Results 1  10
of
26
Relational dependency networks
 Journal of Machine Learning Research
, 2007
"... Recent work on graphical models for relational data has demonstrated significant improvements in classification and inference when models represent the dependencies among instances. Despite its use in conventional statistical models, the assumption of instance independence is contradicted by most re ..."
Abstract

Cited by 70 (20 self)
 Add to MetaCart
Recent work on graphical models for relational data has demonstrated significant improvements in classification and inference when models represent the dependencies among instances. Despite its use in conventional statistical models, the assumption of instance independence is contradicted by most relational datasets. For example, in citation data there are dependencies among the topics of a paper’s references, and in genomic data there are dependencies among the functions of interacting proteins. In this paper, we present relational dependency networks (RDNs), graphical models that are capable of expressing and reasoning with such dependencies in a relational setting. We discuss RDNs in the context of relational Bayes networks and relational Markov networks and outline the relative strengths of RDNs—namely, the ability to represent cyclic dependencies, simple methods for parameter estimation, and efficient structure learning techniques. The strengths of RDNs are due to the use of pseudolikelihood learning techniques, which estimate an efficient approximation of the full joint distribution. We present learned RDNs for a number of realworld datasets and evaluate the models in a prediction context, showing that RDNs identify and exploit cyclic relational dependencies to achieve significant performance gains over conventional conditional models. In addition, we use synthetic data to explore model performance under various relational data characteristics, showing that RDN learning and inference techniques are accurate over a wide range of conditions.
Statistical relational learning for link prediction
 In Proceedings of the Workshop on Learning Statistical Models from Relational Data at IJCAI2003
, 2003
"... Link prediction is a complex, inherently relational, task. Be it in the domain of scientific citations, social networks or hypertext links, the underlying data are extremely noisy and the characteristics useful for prediction are not readily available in a “flat ” file format, but rather involve com ..."
Abstract

Cited by 63 (5 self)
 Add to MetaCart
Link prediction is a complex, inherently relational, task. Be it in the domain of scientific citations, social networks or hypertext links, the underlying data are extremely noisy and the characteristics useful for prediction are not readily available in a “flat ” file format, but rather involve complex relationships among objects. In this paper, we propose the application of our methodology for Statistical Relational Learning to building link prediction models. We propose an integrated approach to building regression models from data stored in relational databases in which potential predictors are generated by structured search of the space of queries to the database, and then tested for inclusion in a logistic regression. We present experimental results for the task of predicting citations made in scientific literature using relational data taken from CiteSeer. This data includes the citation graph, authorship and publication venues of papers, as well as their word content. 1
Clusterbased Concept Invention for Statistical Relational Learning
 Proceedings of the 10th SIGKDD
, 2004
"... We use clustering to derive new relations which augment database schema used in automatic generation of predictive features in statistical relational learning. Entities derived from clusters increase the expressivity of feature spaces by creating new firstclass concepts which contribute to the crea ..."
Abstract

Cited by 26 (5 self)
 Add to MetaCart
We use clustering to derive new relations which augment database schema used in automatic generation of predictive features in statistical relational learning. Entities derived from clusters increase the expressivity of feature spaces by creating new firstclass concepts which contribute to the creation of new features. For example, in CiteSeer, papers can be clustered based on words or citations giving "topics", and authors can be clustered based on documents they coauthor giving "communities". Such clusterderived concepts become part of more complex feature expressions. Out of the large number of generated features, those which improve predictive accuracy are kept in the model, as decided by statistical feature selection criteria. We present results demonstrating improved accuracy on two tasks, venue prediction and link prediction, using CiteSeer data.
nFOIL: Integrating Naïve Bayes and FOIL
, 2005
"... We present the system nFOIL. It tightly integrates the naïve Bayes learning scheme with the inductive logic programming rulelearner FOIL. In contrast to previous combinations, which have employed naïve Bayes only for postprocessing the rule sets, nFOIL employs the naïve Bayes criterion to directly ..."
Abstract

