A review of relational machine learning for knowledge graphs
Relational machine learning studies methods for the statistical analysis of relational, or
graph-structured, data. In this paper, we provide a review of how such statistical models can …
graph-structured, data. In this paper, we provide a review of how such statistical models can …
Semantic Web in data mining and knowledge discovery: A comprehensive survey
P Ristoski, H Paulheim - Journal of Web Semantics, 2016 - Elsevier
Abstract Data Mining and Knowledge Discovery in Databases (KDD) is a research field
concerned with deriving higher-level insights from data. The tasks performed in that field are …
concerned with deriving higher-level insights from data. The tasks performed in that field are …
Automatic recommendation system based on hybrid filtering algorithm
Web recommendation systems are ubiquitous in the world used to overcome the product
overload on e-commerce websites. Among various filtering algorithms, Collaborative …
overload on e-commerce websites. Among various filtering algorithms, Collaborative …
A collection of benchmark datasets for systematic evaluations of machine learning on the semantic web
In the recent years, several approaches for machine learning on the Semantic Web have
been proposed. However, no extensive comparisons between those approaches have been …
been proposed. However, no extensive comparisons between those approaches have been …
Dark web data classification using neural network
There are several issues associated with Dark Web Structural Patterns mining (including
many redundant and irrelevant information), which increases the numerous types of …
many redundant and irrelevant information), which increases the numerous types of …
A survey of the first 20 years of research on semantic Web and linked data
F Gandon - Revue des Sciences et Technologies de l'Information …, 2018 - inria.hal.science
This paper is a survey of the research topics in the field of Semantic Web, Linked Data and
Web of Data. This study looks at the contributions of this research community over its first …
Web of Data. This study looks at the contributions of this research community over its first …
Machine learning in the Internet of Things: A semantic-enhanced approach
Novel Internet of Things (IoT) applications and services rely on an intelligent understanding
of the environment leveraging data gathered via heterogeneous sensors and micro-devices …
of the environment leveraging data gathered via heterogeneous sensors and micro-devices …
Machine learning for the semantic web: Lessons learnt and next research directions
C d'Amato - Semantic Web, 2020 - content.iospress.com
Abstract Machine Learning methods have been introduced in the Semantic Web for solving
problems such as link and type prediction, ontology enrichment and completion (both at …
problems such as link and type prediction, ontology enrichment and completion (both at …
[PDF][PDF] Introducing machine learning
A Ławrynowicz, V Tresp - Perspectives on Ontology Learning, 2014 - academia.edu
In this chapter we provide an overview on some of the main issues in machine learning. We
discuss machine learning both from a formal and a statistical perspective. We describe some …
discuss machine learning both from a formal and a statistical perspective. We describe some …
Substructure counting graph kernels for machine learning from rdf data
GKD De Vries, S De Rooij - Journal of Web Semantics, 2015 - Elsevier
In this paper we introduce a framework for learning from RDF data using graph kernels that
count substructures in RDF graphs, which systematically covers most of the existing kernels …
count substructures in RDF graphs, which systematically covers most of the existing kernels …