Machine Learning with and for Semantic Web Knowledge Graphs

2018 ◽  
pp. 110-141 ◽  
Author(s):  
Heiko Paulheim
Keyword(s):  
Machine Learning ◽  
Semantic Web ◽  
Knowledge Graphs ◽  
Web Knowledge

scholarly journals Machine Learning Meets the Semantic Web

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Konstantinos Ilias Kotis ◽  
Konstantina Zachila ◽  
Evaggelos Paparidis
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Semantic Web ◽  
Graph Representation ◽  
Order Relations ◽  
Knowledge Graphs ◽  
Graph Neural Networks ◽  
Different Levels ◽  
Remarkable Progress ◽  
Open Issues

Remarkable progress in research has shown the efficiency of Knowledge Graphs (KGs) in extracting valuable external knowledge in various domains. A Knowledge Graph (KG) can illustrate high-order relations that connect two objects with one or multiple related attributes. The emerging Graph Neural Networks (GNN) can extract both object characteristics and relations from KGs. This paper presents how Machine Learning (ML) meets the Semantic Web and how KGs are related to Neural Networks and Deep Learning. The paper also highlights important aspects of this area of research, discussing open issues such as the bias hidden in KGs at different levels of graph representation.

scholarly journals Domain Ontology, an Instrument of Semantic Web Knowledge Management in e-Learning

2012 ◽  
Vol 5 (4) ◽  
Author(s):  
Anatoly Jasonovich Gladun ◽  
Julia Vitalijevna Rogushina ◽  
Jeanne Schreurs
Keyword(s):  
Knowledge Management ◽  
Semantic Web ◽  
Domain Ontology ◽  
E Learning ◽  
Web Knowledge

A Requirements Driven Framework for Benchmarking Semantic Web Knowledge Base Systems

2007 ◽  
Vol 19 (2) ◽  
pp. 297-309 ◽  
Author(s):  
Yuanbo Guo ◽  
Abir Qasem ◽  
Zhengxiang Pan ◽  
Jeff Heflin
Keyword(s):  
Semantic Web ◽  
Knowledge Base ◽  
Knowledge Base Systems ◽  
Web Knowledge

Towards Graph Machine Learning for Smart Grid Knowledge Graphs in Industrial Scenarios

2021 ◽  
Author(s):  
Guido Walter Di Donato ◽  
Andrea Damiani ◽  
Alberto Parravicini ◽  
Enea Bionda ◽  
Francesca Soldan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Smart Grid ◽  
Knowledge Graphs

Automatic Semantic Annotation Using Machine Learning

2012 ◽  
pp. 535-578
Author(s):  
Jie Tang ◽  
Duo Zhang ◽  
Limin Yao ◽  
Yi Li
Keyword(s):  
Machine Learning ◽  
Semantic Web ◽  
Semantic Annotation ◽  
Rapid Expansion ◽  
Semantic Data ◽  
Community Boundaries ◽  
Research Problems ◽  
Common Framework ◽  
Automated Discovery ◽  
The Web

This chapter aims to give a thorough investigation of the techniques for automatic semantic annotation. The Semantic Web provides a common framework that allows data to be shared and reused across applications, enterprises, and community boundaries. However, lack of annotated semantic data is a bottleneck to make the Semantic Web vision a reality. Therefore, it is indeed necessary to automate the process of semantic annotation. In the past few years, there was a rapid expansion of activities in the semantic annotation area. Many methods have been proposed for automating the annotation process. However, due to the heterogeneity and the lack of structure of the Web data, automated discovery of the targeted or unexpected knowledge information still present many challenging research problems. In this chapter, we study the problems of semantic annotation and introduce the state-of-the-art methods for dealing with the problems. We will also give a brief survey of the developed systems based on the methods. Several real-world applications of semantic annotation will be introduced as well. Finally, some emerging challenges in semantic annotation will be discussed.

scholarly journals A Review of Relational Machine Learning for Knowledge Graphs

2016 ◽  
Vol 104 (1) ◽  
pp. 11-33 ◽  
Author(s):  
Maximilian Nickel ◽  
Kevin Murphy ◽  
Volker Tresp ◽  
Evgeniy Gabrilovich
Keyword(s):  
Machine Learning ◽  
Relational Machine Learning ◽  
Knowledge Graphs
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