Cited by 24 (3 self)
 Add to MetaCart
We present the system nFOIL. It tightly integrates the naïve Bayes learning scheme with the inductive logic programming rulelearner FOIL. In contrast to previous combinations, which have employed naïve Bayes only for postprocessing the rule sets, nFOIL employs the naïve Bayes criterion to directly guide its search. Experimental evidence shows that nFOIL performs better than both its base line algorithm FOIL or the postprocessing approach, and is at the same time competitive with more sophisticated approaches.
Learning Ensembles of FirstOrder Clauses for RecallPrecision Curves: A Case Study in Biomedical Information Extraction
 Proceedings of the 14th International Conference on Inductive Logic Programming (ILP
, 2004
"... Many domains in the field of Inductive Logic Programming (ILP) involve highly unbalanced data. Our research has focused on Information Extraction (IE), a task that typically involves many more negative examples than positive examples. IE is the process of finding facts in unstructured text, such as ..."
Abstract

Cited by 24 (8 self)
 Add to MetaCart
Many domains in the field of Inductive Logic Programming (ILP) involve highly unbalanced data. Our research has focused on Information Extraction (IE), a task that typically involves many more negative examples than positive examples. IE is the process of finding facts in unstructured text, such as biomedical journals, and putting those facts in an organized system. In particular, we have focused on learning to recognize instances of the proteinlocalization relationship in Medline abstracts. We view the problem as a machinelearning task: given positive and negative extractions from a training corpus of abstracts, learn a logical theory that performs well on a heldaside testing set. A common way to measure performance in these domains is to use precision and recall instead of simply using accuracy. We propose Gleaner, a randomized search method which collects good clauses from a broad spectrum of points along the recall dimension in recallprecision curves and employs an "at least N of these M clauses" thresholding method to combine the selected clauses. We compare Gleaner to ensembles of standard Aleph theories and find that Gleaner produces comparable testset results in a fraction of the training time needed for ensembles.
Structure learning of Markov logic networks through iterated local search
 Proc. ECAI’08
, 2008
"... Many realworld applications of AI require both probability and firstorder logic to deal with uncertainty and structural complexity. Logical AI has focused mainly on handling complexity, and statistical AI on handling uncertainty. Markov Logic Networks (MLNs) are a powerful representation that comb ..."
Abstract

Cited by 17 (2 self)
 Add to MetaCart
Many realworld applications of AI require both probability and firstorder logic to deal with uncertainty and structural complexity. Logical AI has focused mainly on handling complexity, and statistical AI on handling uncertainty. Markov Logic Networks (MLNs) are a powerful representation that combine Markov Networks (MNs) and firstorder logic by attaching weights to firstorder formulas and viewing these as templates for features of MNs. Stateoftheart structure learning algorithms of MLNs maximize the likelihood of a relational database by performing a greedy search in the space of candidates. This can lead to suboptimal results because of the incapability of these approaches to escape local optima. Moreover, due to the combinatorially explosive space of potential candidates these methods are computationally prohibitive. We propose a novel algorithm for learning MLNs structure, based on the Iterated Local Search (ILS) metaheuristic that explores the space of structures through a biased sampling of the set of local optima. The algorithm focuses the search not on the full space of solutions but on a smaller subspace defined by the solutions that are locally optimal for the optimization engine. We show through experiments in two realworld domains that the proposed approach improves accuracy and learning time over the existing stateoftheart algorithms. 1
Firstorder probabilistic languages: Into the unknown
 PROCEEDINGS OF THE 16TH INTERNATIONAL CONFERENCE ON INDUCTIVE LOGIC PROGRAMMING. (2007
, 2007
"... This paper surveys firstorder probabilistic languages (FOPLs), which combine the expressive power of firstorder logic with a probabilistic treatment of uncertainty. We provide a taxonomy that helps make sense of the profusion of FOPLs that have been proposed over the past fifteen years. We also e ..."
Abstract

