Metaknowledge Extraction Based on Multi-Modal Documents

TL;DR

This paper introduces a framework for extracting and organizing metaknowledge from multi-modal documents to improve the structural understanding of knowledge bases, demonstrating its effectiveness through experiments.

Contribution

It presents a novel Metaknowledge Extraction Framework and Document Structure Tree model for extracting structural knowledge from complex documents.

Findings

Effective extraction of metaknowledge elements demonstrated

Detailed examples illustrate the concept and generation of metaknowledge

Analysis of task flow and knowledge-metaknowledge associations provided

Abstract

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.