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Article

Knowledge Acquisition from Critical Annotations

1
Department of Measurement and Information Systems, Budapest University of Technology and Economics, H-1117, Budapest, Hungary
2
Institute for Literary Studies, Hungarian Academy of Sciences, H-1118, Budapest, Hungary
*
Author to whom correspondence should be addressed.
Information 2018, 9(7), 179; https://doi.org/10.3390/info9070179
Received: 30 June 2018 / Revised: 16 July 2018 / Accepted: 17 July 2018 / Published: 20 July 2018
(This article belongs to the Special Issue AI for Digital Humanities)
Critical annotations are important knowledge sources when researching one’s oeuvre. They describe literary, historical, cultural, linguistic and other kinds of information written in natural languages. Acquiring knowledge from these notes is a complex task due to the limited natural language understanding capability of computerized tools. The aim of the research was to extract knowledge from existing annotations, and to develop new authoring methods to facilitate the knowledge acquisition. After structural and semantic analysis of critical annotations, authors developed a software tool that transforms existing annotations into a structured form that encodes referral and factual knowledge. Authors also propose a new method for authoring annotations based on controlled natural languages. This method ensures that annotations are semantically processable by computer programs and the authoring process remains simple for non-technical users. View Full-Text
Keywords: critical annotations; knowledge acquisition; controlled natural languages critical annotations; knowledge acquisition; controlled natural languages
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MDPI and ACS Style

Mészáros, T.; Kiss, M. Knowledge Acquisition from Critical Annotations. Information 2018, 9, 179. https://doi.org/10.3390/info9070179

AMA Style

Mészáros T, Kiss M. Knowledge Acquisition from Critical Annotations. Information. 2018; 9(7):179. https://doi.org/10.3390/info9070179

Chicago/Turabian Style

Mészáros, Tamás; Kiss, Margit. 2018. "Knowledge Acquisition from Critical Annotations" Information 9, no. 7: 179. https://doi.org/10.3390/info9070179

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