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146
A Survey of Kernels for Structured Data
, 2003
"... Kernel methods in general and support vector machines in particular have been successful in various learning tasks on data represented in a single table. Much ‘realworld’ data, however, is structured – it has no natural representation in a single table. Usually, to apply kernel methods to ‘realwor ..."
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Cited by 146 (2 self)
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Kernel methods in general and support vector machines in particular have been successful in various learning tasks on data represented in a single table. Much ‘realworld’ data, however, is structured – it has no natural representation in a single table. Usually, to apply kernel methods to ‘realworld’ data, extensive preprocessing is performed to embed the data into a real vector space and thus in a single table. This survey describes several approaches of defining positive definite kernels on structured instances directly.
Graph mining: laws, generators, and algorithms
 ACM COMPUT SURV (CSUR
, 2006
"... How does the Web look? How could we tell an abnormal social network from a normal one? These and similar questions are important in many fields where the data can intuitively be cast as a graph; examples range from computer networks to sociology to biology and many more. Indeed, any M: N relation in ..."
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Cited by 130 (7 self)
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How does the Web look? How could we tell an abnormal social network from a normal one? These and similar questions are important in many fields where the data can intuitively be cast as a graph; examples range from computer networks to sociology to biology and many more. Indeed, any M: N relation in database terminology can be represented as a graph. A lot of these questions boil down to the following: “How can we generate synthetic but realistic graphs? ” To answer this, we must first understand what patterns are common in realworld graphs and can thus be considered a mark of normality/realism. This survey give an overview of the incredible variety of work that has been done on these problems. One of our main contributions is the integration of points of view from physics, mathematics, sociology, and computer science. Further, we briefly describe recent advances on some related and interesting graph problems.
Linkage and autocorrelation cause feature selection bias in relational learning
 In Proc. of the 19th Intl Conference on Machine Learning
, 2002
"... Two common characteristics of relational data sets — concentrated linkage and relational autocorrelation — can cause learning algorithms to be strongly biased toward certain features, irrespective of their predictive power. We identify these characteristics, define quantitative measures of their sev ..."
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Cited by 119 (34 self)
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Two common characteristics of relational data sets — concentrated linkage and relational autocorrelation — can cause learning algorithms to be strongly biased toward certain features, irrespective of their predictive power. We identify these characteristics, define quantitative measures of their severity, and explain how they produce this bias. We show how linkage and autocorrelation affect a representative algorithm for feature selection by applying the algorithm to synthetic data and to data drawn from the Internet Movie Database. 1.1 Relational Data and Statistical Dependence Figure 1 presents two simple relational data sets. In each
A Simple Relational Classifier
 Proceedings of the Second Workshop on MultiRelational Data Mining (MRDM2003) at KDD2003
, 2003
"... We analyze a Relational Neighbor (RN) classifier, a simple relational predictive model that predicts only based on class labels of related neighbors, using no learning and no inherent attributes. We show that it performs surprisingly well by comparing it to more complex models such as Probabilist ..."
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Cited by 111 (14 self)
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We analyze a Relational Neighbor (RN) classifier, a simple relational predictive model that predicts only based on class labels of related neighbors, using no learning and no inherent attributes. We show that it performs surprisingly well by comparing it to more complex models such as Probabilistic Relational Models and Relational Probability Trees on three data sets from published work.
Probabilistic inductive logic programming
 In ALT
, 2004
"... Abstract. Probabilistic inductive logic programming aka. statistical relational learning addresses one of the central questions of artificial intelligence: the integration of probabilistic reasoning with machine learning and first order and relational logic representations. A rich variety of diffe ..."
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Cited by 70 (9 self)
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Abstract. Probabilistic inductive logic programming aka. statistical relational learning addresses one of the central questions of artificial intelligence: the integration of probabilistic reasoning with machine learning and first order and relational logic representations. A rich variety of different formalisms and learning techniques have been developed. A unifying characterization of the underlying learning settings, however, is missing so far. In this chapter, we start from inductive logic programming and sketch how the inductive logic programming formalisms, settings and techniques can be extended to the statistical case. More precisely, we outline three classical settings for inductive logic programming, namely learning from entailment, learning from interpretations, and learning from proofs or traces, and show how they can be adapted to cover stateoftheart statistical relational learning approaches. 1
ExpertGuided Subgroup Discovery: Methodology and Application
 Journal of Artificial Intelligence Research
, 2002
"... This paper presents an approach to expertguided subgroup discovery. The main step of the subgroup discovery process, the induction of subgroup descriptions, is performed by a heuristic beam search algorithm, using a novel parametrized definition of rule quality which is analyzed in detail. The othe ..."
