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Semantic AI in Knowledge Graphs de Sanju Tiwari

Semantic AI in Knowledge Graphs

Editat de Sanju Tiwari, Fernando Ortiz Rodriguez, Sarra Ben Abbes, Patience Usoro Usip, Rim Hantach
en Limba Engleză Hardback – 21 aug 2023
Recent combinations of semantic technology and artificial intelligence (AI) present new techniques to build intelligent systems that identify more precise results. Semantic AI in Knowledge Graphs locates itself at the forefront of this novel development, uncovering the role of machine learning to extend the knowledge graphs by graph mapping or corpus-based ontology learning.
Securing efficient results via the combination of symbolic AI and statistical AI such as entity extraction based on machine learning, text mining methods, semantic knowledge graphs, and related reasoning power, this book is the first of its kind to explore semantic AI and knowledge graphs. A range of topics are covered, from neuro-symbolic AI, explainable AI and deep learning to knowledge discovery and mining, and knowledge representation and reasoning.
A trailblazing exploration of semantic AI in knowledge graphs, this book is a significant contribution to both researchers in the field of AI and data mining as well as beginner academicians.
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Specificații

ISBN-13: 9781032321851
ISBN-10: 1032321857
Pagini: 216
Ilustrații: 25 Tables, black and white; 32 Line drawings, color; 7 Line drawings, black and white; 3 Halftones, color; 35 Illustrations, color; 7 Illustrations, black and white
Dimensiuni: 156 x 234 x 14 mm
Greutate: 0.84 kg
Ediția:1
Editura: CRC Press
Colecția CRC Press

Public țintă

Postgraduate and Professional

Cuprins

1 Leveraging Semantic Knowledge Graphs in Educational Recommenders to Address the Cold-Start Problem
Sahan Bulathwela, Maria Perez-Ortiz, Emine Yilmaz, and John Shawe-Taylor
2 Modeling Event-Centric Knowledge Graph for Crime Analysis on Online News
Federica Rollo and Laura Po
3 Semantic Natural Language Processing for Knowledge Graphs Creation
Cameron De Sa, Edlira Vakaj, Hossein Ghomeshi, and Ryan McGranaghan
4 MSE**: Multi-Modal Semantic Embeddings for Datasets with Several Positive Matchings
Jeremie Huteau, Adrian Basarab, and Florence Dupin de Saint-Cyr
5 Text-Based Emergency Alert Framework for Under-Resourced Languages in Southern Nigeria
Patience U. Usip, Funebi F. Ijebu, Ifiok J. Udo, and Ikechukwu K. Ollawa
6 Knowledge Graphs in Healthcare
Sanna Aizad and Dr. Bilal Arshad
7 Explainable Machine Learning-Based Knowledge Graph for Modeling Location-Based Recreational Services from Users Profile
Daniel Ekpenyong Asuquo, Patience Usoro Usip, and Kingsley Friday Attai
8 Building Knowledge Graph from Relational Database
Bilal Ben Mahria, Ilham Chaker, and Azeddine Zahi

Notă biografică

Sanju Tiwari is a Senior Researcher at Universidad Autonoma de Tamaulipas, Mexico. She is DAAD Post-Doc-Net AI Fellow for 2021. She previously worked as a Post-Doctoral Researcher in the Ontology Engineering Group, Universidad Polytecnica de Madrid, Spain. Her research focuses on Semantic Web, Knowledge Graphs, Artificial Intelligence and Ontology Engineering. She has edited four Books. She has worked as a Guest Editor for IGI-Global and Inderscience, Multimedia Tools and Applications Journal (MTAP) Journals and is currently working as Guest Editor for the the Journal of Cyber Security and Mobility, and IJWIS, Emerald. Sanju is General Chair (KGSWC 2020-21) and Program Chair for different International Conferences and Workshops (FTSE-2021, AMLDA-2021, RTIP2R-2021-22). Fernando Ortíz-Rodriguez is Full Professor, member of the National Research Council level C and Director of the Research Institute UAT at Tamaulipas Autonomous University, Reynosa, Tamaulipas, Mexico. He is a member of the Information Technology research group and part of the Knowledge Graph and Semantic Web Community. His research interests include Semantic Web, Information Systems, e-Government and Artificial Intelligence. He has been the main Chair and Organizer of the KGSWC multi-series conference. He isa member of National Systems Researchers (SNI) of the National Council of Science and Technology (CONACYT), Mexico’s entity promoting scientific and technological activities and high-quality scientific research. He is also a member of the Association for Computing Machinery (ACM). He holds a PhD degree on “Computer science and Artificial Intelligence and Information Systems” from the Technical University of Madrid, Spain. Sarra BEN ABBES, PhD - R&D Expert, Artificial Intelligence Patience Usip is a Senior Lecturer of Computer Science, University of Uyo,Uyo, Nigeria. She holds a Ph.D and M.Sc in Computer Science from University of Ibadan, Ibadan, Nigeria and B.Sc in Computer Science from University of Calabar, Calabar, Nigeria. She is a Post-doc fellow, Massachusetts Institute of Technology (MIT), USA and also an All Africa House Fellow, University of Cape Town, South Africa. Her research interests include Knowledge Representation and Reasoning– a sub-sub-field of Artificial Intelligence, Formal Representations, Computer Logic, Ontology Development, Knowledge Graphs, Multilingual Sematic Web, Intelligent Systems in several domains including Health, etc. She has published locally and internationally in books, book chapters, journals and conference proceedings. She has served as reviewer to several Journals locally and internationally to include MTAP, ASTEJ, etc. She has served as External Examiner for M.Sc. Dissertation, University of Cape Town, South Africa and for Ph.D. Thesis, India, etc. She has served as Speaker, program committee member and General Chair in winter schools, workshops and conferences. She has several awards to her credit and is a member of many professional bodies. Rim Hantach is a Research Scientist at ENGIE France.

Descriere

Recent combinations of Semantic Technology and Artificial Intelligence presents new techniques to build intelligent systems. Semantic AI in Knowledge Graphs locates itself at the forefront of this novel development, uncovering the role of machine learning to extend the knowledge graphs by graph mapping or corpus-based ontology learning.