Type Extension Trees for feature construction and learning in relational domains

Authors:
Manfred Jaeger;Marco Lippi;Andrea Passerini;Paolo Frasconi
Affiliations:
Institut for Datalogi, Aalborg Universitet, Denmark;Dipartimento di Ingegneria dellInformazione e Scienze Matematiche, Universití degli Studi di Siena, Italy;Dipartimento di Ingegneria e Scienza dellInformazione, Universití degli Studi di Trento, Italy;Dipartimento di Ingegneria dellInformazione, Universití degli Studi di Firenze, Italy
Venue:
Artificial Intelligence
Year:
2013

Citing 33
Cited 0

The anatomy of a large-scale hypertextual Web search engine

WWW7 Proceedings of the seventh international conference on World Wide Web 7
Efficient clustering of high-dimensional data sets with application to reference matching

Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
The Earth Mover's Distance as a Metric for Image Retrieval

International Journal of Computer Vision
Learning Logical Definitions from Relations

Machine Learning
Multi-relational Decision Tree Induction

PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Propositionalisation and Aggregates

PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
The DBLP Computer Science Bibliography: Evolution, Research Issues, Perspectives

SPIRE 2002 Proceedings of the 9th International Symposium on String Processing and Information Retrieval
Transformation-Based Learning Using Multirelational Aggregation

ILP '01 Proceedings of the 11th International Conference on Inductive Logic Programming
Aggregation-based feature invention and relational concept classes

Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning relational probability trees

Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning the structure of Markov logic networks

ICML '05 Proceedings of the 22nd international conference on Machine learning
Learning first-order probabilistic models with combining rules

ICML '05 Proceedings of the 22nd international conference on Machine learning
Markov logic networks

Machine Learning
First order random forests: Learning relational classifiers with complex aggregates

Machine Learning
Entity Resolution with Markov Logic

ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Logical and Relational Learning: From ILP to MRDM (Cognitive Technologies)

Logical and Relational Learning: From ILP to MRDM (Cognitive Technologies)
An Efficient Earth Mover's Distance Algorithm for Robust Histogram Comparison

IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)

Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
EigenRank: a ranking-oriented approach to collaborative filtering

Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Relational learning via collective matrix factorization

Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
ArnetMiner: extraction and mining of academic social networks

Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Feature Discovery with Type Extension Trees

ILP '08 Proceedings of the 18th international conference on Inductive Logic Programming
Bi-directional Joint Inference for Entity Resolution and Segmentation Using Imperatively-Defined Factor Graphs

ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Joint inference in information extraction

AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Learning probabilistic relational models

IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Top-down induction of first-order logical decision trees

Artificial Intelligence
Fast learning of relational kernels

Machine Learning
Concept discovery on relational databases: New techniques for search space pruning and rule quality improvement

Knowledge-Based Systems
Graph Kernels

The Journal of Machine Learning Research
Relational information gain

Machine Learning
Relational bayesian networks

UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Refining aggregate conditions in relational learning

PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Lifted probabilistic inference by first-order knowledge compilation

IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three

Quantified Score

Hi-index	0.00

Visualization

Abstract

Type Extension Trees are a powerful representation language for ''count-of-count'' features characterizing the combinatorial structure of neighborhoods of entities in relational domains. In this paper we present a learning algorithm for Type Extension Trees (TET) that discovers informative count-of-count features in the supervised learning setting. Experiments on bibliographic data show that TET-learning is able to discover the count-of-count feature underlying the definition of the h-index, and the inverse document frequency feature commonly used in information retrieval. We also introduce a metric on TET feature values. This metric is defined as a recursive application of the Wasserstein-Kantorovich metric. Experiments with a k-NN classifier show that exploiting the recursive count-of-count statistics encoded in TET values improves classification accuracy over alternative methods based on simple count statistics.