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Cited by 55 (10 self)
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This paper presents an approach to expertguided subgroup discovery. The main step of the subgroup discovery process, the induction of subgroup descriptions, is performed by a heuristic beam search algorithm, using a novel parametrized definition of rule quality which is analyzed in detail. The other important steps of the proposed subgroup discovery process are the detection of statistically significant properties of selected subgroups and subgroup visualization: statistically significant properties are used to enrich the descriptions of induced subgroups, while the visualization shows subgroup properties in the form of distributions of the numbers of examples in the subgroups. The approach is illustrated by the results obtained for a medical problem of early detection of patient risk groups.
Logical hidden markov models
 Journal of Artificial Intelligence Research
, 2006
"... Logical hidden Markov models (LOHMMs) upgrade traditional hidden Markov models to deal with sequences of structured symbols in the form of logical atoms, rather than flat characters. This note formally introduces LOHMMs and presents solutions to the three central inference problems for LOHMMs: evalu ..."
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Cited by 52 (13 self)
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Logical hidden Markov models (LOHMMs) upgrade traditional hidden Markov models to deal with sequences of structured symbols in the form of logical atoms, rather than flat characters. This note formally introduces LOHMMs and presents solutions to the three central inference problems for LOHMMs: evaluation, most likely hidden state sequence and parameter estimation. The resulting representation and algorithms are experimentally evaluated on problems from the domain of bioinformatics. 1.
Probabilistic Logic Learning
 ACMSIGKDD Explorations: Special issue on MultiRelational Data Mining
, 2004
"... The past few years have witnessed an significant interest in probabilistic logic learning, i.e. in research lying at the intersection of probabilistic reasoning, logical representations, and machine learning. A rich variety of di#erent formalisms and learning techniques have been developed. This pap ..."
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Cited by 43 (10 self)
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The past few years have witnessed an significant interest in probabilistic logic learning, i.e. in research lying at the intersection of probabilistic reasoning, logical representations, and machine learning. A rich variety of di#erent formalisms and learning techniques have been developed. This paper provides an introductory survey and overview of the stateof theart in probabilistic logic learning through the identification of a number of important probabilistic, logical and learning concepts.
Deep transfer via secondorder markov logic
 In Proceedings of the AAAI Workshop on Transfer Learning For Complex Tasks
, 2008
"... Standard inductive learning requires that training and test instances come from the same distribution. Transfer learning seeks to remove this restriction. In shallow transfer, test instances are from the same domain, but have a different distribution. In deep transfer, test instances are from a diff ..."
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Cited by 41 (4 self)
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Standard inductive learning requires that training and test instances come from the same distribution. Transfer learning seeks to remove this restriction. In shallow transfer, test instances are from the same domain, but have a different distribution. In deep transfer, test instances are from a different domain entirely (i.e., described by different predicates). Humans routinely perform deep transfer, but few learning systems, if any, are capable of it. In this paper we propose an approach based on a form of secondorder Markov logic. Our algorithm discovers structural regularities in the source domain in the form of Markov logic formulas with predicate variables, and instantiates these formulas with predicates from the target domain. Using this approach, we have successfully transferred learned knowledge among molecular biology, social network and Web domains. The discovered patterns include broadly useful properties of predicates, like symmetry and transitivity, and relations among predicates, such as various forms of homophily. 1.
Statistical Relational Learning for Document Mining
, 2003
"... A major obstacle to fully integrated deployment of statistical learners is the assumption that data sits in a single table, even though most realworld databases have complex relational structures. In this paper, we introduce an integrated approach to building regression models from data stored ..."
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Cited by 41 (5 self)
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A major obstacle to fully integrated deployment of statistical learners is the assumption that data sits in a single table, even though most realworld databases have complex relational structures. In this paper, we introduce an integrated approach to building regression models from data stored in relational databases. Potential features 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 where scientific papers will be published based on relational data taken from CiteSeer. This data includes word counts in the document, frequently cited authors or papers, cocitations, publication venues of cited papers, word cooccurrences, and word counts in cited or citing documents. Our approach results in classification accuracies superior to those achieved when using classical "flat" features. Our classification task also serves as a "where to publish?" conference/journal recommendation task.