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47
Markov Logic Networks
 Machine Learning
, 2006
"... Abstract. We propose a simple approach to combining firstorder logic and probabilistic graphical models in a single representation. A Markov logic network (MLN) is a firstorder knowledge base with a weight attached to each formula (or clause). Together with a set of constants representing objects ..."
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Cited by 609 (37 self)
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Abstract. We propose a simple approach to combining firstorder logic and probabilistic graphical models in a single representation. A Markov logic network (MLN) is a firstorder knowledge base with a weight attached to each formula (or clause). Together with a set of constants representing objects in the domain, it specifies a ground Markov network containing one feature for each possible grounding of a firstorder formula in the KB, with the corresponding weight. Inference in MLNs is performed by MCMC over the minimal subset of the ground network required for answering the query. Weights are efficiently learned from relational databases by iteratively optimizing a pseudolikelihood measure. Optionally, additional clauses are learned using inductive logic programming techniques. Experiments with a realworld database and knowledge base in a university domain illustrate the promise of this approach.
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 ..."
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Cited by 73 (20 self)
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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.
Dependency Networks for Relational Data
 In Proceedings of the 4th IEEE International Conference on Data Mining
, 2004
"... Instance independence is a critical assumption of traditional machine learning methods contradicted by many relational datasets. For example, in scientific literature datasets there are dependencies among the references of a paper. Recent work on graphical models for relational data has demonstrated ..."
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Cited by 67 (10 self)
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Instance independence is a critical assumption of traditional machine learning methods contradicted by many relational datasets. For example, in scientific literature datasets there are dependencies among the references of a paper. Recent work on graphical models for relational data has demonstrated significant performance gains for models that exploit the dependencies among instances. In this paper, we present relational dependency networks (RDNs), a new form of graphical model capable of reasoning with such dependencies in a relational setting. We describe the details of RDN models and outline their strengths, most notably the ability to learn and reason with cyclic relational dependencies. We present RDN models learned on a number of realworld datasets, and evaluate the models in a classification context, showing significant performance improvements. In addition, we use synthetic data to evaluate the quality of model learning and inference procedures. 1.
Locationbased activity recognition
 In Advances in Neural Information Processing Systems (NIPS
, 2005
"... Learning patterns of human behavior from sensor data is extremely important for highlevel activity inference. We show how to extract and label a person’s activities and significant places from traces of GPS data. In contrast to existing techniques, our approach simultaneously detects and classifies ..."
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Cited by 56 (7 self)
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Learning patterns of human behavior from sensor data is extremely important for highlevel activity inference. We show how to extract and label a person’s activities and significant places from traces of GPS data. In contrast to existing techniques, our approach simultaneously detects and classifies the significant locations of a person and takes the highlevel context into account. Our system uses relational Markov networks to represent the hierarchical activity model that encodes the complex relations among GPS readings, activities and significant places. We apply FFTbased message passing to perform efficient summation over large numbers of nodes in the networks. We present experiments that show significant improvements over existing techniques. 1
Online filtering, smoothing and probabilistic modeling of streaming data
 in ICDE
, 2008
"... In this paper, we address the problem of extending a relational database system to facilitate efficient realtime application of dynamic probabilistic models to streaming data. We use the recently proposed abstraction of modelbased views for this purpose, by allowing users to declaratively specify ..."
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Cited by 54 (3 self)
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In this paper, we address the problem of extending a relational database system to facilitate efficient realtime application of dynamic probabilistic models to streaming data. We use the recently proposed abstraction of modelbased views for this purpose, by allowing users to declaratively specify the model to be applied, and by presenting the output of the models to the user as a probabilistic database view. We support declarative querying over such views using an extended version of SQL that allows for querying probabilistic data. Underneath we use particle filters, a class of sequential Monte Carlo algorithms commonly used to implement dynamic probabilistic models, to represent the present and historical states of the model as sets of weighted samples (particles) that are kept uptodate as new readings arrive. We develop novel techniques to convert the queries on the modelbased view directly into queries over particle tables, enabling highly efficient query processing. Finally, we present experimental evaluation of our prototype implementation over sensor data from the Intel Lab dataset that demonstrates the feasibility of online modeling of streaming data using our system and establishes the advantages of such tight integration between dynamic probabilistic models and database systems. 1
Clp(bn): Constraint logic programming for probabilistic knowledge
 In Proceedings of the 19th Conference on Uncertainty in Artificial Intelligence (UAI03
, 2003
"... Abstract. In Datalog, missing values are represented by Skolem constants. More generally, in logic programming missing values, or existentially quantified variables, are represented by terms built from Skolem functors. The CLP(BN) language represents the joint probability distribution over missing v ..."
