A Review of Relational Machine Learning for Knowledge Graphs [PDF]
, 2015 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 be “trained” on large knowledge graphs, and then used to predict new ...
Gabrilovich, Evgeniy +3 more
core +2 more sources
An empirical investigation of intellectual capital components on each others and organizational learning capabilities [PDF]
Management Science Letters, 2013 During the past few years, there have been growing interests on intellectual capital due to industrial changes on the market. Thus, identifying different ways to create, manage, and evaluate the impact of intellectual capital has remained an open area of
Nabi ollah Nejatizadeh +4 more
doaj
Learning Models over Relational Data using Sparse Tensors and Functional Dependencies
, 2020 Integrated solutions for analytics over relational databases are of great practical importance as they avoid the costly repeated loop data scientists have to deal with on a daily basis: select features from data residing in relational databases using ...
Khamis, Mahmoud Abo +4 more
core +1 more source
Fast relational learning using bottom clause propositionalization with artificial neural networks [PDF]
, 2013 Relational learning can be described as the task of learning first-order logic rules from examples. It has enabled a number of new machine learning applications, e.g. graph mining and link analysis.
A. Paes +56 more
core +1 more source
Pattern discovery and disentanglement on relational datasets
Scientific Reports, 2021 Machine Learning has made impressive advances in many applications akin to human cognition for discernment. However, success has been limited in the areas of relational datasets, particularly for data with low volume, imbalanced groups, and mislabeled ...
Andrew K. C. Wong +2 more
doaj +1 more source
kLog: A Language for Logical and Relational Learning with Kernels
, 2014 We introduce kLog, a novel approach to statistical relational learning. Unlike standard approaches, kLog does not represent a probability distribution directly. It is rather a language to perform kernel-based learning on expressive logical and relational
Altun +92 more
core +1 more source
On the Implementation of the Probabilistic Logic Programming Language ProbLog [PDF]
, 2009 The past few years have seen a surge of interest in the field of probabilistic logic learning and statistical relational learning. In this endeavor, many probabilistic logics have been developed.
ANGELIKA KIMMIG +23 more
core +3 more sources
Investigating the effects of intellectual capital on organizational performance measurement through organizational learning capabilities [PDF]
Management Science Letters, 2013 During the past few years, there have been growing interests on intellectual capital due to industrial changes on the market. Thus, identifying different ways to create, manage, and evaluate the impact of intellectual capital has remained an open area of
Nabi ollah Nejatizadeh +4 more
doaj
Statistical Relational Learning with Formal Ontologies [PDF]
, 2009 We propose a learning approach for integrating formal knowledge into statistical inference by exploiting ontologies as a semantically rich and fully formal representation of prior knowledge. The logical constraints deduced from ontologies can be utilized to enhance and control the learning task by enforcing description logic satisfiability in a latent ...
Rettinger, Achim +2 more
openaire +2 more sources
Exploiting prior knowledge in Intelligent Assistants - Combining relational models with hierarchies [PDF]
, 2008 Statitsical relational models have been successfully used to model static probabilistic relationships between the entities of the domain. In this talk, we illustrate their use in a dynamic decison-theoretic setting where the task is to assist a user by ...
Fern, Alan +2 more
core +1 more source