Imaxe de Portada

Semantic Annotation Model and Method Based on Internet Open Dataset

Gardado en:
Publicado en:International Journal of Intelligent Information Technologies vol. 21, no. 1 (2025), p. 1-20
Autor Principal: Gao, Xin
Outros autores: Wang, Yansong, Wang, Fang, Zhang, Baoqun, Hu, Caie, Wang, Jian, Ma, Longfei
Publicado:
IGI Global
Materias:
Accuracy
Internet
Datasets
Ontology
Information retrieval
Data mining
Context
Supervised learning
Organization theory
Labeling
Data processing
Data analysis
Semantic web
Annotations
Information sharing
Efficiency
Speech
Semantics
Decision making
Electric power
Information systems
Natural language processing
Methods
Resource Description Framework-RDF
Information technology
Cultural heritage
Acceso en liña:Citation/Abstract
Full Text - PDF
Etiquetas: Engadir etiqueta
Sen Etiquetas, Sexa o primeiro en etiquetar este rexistro!
Resumo:Traditional semantic annotation faces the problem of dataset diversity. Different fields and scenarios need to be specially annotated, and annotation work usually requires a lot of manpower and time investment. To meet these challenges, this paper deeply studies the semantic annotation model and method based on internet open datasets, aiming to improve annotation efficiency and accuracy and promote data resource sharing and utilization. This paper selects Common Crawl dataset to provide sufficient training samples; methods such as removing stop words and deduplication are used to preprocess data to improve data quality; a keyword extraction model based on heuristic rules and text context is constructed. In terms of semantic annotation model, this paper constructs a model based on Bidirectional Long Short-Term Memory (BiLSTM), which can make full use of the part-of-speech information of the corpus context, capture the part-of-speech features of the corpus, and generate semantic tags through supervised learning.
ISSN:1548-3657
1548-3665
DOI:10.4018/IJIIT.370966
Fonte:Engineering Database