Yan Jia (editor) - MDATA: A New Knowledge Representation Model: Theory, Methods and Applications

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With the fast development of information technologies, many researchers are dedicated to knowledge representation, a high-level understanding and abstraction of massive data. Although many knowledge representation models have been proposed, such as symbolic logic models, semantic networks, expert systems, semantic webs, and knowledge graphs, these models cannot represent procedural knowledge, i.e. dynamic knowledge that changes with temporal and spatial factors.

In this edited book, we introduce a new knowledge representation model called MDATA ( M ulti-dimensional D ata A ssociation and in T elligent A nalysis). After investigating a large number of related works in knowledge representation and cognitive models, we propose the MDATA model on the basis of the widely adopted knowledge graphs. By modifying the representation of relations and properties 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.

Similar as knowledge graphs, there are many key technologies in the MDATA model, such as entity recognition, relation extraction, property extraction, etc. The MDATA model pays particular attention to temporal and spatial characteristics, and the recognition of such temporal and spatial characteristics is important. These characteristics could be time period, location, or even IP address. In order to improve the knowledge graph, knowledge reasoning and completion are necessary, such as entity reasoning and relation completion. In the MDATA model, the dynamic knowledge should also be updated by reasoning, which combines the temporal and spatial factors. As many existing technologies have been proposed in constructing knowledge graphs, we need to extend them to the MDATA model.

The MDATA model can represent knowledge from different dimensions and it can be applied in many critical applications. The outbreak of the coronavirus pandemic has caused serious consequences. By representing personal knowledge such as dynamic travel information by the MDATA model, we can identify close contacts of infected persons in an efficient way. Considering network attacks that might happen every day around the world, the MDATA model can combine the analysis of massive data from log or network traffic to detect attack events. For social network analysis, in which information updates quickly and peoples opinion might change, the MDATA model can represent such dynamic knowledge for better analysis, such as computing individual influence, community detection and sentiment analysis.

To promote the MDATA model, we have brought together research teams from many universities, research institutes, and companies, such as the Harbin Institute of Technology, Shenzhen (China), Guangzhou University (China), National University of Defense Technology (China), Zhejiang University of Technology (China), Hangzhou Dianzi University (China), The University of Queensland (Australia), The University of New South Wales (Australia), etc. We have applied the MDATA model in many practical applications: for example, we have developed a cyberspace security situation awareness system that can detect various network attacks in a timely way. We have also organized many seminars and workshops to exchange the latest research, such as the MDATA workshop in the IEEE International Conference on Data Science in Cyberspace. We hope the MDATA model will be widely adopted in various areas and can promote the study of knowledge representation significantly.