87 research outputs found
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
Fast relational learning using bottom clause propositionalization with artificial neural networks
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. Inductive Logic Programming (ILP) performs relational learning either directly by manipulating first-order rules or through propositionalization, which translates the relational task into an attribute-value learning task by representing subsets of relations as features. In this paper, we introduce a fast method and system for relational learning based on a novel propositionalization called Bottom Clause Propositionalization (BCP). Bottom clauses are boundaries in the hypothesis search space used by ILP systems Progol and Aleph. Bottom clauses carry semantic meaning and can be mapped directly onto numerical vectors, simplifying the feature extraction process. We have integrated BCP with a well-known neural-symbolic system, C-IL2P, to perform learning from numerical vectors. C-IL2P uses background knowledge in the form of propositional logic programs to build a neural network. The integrated system, which we call CILP++, handles first-order logic knowledge and is available for download from Sourceforge. We have evaluated CILP++ on seven ILP datasets, comparing results with Aleph and a well-known propositionalization method, RSD. The results show that CILP++ can achieve accuracy comparable to Aleph, while being generally faster, BCP achieved statistically significant improvement in accuracy in comparison with RSD when running with a neural network, but BCP and RSD perform similarly when running with C4.5. We have also extended CILP++ to include a statistical feature selection method, mRMR, with preliminary results indicating that a reduction of more than 90 % of features can be achieved with a small loss of accuracy
Automated Construction of Relational Attributes ACORA: A Progress Report
Data mining research has not only development a large number of algorithms, but also
enhanced our knowledge and understanding of their applicability and performance.
However, the application of data mining technology in business environments is still no
very common, despite the fact that organizations have access to large amounts of data
and make decisions that could profit from data mining on a daily basis. One of the
reasons is the mismatch between data representation for data storage and data analysis.
Data are most commonly stored in multi-table relational databases whereas data mining
methods require that the data be represented as a simple feature vector. This work
presents a general framework for feature construction from multiple relational tables for
data mining applications. The second part describes our prototype implementation
ACORA (Automated Construction of Relational Features).Information Systems Working Papers Serie
Structural Logistic Regression for Link Analysis
We present Structural Logistic Regression, an extension of logistic regression to modeling relational data. It is an integrated approach to building regression models from data stored in relational databases in which potential predictors, both boolean and real-valued, are generated by structured search in the space of queries to the database, and then tested with statistical information criteria for inclusion in a logistic regression. Using statistics and relational representation allows modeling in noisy domains with complex structure. Link prediction is a task of high interest with exactly such characteristics. Be it in the domain of scientific citations, social networks or hypertext, the underlying data are extremely noisy and the features useful for prediction are not readily available in a flat file format. We propose the application of Structural Logistic Regression to building link prediction models, and present experimental results for the task of predicting citations made in scientific literature using relational data taken from the CiteSeer search engine. This data includes the citation graph, authorship and publication venues of papers, as well as their word content
Automated Construction of Relational Attributes ACORA: A Progress Report
Data mining research has not only development a large number of algorithms, but also
enhanced our knowledge and understanding of their applicability and performance.
However, the application of data mining technology in business environments is still no
very common, despite the fact that organizations have access to large amounts of data
and make decisions that could profit from data mining on a daily basis. One of the
reasons is the mismatch between data representation for data storage and data analysis.
Data are most commonly stored in multi-table relational databases whereas data mining
methods require that the data be represented as a simple feature vector. This work
presents a general framework for feature construction from multiple relational tables for
data mining applications. The second part describes our prototype implementation
ACORA (Automated Construction of Relational Features).Information Systems Working Papers Serie
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