Research on statistical relational learning at the University of Washington (original) (raw)

Research on Statistical Relational Learning

2007

This paper presents an overview of the research on learning statistical models of relational data being carried out at the University of Washington. Our work falls into five main directions: learning models of social networks; learning models of sequential relational processes; scaling up statistical relational learning to massive data sources; learning for knowledge integration; and learning programs in procedural languages. We describe some of the common themes and research issues arising from this work.

Learning statistical models from relational data

2011

Abstract Statistical Relational Learning (SRL) is a subarea of machine learning which combines elements from statistical and probabilistic modeling with languages which support structured data representations.

SRL2003 IJCAI 2003 Workshop on Learning Statistical Models from Relational Data

2003

This workshop is the second in a series of workshops held in conjunction with AAAI and IJCAI. The first workshop was held in July, 2000 at AAAI. Notes from that workshop are available at http://robotics. stanford. edu/srl/. Since the AAAI 2000 workshop, there has been a surge of interest in this area. The efforts have been diffused across a wide collection of sub-areas in computer science including machine learning, database management and theoretical computer science.

ICML 2004 Workshop on Statistical Relational Learning and its Connections to Other Fields

This workshop is the third in a series of workshops held in conjunction with AAAI and IJCAI. The first workshop was held in July, 2000 at AAAI. Notes from that workshop are available at http://robotics. stanford. edu/srl/. The second workshop was held in July, 2003 at IJCAI. Notes from that workshop are available at http://kdl. cs. umass. edu/srl2003/There has been a surge of interest in this area.

Introduction to statistical relational learning

2007

Handling inherent uncertainty and exploiting compositional structure are fundamental to understanding and designing large-scale systems. Statistical relational learning builds on ideas from probability theory and statistics to address uncertainty while incorporating tools from logic, databases, and programming languages to represent structure.

Statistical relational learning: Four claims and a survey

2003

Statistical relational learning (SRL) research has made significant progress over the last 5 years. We have successfully demonstrated the feasibility of a number of probabilistic models for relational data, including probabilistic relational models, Bayesian logic programs, and relational probability trees, and the interest in SRL is growing. However, in order to sustain and nurture the growth of SRL as a subfield we need to refocus our efforts on the science of machine learning—moving from demonstrations to comparative and ablation studies.

Learning graphical models for relational data via lattice search

Machine Learning, 2012

Many machine learning applications that involve relational databases incorporate first-order logic and probability. Relational extensions of graphical models include Parametrized Bayes Net (Poole in IJCAI, pp. 985-991, 2003), Probabilistic Relational Models (Getoor et al. in Introduction to statistical relational learning, pp. 129-173, 2007), and Markov Logic Networks (MLNs) (Domingos and Richardson in Introduction to statistical relational learning, 2007). Many of the current state-of-the-art algorithms for learning MLNs have focused on relatively small datasets with few descriptive attributes, where predicates are mostly binary and the main task is usually prediction of links between entities. This paper addresses what is in a sense a complementary problem: learning the structure of a graphical model that models the distribution of discrete descriptive attributes given the links between entities in a relational database. Descriptive attributes are usually nonbinary and can be very informative, but they increase the search space of possible candidate clauses. We present an efficient new algorithm for learning a Parametrized Bayes Net that performs a level-wise search through the table join lattice for relational dependencies. From the Bayes net we obtain an MLN structure via a standard moralization procedure for converting directed models to undirected models. Learning MLN structure by moralization is 200-1000 times faster and scores substantially higher in predictive accuracy than benchmark MLN algorithms on five relational databases.

A Comparison between Two Statistical Relational Models

Lecture Notes in Computer Science

Statistical Relational Learning has received much attention this last decade. In the ILP community, several models have emerged for modelling and learning uncertain knowledge, expressed in subset of first order logics. Nevertheless, no deep comparisons have been made among them and, given an application, determining which model must be chosen is difficult. In this paper, we compare two of them, namely Markov Logic Networks and Bayesian Programs, especially with respect to their representation ability and inference methods. The comparison shows that the two models are substantially different, from the point of view of the user, so that choosing one really means choosing a different philosophy to look at the problem. In order to make the comparison more concrete, we have used a running example, which shows most of the interesting points of the approaches, yet remaining exactly tractable.