Titlebook: MDATA: A New Knowledge Representation Model; Theory, Methods and Yan Jia,Zhaoquan Gu,Aiping Li Book 2021 Springer Nature Switzerland AG 20

潰爛

Kai Xing,Aiping Li,Rong Jiang,Yan Jialearning, identity and diversity by presenting actual research findings that were retrieved from classroom settings and related social practices. Learning is to a large extent an ongoing social process as both students and their teachers learn by being part of shared social practices through social

Ordeal

cognizant

怪物

Liqun Gao,Bin Zhou,Yan Jia,Hongkui Tu,Ye Wanglearning, identity and diversity by presenting actual research findings that were retrieved from classroom settings and related social practices. Learning is to a large extent an ongoing social process as both students and their teachers learn by being part of shared social practices through social

發(fā)怨言

NOTCH

Introduction to the MDATA Model,ata .ssociation and in.elligent .nalysis (MDATA for short). We introduce three main parts in the MDATA model, knowledge representation, knowledge acquisition, and knowledge usage. We also discuss some potential applications that MDATA could be adopted and works greatly to improve the efficiency by the stronger ability of representing knowledge.

LAY

0302-9743 ldKnowledge 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 wa

Bucket

Chinese Named Entity Recognition: Applications and Challenges,cation. We also introduce the challenge of Chinese entity recognition. Last, we compare recent works, give a vision about future work and propose the application in the Multi-dimensional Data Association and inTelligent Analysis (MDATA) model.

ARIA

Entity Alignment: Optimization by Seed Selection,aph. In order to solve the problem of insufficient number of high-quality seed entities, an iterative entity alignment method is adopted. We have done experiments on DBP15K dataset, and the experimental results show that the proposed method can achieve good entity alignment even under weak supervision.

allude

Information Cascading in Social Networks,om multiple sources in a graph, which can be applied to the SNICA problems. Recently, deep learning models have changed this situation, and it has achieved success in SNICA with its powerful implicit feature extraction capabilities. This chapter provides a comprehensive survey of recent progress in applying deep learning techniques for SNICA.