses Induced Information and communication sciences Information and document structure and analysis Information processing and retrieval Information science. Documentation Knowledge Knowledge management Machine learning N-Gram language models Performance evaluation Sciences and techniques of general use Semantic analysis Semantics Semiotics Studies Taxonomy Text segmentation Training Treatment methods XML Performance evaluation Description language Semantic analysis Taxonomy Syntactic analysis XML language Knowledge base Algorithm Knowledge Knowledge extraction Automatic processing Flora Document structure Machine learning Markup
ISSN:	1532-2882, 2330-1635, 1532-2890, 2330-1643
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Popis
Shrnutí:	To automatically convert legacy data of taxonomic descriptions into extensible markup language (XML) format, the authors designed a machine‐learning‐based approach. In this project three corpora of taxonomic descriptions were selected to prove the hypothesis that domain knowledge and conventions automatically induced from some semistructured corpora (i.e., base corpora) are useful to improve the markup performance of other less‐structured, quite different corpora (i.e., evaluation corpora). The “structuredness” of the three corpora was carefully measured. Basing on the structuredness measures, two of the corpora were used as the base corpora and one as the evaluation corpus. Three series of experiments were carried out with the MARTT (markuper of taxonomic treatments) system the authors developed to evaluate the effectiveness of different methods of using the n‐gram semantic class association rules, the element relative position probabilities, and a combination of the two types of knowledge mined from the automatically marked‐up base corpora. The experimental results showed that the induced knowledge from the base corpora was more reliable than that learned from the training examples alone, and that the n‐gram semantic class association rules were effective in improving the markup performance, especially on the elements with sparse training examples. The authors also identify a number of challenges for any automatic markup system using taxonomic descriptions.
Bibliografia:	ArticleID:ASI20463 istex:64331E448755005562643DD5AE33DC21C32145E0 ark:/67375/WNG-XJDDPT7P-X ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Article-2 ObjectType-Feature-1 content type line 23
ISSN:	1532-2882 2330-1635 1532-2890 2330-1643
DOI:	10.1002/asi.20463