1,824 research outputs found
Statistical relational learning with soft quantifiers
Quantification in statistical relational learning (SRL) is either existential or universal, however humans might be more inclined to express knowledge using soft quantifiers, such as ``most'' and ``a few''. In this paper, we define the syntax and semantics of PSL^Q, a new SRL framework that supports reasoning with soft quantifiers, and present its most probable explanation (MPE) inference algorithm. To the best of our knowledge, PSL^Q is the first SRL framework that combines soft quantifiers with first-order logic rules for modelling uncertain relational data. Our experimental results for link prediction in social trust networks demonstrate that the use of soft quantifiers not only allows for a natural and intuitive formulation of domain knowledge, but also improves the accuracy of inferred results
Transforming Graph Representations for Statistical Relational Learning
Relational data representations have become an increasingly important topic
due to the recent proliferation of network datasets (e.g., social, biological,
information networks) and a corresponding increase in the application of
statistical relational learning (SRL) algorithms to these domains. In this
article, we examine a range of representation issues for graph-based relational
data. Since the choice of relational data representation for the nodes, links,
and features can dramatically affect the capabilities of SRL algorithms, we
survey approaches and opportunities for relational representation
transformation designed to improve the performance of these algorithms. This
leads us to introduce an intuitive taxonomy for data representation
transformations in relational domains that incorporates link transformation and
node transformation as symmetric representation tasks. In particular, the
transformation tasks for both nodes and links include (i) predicting their
existence, (ii) predicting their label or type, (iii) estimating their weight
or importance, and (iv) systematically constructing their relevant features. We
motivate our taxonomy through detailed examples and use it to survey and
compare competing approaches for each of these tasks. We also discuss general
conditions for transforming links, nodes, and features. Finally, we highlight
challenges that remain to be addressed
Soft quantification in statistical relational learning
We present a new statistical relational learning (SRL) framework that supports reasoning with soft quantifiers, such as "most" and "a few." We define the syntax and the semantics of this language, which we call , and present a most probable explanation inference algorithm for it. To the best of our knowledge, is the first SRL framework that combines soft quantifiers with first-order logic rules for modelling uncertain relational data. Our experimental results for two real-world applications, link prediction in social trust networks and user profiling in social networks, demonstrate that the use of soft quantifiers not only allows for a natural and intuitive formulation of domain knowledge, but also improves inference accuracy
Improving Data Quality by Leveraging Statistical Relational Learning
Digitally collected data su
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ers from many data quality issues, such as duplicate, incorrect, or incomplete data. A common
approach for counteracting these issues is to formulate a set of data cleaning rules to identify and repair incorrect, duplicate and
missing data. Data cleaning systems must be able to treat data quality rules holistically, to incorporate heterogeneous constraints
within a single routine, and to automate data curation. We propose an approach to data cleaning based on statistical relational
learning (SRL). We argue that a formalism - Markov logic - is a natural fit for modeling data quality rules. Our approach
allows for the usage of probabilistic joint inference over interleaved data cleaning rules to improve data quality. Furthermore, it
obliterates the need to specify the order of rule execution. We describe how data quality rules expressed as formulas in first-order
logic directly translate into the predictive model in our SRL framework
kLog: A Language for Logical and Relational Learning with Kernels
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 representations. kLog allows users to specify
learning problems declaratively. It builds on simple but powerful concepts:
learning from interpretations, entity/relationship data modeling, logic
programming, and deductive databases. Access by the kernel to the rich
representation is mediated by a technique we call graphicalization: the
relational representation is first transformed into a graph --- in particular,
a grounded entity/relationship diagram. Subsequently, a choice of graph kernel
defines the feature space. kLog supports mixed numerical and symbolic data, as
well as background knowledge in the form of Prolog or Datalog programs as in
inductive logic programming systems. The kLog framework can be applied to
tackle the same range of tasks that has made statistical relational learning so
popular, including classification, regression, multitask learning, and
collective classification. We also report about empirical comparisons, showing
that kLog can be either more accurate, or much faster at the same level of
accuracy, than Tilde and Alchemy. kLog is GPLv3 licensed and is available at
http://klog.dinfo.unifi.it along with tutorials
A Novel Neural-symbolic System under Statistical Relational Learning
A key objective in field of artificial intelligence is to develop cognitive
models that can exhibit human-like intellectual capabilities. One promising
approach to achieving this is through neural-symbolic systems, which combine
the strengths of deep learning and symbolic reasoning. However, current
approaches in this area have been limited in their combining way,
generalization and interpretability. To address these limitations, we propose a
general bi-level probabilistic graphical reasoning framework called GBPGR. This
framework leverages statistical relational learning to effectively integrate
deep learning models and symbolic reasoning in a mutually beneficial manner. In
GBPGR, the results of symbolic reasoning are utilized to refine and correct the
predictions made by the deep learning models. At the same time, the deep
learning models assist in enhancing the efficiency of the symbolic reasoning
process. Through extensive experiments, we demonstrate that our approach
achieves high performance and exhibits effective generalization in both
transductive and inductive tasks
Why deterministic logic is hard to learn but Statistical Relational Learning works
A brief note on why we think that the statistical relational learning framework is a great advancement over deterministic logic -- in particular in the context of model-based Reinforcement Learning
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