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 …
Knowledge graph embedding: A survey of approaches and applications
Knowledge graph (KG) embedding is to embed components of a KG including entities and
relations into continuous vector spaces, so as to simplify the manipulation while preserving …
relations into continuous vector spaces, so as to simplify the manipulation while preserving …
Knowledge representation learning: A quantitative review
Knowledge representation learning (KRL) aims to represent entities and relations in
knowledge graph in low-dimensional semantic space, which have been widely used in …
knowledge graph in low-dimensional semantic space, which have been widely used in …
Deductive and inductive stream reasoning for semantic social media analytics
D Barbieri, D Braga, S Ceri, E Della Valle… - IEEE Intelligent …, 2010 - ieeexplore.ieee.org
A combined approach of deductive and inductive reasoning can leverage the clear
separation between the evolving (streaming) and static parts of online knowledge at …
separation between the evolving (streaming) and static parts of online knowledge at …
BOTTARI: An augmented reality mobile application to deliver personalized and location-based recommendations by continuous analysis of social media streams
In 2011, an average of three million tweets per day was posted in Seoul. Hundreds of
thousands of tweets carry the live opinion of some tens of thousands of users about …
thousands of tweets carry the live opinion of some tens of thousands of users about …
[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 …
[PDF][PDF] Core: Context-aware open relation extraction with factorization machines
We propose CORE, a novel matrix factorization model that leverages contextual information
for open relation extraction. Our model is based on factorization machines and integrates …
for open relation extraction. Our model is based on factorization machines and integrates …
Learning with memory embeddings
Embedding learning, aka representation learning, has been shown to be able to model
large-scale semantic knowledge graphs. A key concept is a mapping of the knowledge …
large-scale semantic knowledge graphs. A key concept is a mapping of the knowledge …
Querying factorized probabilistic triple databases
An increasing amount of data is becoming available in the form of large triple stores, with the
Semantic Web's linked open data cloud (LOD) as one of the most prominent examples. Data …
Semantic Web's linked open data cloud (LOD) as one of the most prominent examples. Data …
Induction of concepts in web ontologies through terminological decision trees
A new framework for the induction of logical decision trees is presented. Differently from the
original setting, tests at the tree nodes are expressed with Description Logic concepts. This …
original setting, tests at the tree nodes are expressed with Description Logic concepts. This …