Collective Classification in Network Data (2008)
Sen, Prithviraj, Namata, Galileo, Bilgic, Mustafa, Getoor, Lise, Gallagher, Brian, Eliassi-Rad, Tina
Numerous real-world applications produce networked data such as web data (hypertext documents connected via hyperlinks) and communication networks (people connected via communication links). A recent...
Collective Classification in Network Data (2008)
Sen, Prithviraj, Namata, Galileo, Bilgic, Mustafa, Getoor, Lise, Gallagher, Brian, Eliassi-Rad, Tina
Numerous real-world applications produce networked data such as web data (hypertext documents connected via hyperlinks) and communication networks (people connected via communication links). A recent...
Features generated for computational splice-site prediction correspond to functional elements (2007)
Dogan, Rezarta, Getoor, Lise, Wilbur, W John, Mount, Stephen M
Abstract Background Accurate selection of splice sites during the splicing of precursors to messenger RNA requires both relatively well-characterized signals at the splice sites and auxiliary signals...
Link-based Classification (2007)
Over the past few years, a number of approximate inference algorithms for networked data have been put forth. We empirically compare the performance of three of the popular algorithms: loopy belief...
Link-based Classification (2007)
Over the past few years, a number of approximate inference algorithms for networked data have been put forth. We empirically compare the performance of three of the popular algorithms: loopy belief...
Predicting Protein-Protein Interactions Using Relational Features (2007)
Proteins play a fundamental role in ever y process within the cell. Understanding how proteins interact, and the functional units they are par t of, is important to furthering our knowledge of the...
Predicting Protein-Protein Interactions Using Relational Features (2007)
Proteins play a fundamental role in ever y process within the cell. Understanding how proteins interact, and the functional units they are par t of, is important to furthering our knowledge of the...
Social Capital in Friendship-Event Networks (2006)
In this paper, we examine a particular form of social network which we call a friendship-event network. A friendship-event network captures both the friendship relationship among a set of actors, and...
Social Capital in Friendship-Event Networks (2006)
In this paper, we examine a particular form of social network which we call a friendship-event network. A friendship-event network captures both the friendship relationship among a set of actors, and...
D-Dupe: An Interactive Tool for Entity Resolution in Social Networks (2006)
Bilgic, Mustafa, Licamele, Louis, Getoor, Lise, Shneidermann, Ben
Graphs describing real world data often contain duplicate entries for names, cities, or other entities. This paper presents D-Dupe, an interactive visualization tool designed to help users to...
D-Dupe: An Interactive Tool for Entity Resolution in Social Networks (2006)
Bilgic, Mustafa, Licamele, Louis, Getoor, Lise, Shneidermann, Ben
Graphs describing real world data often contain duplicate entries for names, cities, or other entities. This paper presents D-Dupe, an interactive visualization tool designed to help users to...
D-Dupe: An Interactive Tool for Entity Resolution in Social Networks (2006)
Bilgic, Mustafa, Licamele, Louis, Getoor, Lise, Shneidermann, Ben
Graphs describing real world data often contain duplicate entries for names, cities, or other entities. This paper presents D-Dupe, an interactive visualization tool designed to help users to...
Entity Resolution In Graphs (2005)
Bhattacharya, Indrajit, Getoor, Lise
The goal of entity resolution is to reconcile data references corresponding to the same real world entity. Here we introduce the problem of entity resolution in graphs, where the nodes are the...
Entity Resolution In Graphs (2005)
Bhattacharya, Indrajit, Getoor, Lise
The goal of entity resolution is to reconcile data references corresponding to the same real world entity. Here we introduce the problem of entity resolution in graphs, where the nodes are the...
A Latent Dirichlet Model for Unsupervised Entity Resolution (2005)
Bhattacharya, Indrajit, Getoor, Lise
In this paper, we address the problem of entity resolution, where given many references to underlying objects, the task is to predict which references correspond to the same object. We propose a...
A Latent Dirichlet Model for Unsupervised Entity Resolution (2005)
Bhattacharya, Indrajit, Getoor, Lise
In this paper, we address the problem of entity resolution, where given many references to underlying objects, the task is to predict which references correspond to the same object. We propose a...
Extracting sentiments from unstructured text has emerged as an important problem in many disciplines. An accurate method would enable us, for example, to mine on-line opinions from the Internet and...
Learning Probabilistic Models of Relational Structure (2003)
Lise Getoor, Nir Friedman, Benjamin Taskar
Most real-world data is stored in relational form. In contrast, most statistical learning methods work with "flat" data representations, forcing us to convert our data into a form that loses much of...
Learning Probabilistic Models of Link Structure (2002)
Lise Getoor, Nit Friedman, Daphne Koller, Ben Taskar
Most real-world data is heterogeneous and richly interconnected. Examples include the Web, hypertext, bibliometric data and social networks. In contrast, most statistical learning methods work with...
Learning Probabilistic Models of Relational Structure (2002)
Lise Getoor, Nir Friedman, Benjamin Taskar
Most real-world data is stored in relational form. In contrast, most statistical learning methods work with "flat" data representations, forcing us to convert our data into a form that loses much of...
Selectivity Estimation using Probabilistic Models (2001)
Lise Getoor, Ben Taskar, Daphne Koller
Estimating the result size of complex queries that involve selection on multiple attributes and the join of several relations is a difficult but fundamental task in database query processing. It...
Probabilistic Models of Text and Link Structure for Hypertext Classification (2001)
Lise Getoor, Eran Segal, Ben Taskar, Daphne Koller
Most text classification methods treat each document as an independent instance. However, in many text domains, documents are linked and the topics of linked documents are correlated.
Link: Planning for Image Processing Tasks (2001)
Amy L. Lansky, Mark Friedman, Lise Getoor, Scott Schmidler, Nick Short
This paper describes the application of the Collage planner to the task of generating image processing plans for satellite remote sensing data. In particular, we focus on the linkage of Collage to...
Probabilistic Models of Text and Link Structure for Hypertext Classification (2001)
Lise Getoor, Eran Segal, Ben Taskar, Daphne Koller
Most text classification methods treat each document as an independent instance. However, in many text domains, documents are linked and the topics of linked documents are correlated. For example,...
Learning Probabilistic Models of Relational Structure (2001)
Lise Getoor, Nit Friedman, Daphne Koller, Benjamin Taskar
Most real-world data is stored in relational form. In contrast, most statistical learning methods work with "flat" data representations, forcing us to convert our data into a form that loses much of...
Selectivity Estimation using Probabilistic Models (2001)
Lise Getoor, Ben Taskar, Daphne Koller
Estimating the result size of complex queries that involve selection on multiple attributes and the join of several relations is a difficult but fundamental task in database query processing. It...
Learning Probabilistic Relational Models with Structural Uncertainty (2000)
Lise Getoor, Daphne Koller, Benjamin Taskar, Nir Friedman
Most real-world data is stored in relational form. In contrast, most statistical learning methods, e.g., Bayesian network learning, work only with "flat" data representations, forcing us to convert...
Learning Probabilistic Relational Models (2000)
Nir Friedman, Lise Getoor, Daphne Koller, Avi Pfeffer
A large portion of real-world data is stored in commercial relational database systems. In contrast, most statistical learning methods work only with "flat" data representations. Thus, to apply these...
From Instances to Classes in Probabilistic Relational Models (2000)
Lise Getoor, Daphne Koller, Nir Friedman
Probabilistic graphical models, in particular Bayesian networks, are useful models for representing statistical patterns in propositional domains. Recent work develops effective techniques for...
Learning Probabilistic Relational Models (1999)
Nir Friedman, Lise Getoor, Daphne Koller, Avi Pfeffer
A large portion of real-world data is stored in commercial relational database systems. In contrast, most statistical learning methods work only with "flat" data representations. Thus, to apply these...
Using Probabilistic Relational Models for Collaborative Filtering (1999)
Recent projects in collaborative filtering and information filtering address the task of inferring user preference relationships for products or information. The data on which these inferences are...
Learning Probabilistic Relational Models (1999)
Nir Friedman, Lise Getoor, Daphne Koller, Avi Pfeffer
A large portion of real-world data is stored in commercial relational database systems. In contrast, most statistical learning methods work only with "flat" data representations. Thus, to apply these...
