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

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發(fā)表于 2025-3-21 19:07:36 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱MDATA: A New Knowledge Representation Model
副標(biāo)題Theory, Methods and
編輯Yan Jia,Zhaoquan Gu,Aiping Li
視頻videohttp://file.papertrans.cn/621/620094/620094.mp4
概述Introduces a new knowledge representation model called MDATA.Explores some key technologies of the MDATA model.Written by experts in the field
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: MDATA: A New Knowledge Representation Model; Theory, Methods and Yan Jia,Zhaoquan Gu,Aiping Li Book 2021 Springer Nature Switzerland AG 20
描述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
出版日期Book 2021
關(guān)鍵詞artificial intelligence; cognitive model; data mining; databases; entity alignment; entity recognition; in
版次1
doihttps://doi.org/10.1007/978-3-030-71590-8
isbn_softcover978-3-030-71589-2
isbn_ebook978-3-030-71590-8Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2021
The information of publication is updating

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978-3-030-71589-2Springer Nature Switzerland AG 2021
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The Framework of the MDATA Computing Model,. The computing paradigm is also shifting from centralized computing in the cloud to collaborative computing in the front end, middle layer, and cloud. Therefore, traditional computing paradigms such as cloud computing and edge computing can no longer satisfy the evolving computing needs of big data
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Spatiotemporal Data Cleaning and Knowledge Fusion, key technologies supporting knowledge fusion. In this chapter, we give a brief overview of some important technologies of knowledge fusion and data cleaning. We first briefly introduce the motivation and background of knowledge fusion and data cleaning. Then, we discuss some of the recent methods f
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Chinese Named Entity Recognition: Applications and Challenges,nswering, reading comprehension, knowledge graph, machine translation and other fields. With the development of natural language processing techniques and the enhancement of text mining, the acquisition of semantic knowledge in text area becomes very important, and named entity recognition is the fo
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Joint Extraction of Entities and Relations: An Advanced BERT-based Decomposition Method, tasks. However, most existing joint models cannot solve the problem of overlapping triples well. We propose an efficient end-to-end model for joint extraction of entities and overlapping relations in this chapter. Firstly, the BERT pre-training model is introduced to model the text more finely. Nex
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