Data extraction and annotation based on domain-specific ontology evolution for deep web | Zendy

Kerui Chen | Zendy; Wanli Zuo | Zendy; Fengling He | Zendy; Yongheng Chen | Zendy; Ying Wang | Zendy

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Data extraction and annotation based on domain-specific ontology evolution for deep web

Author(s) -

Kerui Chen,

Wanli Zuo,

Fengling He,

Yongheng Chen,

Ying Wang

Publication year - 2011

Publication title -

computer science and information systems

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.244

H-Index - 24

eISSN - 2406-1018

pISSN - 1820-0214

DOI - 10.2298/csis101011023k

Subject(s) - computer science , information retrieval , ontology , annotation , data extraction , ontology learning , ontology based data integration , precision and recall , domain (mathematical analysis) , semantic web , data mining , artificial intelligence , suggested upper merged ontology , mathematical analysis , philosophy , mathematics , medline , epistemology , political science , law

Deep web respond to a user query result records encoded in HTML files. Data extraction and data annotation, which are important for many applications, extracts and annotates the record from the HTML pages. We proposed an domain-specific ontology based data extraction and annotation technique; we first construct mini-ontology for specific domain according to information of query interface and query result pages; then, use constructed mini-ontology for identifying data areas and mapping data annotations in data extraction; in order to adapt to new sample set, mini-ontology will evolve dynamically based on data extraction and data annotation. Experimental results demonstrate that this method has higher precision and recall in data extraction and data annotation.

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