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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of the American Society for Information Science and Technology Jg. 58; H. 1; S. 133 - 149
Hauptverfasser: Cui, Hong, Heidorn, P. Bryan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Hoboken Wiley Subscription Services, Inc., A Wiley Company 01.01.2007
Wiley
Wiley Periodicals Inc
Schlagworte:
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
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
Bibliographie: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