Aiping Li – författare

Knowledge representation is an important task in understanding how humans think and learn. Although many representation models or cognitive models have been proposed, such as expert systems or knowledge graphs, they cannot represent procedural knowledge, i.e., dynamic knowledge, in an efficient way.This book introduces a new knowledge representation model called MDATA (Multi-dimensional Data Association and inTelligent Analysis). By modifying the representation of entities and relations in knowledge graphs, dynamic knowledge can be efficiently described with temporal and spatial characteristics. The MDATA model can be regarded as a high-level temporal and spatial knowledge graph model, which has strong capabilities for knowledge representation. This book introduces some key technologies in the MDATA model, such as entity recognition, relation extraction, entity alignment, and knowledge reasoning with spatiotemporal factors. The MDATA model can be applied in many critical applications and this book introduces some typical examples, such as network attack detection, social network analysis, and epidemic assessment.The MDATA model should be of interest to readers from many research fields, such as database, cyberspace security, and social network, as the need for the knowledge representation arises naturally in many practical scenarios.

Knowledge representation is an important task in understanding how humans think and learn. Although many representation models or cognitive models have been proposed, such as expert systems or knowledge graphs, they cannot represent procedural knowledge, i.e., dynamic knowledge, in an efficient way.

This book introduces a new knowledge representation model called MDATA (Multi-dimensional Data Association and inTelligent Analysis). By modifying the representation of entities and relations in knowledge graphs, dynamic knowledge can be efficiently described with temporal and spatial characteristics. The MDATA model can be regarded as a high-level temporal and spatial knowledge graph model, which has strong capabilities for knowledge representation. This book introduces some key technologies in the MDATA model, such as entity recognition, relation extraction, entity alignment, and knowledge reasoning with spatiotemporal factors. The MDATA model can be applied in many critical applications and this book introduces some typical examples, such as network attack detection, social network analysis, and epidemic assessment.

The MDATA model should be of interest to readers from many research fields, such as database, cyberspace security, and social network, as the need for the knowledge representation arises naturally in many practical scenarios.

This book presents the theoretical foundations of the MDATA cognitive model and its applications in the field of cybersecurity. The MDATA model is an innovative analytical tool designed to simulate and improve cognitive processes. It bridges cognitive science and cybersecurity, making it essential for professionals and researchers in these fields.The core content explores three critical technologies within the MDATA model: knowledge representation, knowledge acquisition, and knowledge application. Each section provides in-depth technical analysis and practical applications, enabling readers to grasp the structural and operational principles of the model. With clear implementation strategies, the book equips readers to apply the MDATA model in real-world scenarios.Through detailed case studies, the book demonstrates how the MDATA model enhances the identification and resolution of cybersecurity threats. Applications include network attack analysis, open-source intelligence, public sentiment monitoring, and cybersecurity assessments. Readers will gain a powerful tool for navigating complex cybersecurity incidents, making this book an indispensable resource for cybersecurity professionals, AI researchers, and data analysts. A foundational understanding of cybersecurity and cognitive science is recommended.