Metaknowledge Extraction Based on Multi-Modal Documents

The triplet-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. Th...

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Autores principales:	Shu-Kan Liu, Rui-Lin Xu, Bo-Ying Geng, Qiao Sun, Li Duan, Yi-Ming Liu
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Metaknowledge multi-modal document layout analysis knowledge graph Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/a8ccb9c6d91348e882481d887a4bde28
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id	oai:doaj.org-article:a8ccb9c6d91348e882481d887a4bde28
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spelling	oai:doaj.org-article:a8ccb9c6d91348e882481d887a4bde282021-11-19T00:05:53ZMetaknowledge Extraction Based on Multi-Modal Documents2169-353610.1109/ACCESS.2021.3068728https://doaj.org/article/a8ccb9c6d91348e882481d887a4bde282021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9386086/https://doaj.org/toc/2169-3536The triplet-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.Shu-Kan LiuRui-Lin XuBo-Ying GengQiao SunLi DuanYi-Ming LiuIEEEarticleMetaknowledgemulti-modaldocument layout analysisknowledge graphElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 50050-50060 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Metaknowledge multi-modal document layout analysis knowledge graph Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Metaknowledge multi-modal document layout analysis knowledge graph Electrical engineering. Electronics. Nuclear engineering TK1-9971 Shu-Kan Liu Rui-Lin Xu Bo-Ying Geng Qiao Sun Li Duan Yi-Ming Liu Metaknowledge Extraction Based on Multi-Modal Documents
description	The triplet-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.
format	article
author	Shu-Kan Liu Rui-Lin Xu Bo-Ying Geng Qiao Sun Li Duan Yi-Ming Liu
author_facet	Shu-Kan Liu Rui-Lin Xu Bo-Ying Geng Qiao Sun Li Duan Yi-Ming Liu
author_sort	Shu-Kan Liu
title	Metaknowledge Extraction Based on Multi-Modal Documents
title_short	Metaknowledge Extraction Based on Multi-Modal Documents
title_full	Metaknowledge Extraction Based on Multi-Modal Documents
title_fullStr	Metaknowledge Extraction Based on Multi-Modal Documents
title_full_unstemmed	Metaknowledge Extraction Based on Multi-Modal Documents
title_sort	metaknowledge extraction based on multi-modal documents
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/a8ccb9c6d91348e882481d887a4bde28
work_keys_str_mv	AT shukanliu metaknowledgeextractionbasedonmultimodaldocuments AT ruilinxu metaknowledgeextractionbasedonmultimodaldocuments AT boyinggeng metaknowledgeextractionbasedonmultimodaldocuments AT qiaosun metaknowledgeextractionbasedonmultimodaldocuments AT liduan metaknowledgeextractionbasedonmultimodaldocuments AT yimingliu metaknowledgeextractionbasedonmultimodaldocuments
_version_	1718420687053389824

Metaknowledge Extraction Based on Multi-Modal Documents

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