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Cited by 53 (6 self)
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Abstract. In Datalog, missing values are represented by Skolem constants. More generally, in logic programming missing values, or existentially quantified variables, are represented by terms built from Skolem functors. The CLP(BN) language represents the joint probability distribution over missing values in a database or logic program by using constraints to represent Skolem functions. Algorithms from inductive logic programming (ILP) can be used with only minor modification to learn CLP(BN) programs. An implementation of CLP(BN) is publicly available as part of YAP Prolog at
Using relational knowledge discovery to prevent securities fraud
 In Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining
, 2005
"... We describe an application of relational knowledge discovery to a key regulatory mission of the National Association of Securities Dealers (NASD). NASD is the world’s largest privatesector securities regulator, with responsibility for preventing and discovering misconduct among securities brokers. ..."
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Cited by 48 (13 self)
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We describe an application of relational knowledge discovery to a key regulatory mission of the National Association of Securities Dealers (NASD). NASD is the world’s largest privatesector securities regulator, with responsibility for preventing and discovering misconduct among securities brokers. Our goal was to help focus NASD’s limited regulatory resources on the brokers who are most likely to engage in securities violations. Using statistical relational learning algorithms, we developed models that rank brokers with respect to the probability that they would commit a serious violation of securities regulations in the near future. Our models incorporate organizational relationships among brokers (e.g., past coworker), which domain experts consider important but have not been easily used before now. The learned models were subjected to an extensive evaluation using more than 18 months of data unseen by the model developers and comprising over two person weeks of effort by NASD staff. Model predictions were found to correlate highly with the subjective evaluations of experienced NASD examiners. Furthermore, in all performance measures, our models performed as well as or better than the handcrafted rules that are currently in use at NASD.
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 46 (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.
TildeCRF: Conditional random fields for logical sequences
 In Proceedings of the 15th European Conference on Machine Learning (ECML06
, 2006
"... Abstract. Conditional Random Fields (CRFs) provide a powerful instrument for labeling sequences. So far, however, CRFs have only been considered for labeling sequences over flat alphabets. In this paper, we describe TildeCRF, the first method for training CRFs on logical sequences, i.e., sequences o ..."
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Cited by 31 (16 self)
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Abstract. Conditional Random Fields (CRFs) provide a powerful instrument for labeling sequences. So far, however, CRFs have only been considered for labeling sequences over flat alphabets. In this paper, we describe TildeCRF, the first method for training CRFs on logical sequences, i.e., sequences over an alphabet of logical atoms. TildeCRF’s key idea is to use relational regression trees in Dietterich et al.’s gradient tree boosting approach. Thus, the CRF potential functions are represented as weighted sums of relational regression trees. Experiments show a significant improvement over established results achieved with hidden Markov models and Fisher kernels for logical sequences. 1
Building an effective representation for dynamic networks
 Journal of Computational and Graphical Statistics
, 2006
"... A dynamic network is a special type of network which is comprised of connected transactors which have repeated evolving interaction. Data on large dynamic networks such as telecommunications networks and the Intenlet are pervaive. However, represe~lting dyna~nic networks in a manner that is conduciv ..."
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Cited by 20 (2 self)
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A dynamic network is a special type of network which is comprised of connected transactors which have repeated evolving interaction. Data on large dynamic networks such as telecommunications networks and the Intenlet are pervaive. However, represe~lting dyna~nic networks in a manner that is conducive to efficient largescale analysis is a challenge. In this paper, we represent dynamic graphs using a data structure introduced by Cortes et. a]. [Q]. We advocate their representation because it accounts for the evolution of relationships between transactors through time, mitigates noise at the local transactor level, and allows for the removal of stale relationships. Our work improves on their heuristic arguments by formalizing the representation with three tunable parameters. In doing this, we develop a generic framework for evaluating and tuning any dynamic graph. We show that the storage saving approximations involved in the representation do not affect predictive performance, and typically improve it. We motivate our approach using a fraud detection example from the telecommunications industry, and demonstrate that we can outperform published results on the fraud detection task. In addition, we present preliminary analysis on web logs and email networks.