Cited by 17 (0 self)
 Add to MetaCart
This paper surveys firstorder probabilistic languages (FOPLs), which combine the expressive power of firstorder logic with a probabilistic treatment of uncertainty. We provide a taxonomy that helps make sense of the profusion of FOPLs that have been proposed over the past fifteen years. We also emphasize the importance of representing uncertainty not just about the attributes and relations of a fixed set of objects, but also about what objects exist. This leads us to Bayesian logic, or BLOG, a new language for defining probabilistic models with unknown objects. We give a brief overview of BLOG syntax and semantics, and emphasize some of the design decisions that distinguish it from other languages. Finally, we consider the challenge of constructing FOPL models automatically from data.
View learning for statistical relational learning: With an application to mammography
 Proceeding of the 19th International Joint Conference on Artificial Intelligence
, 2005
"... Statistical relational learning (SRL) constructs probabilistic models from relational databases. A key capability of SRL is the learning of arcs (in the Bayes net sense) connecting entries in different rows of a relational table, or in different tables. Nevertheless, SRL approaches currently are con ..."
Abstract

Cited by 15 (8 self)
 Add to MetaCart
Statistical relational learning (SRL) constructs probabilistic models from relational databases. A key capability of SRL is the learning of arcs (in the Bayes net sense) connecting entries in different rows of a relational table, or in different tables. Nevertheless, SRL approaches currently are constrained to use the existing database schema. For many database applications, users find it profitable to define alternative “views ” of the database, in effect defining new fields or tables. Such new fields or tables can also be highly useful in learning. We provide SRL with the capability of learning new views. 1
Say EM’ for Selecting Probabilistic Models for Logical Sequences
 In Proceedings of the twenty first conference on uncertainty in artificial intelligence
, 2005
"... Many real world sequences such as protein secondary structures or shell logs exhibit a rich internal structures. Traditional probabilistic models of sequences, however, consider sequences of flat symbols only. Logical hidden Markov models have been proposed as one solution. They deal with logical se ..."
Abstract

Cited by 15 (6 self)
 Add to MetaCart
Many real world sequences such as protein secondary structures or shell logs exhibit a rich internal structures. Traditional probabilistic models of sequences, however, consider sequences of flat symbols only. Logical hidden Markov models have been proposed as one solution. They deal with logical sequences, i.e., sequences over an alphabet of logical atoms. This comes at the expense of a more complex model selection problem. Indeed, different abstraction levels have to be explored. In this paper, we propose a novel method for selecting logical hidden Markov models from data called SAGEM. SAGEM combines generalized expectation maximization, which optimizes parameters, with structure search for model selection using inductive logic programming refinement operators. We provide convergence and experimental results that show SAGEM’s effectiveness. 1
Change of representation for statistical relational learning
 Proc. IJCAI’07
, 2007
"... Statistical relational learning (SRL) algorithms learn statistical models from relational data, such as that stored in a relational database. We previously introduced view learning for SRL, in which the view of a relational database can be automatically modified, yielding more accurate statistical m ..."
Abstract

Cited by 15 (4 self)
 Add to MetaCart
Statistical relational learning (SRL) algorithms learn statistical models from relational data, such as that stored in a relational database. We previously introduced view learning for SRL, in which the view of a relational database can be automatically modified, yielding more accurate statistical models. The present paper presents SAYUVISTA, an algorithm which advances beyond the initial view learning approach in three ways. First, it learns views that introduce new relational tables, rather than merely new fields for an existing table of the database. Second, new tables or new fields are not limited to being approximations to some target concept; instead, the new approach performs a type of predicate invention. The new approach avoids the classical problem with predicate invention, of learning many useless predicates, by keeping only new fields or tables (i.e., new predicates) that immediately improve the performance of the statistical model. Third, retained fields or tables can then be used in the definitions of further new fields or tables. We evaluate the new view learning approach on three relational classification tasks. 1