Semantic Annotation Model and Method Based on Internet Open Dataset
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| Publicat a: | International Journal of Intelligent Information Technologies vol. 21, no. 1 (2025), p. 1-20 |
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| Autor principal: | |
| Altres autors: | , , , , , |
| Publicat: |
IGI Global
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| Matèries: | |
| Accés en línia: | Citation/Abstract Full Text - PDF |
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| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3177449582 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1548-3657 | ||
| 022 | |a 1548-3665 | ||
| 024 | 7 | |a 10.4018/IJIIT.370966 |2 doi | |
| 035 | |a 3177449582 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 100 | 1 | |a Gao, Xin |u State Grid Beijing Electric Power Company, China | |
| 245 | 1 | |a Semantic Annotation Model and Method Based on Internet Open Dataset | |
| 260 | |b IGI Global |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Accuracy | ||
| 653 | |a Internet | ||
| 653 | |a Datasets | ||
| 653 | |a Ontology | ||
| 653 | |a Information retrieval | ||
| 653 | |a Data mining | ||
| 653 | |a Context | ||
| 653 | |a Supervised learning | ||
| 653 | |a Organization theory | ||
| 653 | |a Labeling | ||
| 653 | |a Data processing | ||
| 653 | |a Data analysis | ||
| 653 | |a Semantic web | ||
| 653 | |a Annotations | ||
| 653 | |a Information sharing | ||
| 653 | |a Efficiency | ||
| 653 | |a Speech | ||
| 653 | |a Semantics | ||
| 653 | |a Decision making | ||
| 653 | |a Electric power | ||
| 653 | |a Information systems | ||
| 653 | |a Natural language processing | ||
| 653 | |a Methods | ||
| 653 | |a Resource Description Framework-RDF | ||
| 653 | |a Information technology | ||
| 653 | |a Cultural heritage | ||
| 700 | 1 | |a Wang, Yansong |u State Grid Beijing Electric Power Company, China | |
| 700 | 1 | |a Wang, Fang |u State Grid Beijing Electric Power Company, China | |
| 700 | 1 | |a Zhang, Baoqun |u State Grid Beijing Electric Power Company, China | |
| 700 | 1 | |a Hu, Caie |u State Grid Beijing Electric Power Company, China | |
| 700 | 1 | |a Wang, Jian |u State Grid Beijing Electric Power Company, China | |
| 700 | 1 | |a Ma, Longfei |u State Grid Beijing Electric Power Company, China | |
| 773 | 0 | |t International Journal of Intelligent Information Technologies |g vol. 21, no. 1 (2025), p. 1-20 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3177449582/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3177449582/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |