Knowledge and Information Extraction Research

Dear Colleagues,

The field of "Knowledge and Information Extraction Research" is characterized by its focus on advancing methods and techniques for extracting structured knowledge from diverse data sources. While this area has explored various aspects of knowledge extraction, there is a continued need to address complex challenges and enhance the practical applications of these methods.

This Special Issue aims to contribute to the evolution of knowledge and information extraction by fostering innovative research. It invites researchers to explore novel methodologies and tools that can improve the precision and efficiency of knowledge extraction processes. The scope includes interdisciplinary collaboration, drawing on expertise from fields like natural language processing, machine learning, data mining, and information retrieval.

The main purposes of this Special Issue are to advance collective knowledge in this field, introduce methodological innovations, and emphasize the practical relevance of knowledge extraction. Research within this issue is not limited to academic exploration but also seeks to bridge the gap between theory and real-world problem-solving in industries such as healthcare, finance, e-commerce, and more.

Topics of interest include, but are not limited to, the following:

• Advanced knowledge extraction techniques;
• Interdisciplinary approaches;
• Practical applications in diverse domains;
• Methodological innovations;
• Bridging the gap between academia and industry;
• Collaborative efforts to enhance knowledge extraction.

The aim of this Special Issue is to contribute to the existing literature by consolidating knowledge, introducing new methodologies, and promoting the application of information extraction techniques to real-world challenges. It provides a platform for researchers to share their insights and advancements, shaping the future of knowledge and information extraction.

We invite contributions that expand the boundaries of knowledge extraction and encourage researchers to explore the potential of these techniques in practical contexts. Your submissions will play a crucial role in advancing this field and addressing the ever-evolving demands for structured knowledge from a wide range of data sources.

Dr. Lanting Fang
Dr. Yubo Song
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• data mining
• information retrieval
• machine learning
• information extraction
• techniques
• knowledge graphs