21,306 research outputs found
Inference, Learning, and Population Size: Projectivity for SRL Models
A subtle difference between propositional and relational data is that in many
relational models, marginal probabilities depend on the population or domain
size. This paper connects the dependence on population size to the classic
notion of projectivity from statistical theory: Projectivity implies that
relational predictions are robust with respect to changes in domain size. We
discuss projectivity for a number of common SRL systems, and identify syntactic
fragments that are guaranteed to yield projective models. The syntactic
conditions are restrictive, which suggests that projectivity is difficult to
achieve in SRL, and care must be taken when working with different domain
sizes
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
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
Structure Selection from Streaming Relational Data
Statistical relational learning techniques have been successfully applied in
a wide range of relational domains. In most of these applications, the human
designers capitalized on their background knowledge by following a
trial-and-error trajectory, where relational features are manually defined by a
human engineer, parameters are learned for those features on the training data,
the resulting model is validated, and the cycle repeats as the engineer adjusts
the set of features. This paper seeks to streamline application development in
large relational domains by introducing a light-weight approach that
efficiently evaluates relational features on pieces of the relational graph
that are streamed to it one at a time. We evaluate our approach on two social
media tasks and demonstrate that it leads to more accurate models that are
learned faster
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