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Metaknowledge Extraction Based on Multi-Modal Documents

arXiv 2102.02971 ↗First posted 5 Feb 2021cs.CVcited by 0

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Abstract (arXiv)

The triple-based knowledge in large-scale knowledge bases is most likely lacking in structural logic and problematic of conducting knowledge hierarchy. In this paper, we introduce the concept of metaknowledge to knowledge engineering research for the purpose of structural knowledge construction. Therefore, the Metaknowledge Extraction Framework and Document Structure Tree model are presented to extract and organize metaknowledge elements (titles, authors, abstracts, sections, paragraphs, etc.), so that it is feasible to extract the structural knowledge from multi-modal documents. Experiment results have proved the effectiveness of metaknowledge elements extraction by our framework. Meanwhile, detailed examples are given to demonstrate what exactly metaknowledge is and how to generate it. At the end of this paper, we propose and analyze the task flow of metaknowledge applications and the associations between knowledge and metaknowledge.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 26 of its 50 references that are in the map. First posted on arXiv 5 Feb 2021.

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14 more
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