research-article

Automatic Ontology-Based Knowledge Extraction from Web Documents

Authors:

Nigel R. ShadboltAuthors Info & Claims

IEEE Intelligent Systems, Volume 18, Issue 1

Pages 14 - 21

https://doi.org/10.1109/MIS.2003.1179189

Published: 01 January 2003 Publication History

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Abstract

The Artequakt project seeks to automatically extract knowledge about artists from the Web, populate a knowledge base, and use it to generate personalized narrative biographies. The system architecture has three key components-knowledge extraction, information management, and biography construction. An example of the generation of a short biography of Rembrandt illustrates Artequakt's functionality.

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Alec C(2023)Ontology Population from French Classified AdsGraph-Based Representation and Reasoning10.1007/978-3-031-40960-8_13(155-170)Online publication date: 11-Sep-2023
https://dl.acm.org/doi/10.1007/978-3-031-40960-8_13
Ahamed BYuvaraj DShitharth SMirza OAlsobhi AYafoz A(2022)An Efficient Mechanism for Deep Web Data Extraction Based on Tree-Structured Web Pattern MatchingWireless Communications & Mobile Computing10.1155/2022/63352012022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/6335201
Hu YChapman AWen GHall D(2022)What Can Knowledge Bring to Machine Learning?—A Survey of Low-shot Learning for Structured DataACM Transactions on Intelligent Systems and Technology10.1145/351003013:3(1-45)Online publication date: 3-Mar-2022
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Index Terms

Automatic Ontology-Based Knowledge Extraction from Web Documents
1. Applied computing
  1. Computers in other domains
    1. Digital libraries and archives
2. Information systems

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Published In

IEEE Intelligent Systems Volume 18, Issue 1

January 2003

94 pages

ISSN:1541-1672

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IEEE Educational Activities Department

United States

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Published: 01 January 2003

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Alec C(2023)Ontology Population from French Classified AdsGraph-Based Representation and Reasoning10.1007/978-3-031-40960-8_13(155-170)Online publication date: 11-Sep-2023
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Ahamed BYuvaraj DShitharth SMirza OAlsobhi AYafoz A(2022)An Efficient Mechanism for Deep Web Data Extraction Based on Tree-Structured Web Pattern MatchingWireless Communications & Mobile Computing10.1155/2022/63352012022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/6335201
Hu YChapman AWen GHall D(2022)What Can Knowledge Bring to Machine Learning?—A Survey of Low-shot Learning for Structured DataACM Transactions on Intelligent Systems and Technology10.1145/351003013:3(1-45)Online publication date: 3-Mar-2022
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Martinez-Rodriguez JHogan ALopez-Arevalo I(2020)Information extraction meets the Semantic WebSemantic Web10.3233/SW-18033311:2(255-335)Online publication date: 1-Jan-2020
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Meškelė DFrasincar FHung CPapadopoulos G(2019)ALDONAProceedings of the 34th ACM/SIGAPP Symposium on Applied Computing10.1145/3297280.3297525(2489-2496)Online publication date: 8-Apr-2019
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Aeiad EMeziane F(2019)An adaptable and personalised E-learning system applied to computer science Programmes designEducation and Information Technologies10.1007/s10639-018-9836-x24:2(1485-1509)Online publication date: 1-Mar-2019
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Adriani MBrambilla MDi Giovanni M(2019)Extraction of Relations Between Entities from Human-Generated Content on Social NetworksCurrent Trends in Web Engineering10.1007/978-3-030-51253-8_7(48-60)Online publication date: 11-Jun-2019
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References

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Incremental Ontology Population and Enrichment through Semantic-based Text Mining: An Application for IT Audit Domain

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