Metaknowledge Extraction Based on Multi-Modal Documents
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Novel Metaknowledge-based Processing Technique for Multimedia Big Data clustering challenges
Past research has challenged us with the task of showing relational patterns between text-based data and then clustering for predictive analysis using Golay Code technique. We focus on a novel approach to extract metakno…
ClusteringViBERTgrid BiLSTM-CRF: Multimodal Key Information Extraction from Unstructured Financial Documents
Multimodal key information extraction (KIE) models have been studied extensively on semi-structured documents. However, their investigation on unstructured documents is an emerging research topic. The paper presents an a…
Key Information Extractionnamed-entity-recognitionNamed Entity RecognitionDistilling Causal Metaknowledge from Massive Knowledge Graph
In recent years, the growing information overload facilitates the access to billions of relational facts in the world, which are usually integrated in all manner of knowledge graphs. The metaknowledge, defined as the …
Knowledge GraphsLayoutXLM vs. GNN: An Empirical Evaluation of Relation Extraction for Documents
This paper investigates the Relation Extraction task in documents by benchmarking two different neural network models: a multi-modal language model (LayoutXLM) and a Graph Neural Network: Edge Convolution Network (ECN). …
BenchmarkingGraph Neural NetworkLanguage ModelingLanguage Modelling+2Robustness of Structured Data Extraction from In-plane Rotated Documents using Multi-Modal Large Language Models (LLM)
Multi-modal large language models (LLMs) have shown remarkable performance in various natural language processing tasks, including data extraction from documents. However, the accuracy of these models can be significantl…