Human-Guided Learning for Probabilistic Logic Models
Frontiers in Robotics and AI, 2018 Advice-giving has been long explored in the artificial intelligence community to build robust learning algorithms when the data is noisy, incorrect or even insufficient. While logic based systems were effectively used in building expert systems, the role
Phillip Odom, Sriraam Natarajan
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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
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Connection Science, 2020 Similarity-based generalisation is fundamental to human cognition, and the ability to draw analogies based on relational similarities between superficially different domains is crucial for reasoning and inference.
Paul H. Thibodeau +2 more
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A social work study on the impact of age, gender and residential status on drug addiction [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
Mohammad Reza Iravani +4 more
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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
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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
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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
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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
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Computing Multi-Relational Sufficient Statistics for Large Databases [PDF]
, 2014 Databases contain information about which relationships do and do not hold among entities. To make this information accessible for statistical analysis requires computing sufficient statistics that combine information from different database tables. Such
Qian, Zhensong +2 more
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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
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