Wir verwenden Cookies und Analyse-Tools, um die Nutzerfreundlichkeit der Internet-Seite zu verbessern und für Marketingzwecke. Wenn Sie fortfahren, diese Seite zu verwenden, nehmen wir an, dass Sie damit einverstanden sind. Zur Datenschutzerklärung.
Web and Big Data
Details
This book constitutes the thoroughly refereed post-conference proceedings of the Third APWeb-WAIM 2019 Workshops, held jointly with the Third International Joint Conference APWeb-WAIM 2019 in Macau, China, in August 2019.
The 8 full papers presented together with 1 invited talk were carefully reviewed and selected from 18 submissions. The papers originating from two workshops, KGMA and DSEA, present cutting-edge ideas, results, experiences, techniques, and tools from all aspects of the management, analysis, and application of knowledge graphs in different domains, as well as theoretical foundations, algorithms, systems, models and applications for big data management and mining.
Inhalt
KGMA.- Distributed Query Evaluation over Large RDF (Invited Talk).- Classification-based Emoji Recommendation for User Social Networks.- Leveraging Context Information for Joint Entity and Relation Linking.- Community Detection in Knowledge Graph Network with Matrix Factorization Learning.- A Survey of Relation Extraction of Knowledge Graphs.- DSEA.- PEVR: Pose Estimation for Vehicle Re-identification.- The Research of Chinese Ethnical Face Recognition Based on Deep Learning.- Model of Charging Stations Construction and Electric Vehicles Development Prediction.- Boundary Detector Encoder and Decoder with Soft Attention for Video Captioning.
Weitere Informationen
- Allgemeine Informationen
- GTIN 09783030339814
- Editor Xiaofeng Zhu, Jingkuan Song
- Sprache Englisch
- Auflage 1st edition 2019
- Größe H235mm x B155mm x T8mm
- Jahr 2019
- EAN 9783030339814
- Format Kartonierter Einband
- ISBN 3030339815
- Veröffentlichung 01.11.2019
- Titel Web and Big Data
- Untertitel APWeb-WAIM 2019 International Workshops, KGMA and DSEA, Chengdu, China, August 1-3, 2019, Revised Selected Papers
- Gewicht 207g
- Herausgeber Springer International Publishing
- Anzahl Seiten 128
- Lesemotiv Verstehen
- Genre Informatik