Learning Probabilistic Relational Models (1999)
Nir Friedman, Lise Getoor, Daphne Koller, Avi Pfeffer
A large portion of real-world data is stored in commercial relational database systems. In contrast, most statistical learning methods work only with "flat" data representations. Thus, to apply these...
Learning Probabilistic Relational Models (1999)
Nir Friedman, Lise Getoor, Daphne Koller, Avi Pfeffer
A large portion of real-world data is stored in commercial relational database systems. In contrast, most statistical learning methods work only with "flat" data representations. Thus, to apply these...
Efficient Learning using Constrained Sufficient Statistics (1999)
Learning Bayesian networks is a central problem for pattern recognition, density estimation and classification. In this paper, we propose a new method for speeding up the computational process of...
Efficient Learning using Constrained Sufficient Statistics (1999)
Learning Bayesian networks is a central problem for pattern recognition, density estimation and classification. In this paper, we propose a new method for speeding up the computational process of...
Online Scheduling for Reprographic Machines (1998)
We present a real-world online scheduling application. In this application, the problem...
Online Scheduling for Reprographic Machines (1998)
this paper, we assume that the machine has one input and one output (Figure 1). In between, sheets are continuously moving, and once a sheet is fed into the machine, its itinerary through the machine...
Online Scheduling for Reprographic Machines (1998)
this paper, we assume that the machine has one input and one output (Figure 1). In between, sheets are continuously moving, and once a sheet is fed into the machine, its itinerary through the machine...
Efficient Learning using Constrained Sufficient Statistics (1998)
Learning Bayesian networks is a central problem for pattern recognition, density estimation and classification. In this paper, we propose a new method for speeding up the computational process of...
Efficient Learning using Constrained Sufficient Statistics (1998)
Learning Bayesian networks is a central problem for pattern recognition, density estimation and classification. In this paper, we propose a new method for speeding up the computational process of...
Utility Elicitation as a Classification Problem (1998)
Urszula Chajewska, Lise Getoor, Joseph Norman, Yuval Shahar
We investigate the application of classification techniques to utility elicitation. In a decision problem, two sets of parameters must generally be elicited: the probabilities and the utilities....
Effective Redundant Constraints for Online Scheduling (1998)
Lise Getoor, Greger Ottosson, Markus Fromherz, Bjorn Carlson
The use of heuristics as a means to improve constraint solver performance has been researched widely. However, most work has been on problem-independent heuristics (e.g., variable and value...
Effective Redundant Constraints for Online Scheduling (1998)
Lise Getoor, Greger Ottosson, Markus Fromherz, Bjorn Carlson
The use of heuristics as a means to improve constraint solver performance has been researched widely. However, most work has been on problem-independent heuristics (e.g., variable and value...
Utility Elicitation as a Classification Problem (1998)
Urszula Chajewska, Lise Getoor, Joseph Norman, Yuval Shahar
We investigate the application of classification techniques to utility elicitation. In a decision problem, two sets of parameters must generally be elicited: the probabilities and the utilities....
Utility Elicitation as a Classification Problem (1998)
Urszula Chajewska, Lise Getoor, Joseph Norman, Yuval Shahar
We investigate the application of classification techniques to utility elicitation. In a decision problem, two sets of parameters must generally be elicited: the probabilities and the utilities....
Utility Elicitation as a Classification Problem (1998)
Urszula Chajewska, Lise Getoor, Joseph Norman, Yuval Shahar
We investigate the application of classification techniques to utility elicitation. In a decision problem, two sets of parameters must generally be elicited: the probabilities and the utilities....
Utility Elicitation as a Classification Problem (1998)
Urszula Chajewska, Lise Getoor, Joseph Norman, Yuval Shahar
We investigate the application of classification techniques to utility elicitation. In a decision problem, two sets of parameters must generally be elicited: the probabilities and the utilities....
Effective Redundant Constraints for Online Scheduling (1997)
Lise Getoor, Greger Ottosson, Markus Fromherz, Bjorn Carlson
The use of heuristics as a means to improve constraint solver performance has been researched widely. However, most work has been on problem-independent heuristics (e.g., variable and value...
Effective Redundant Constraints for Online Scheduling (1997)
Lise Getoor, Greger Ottosson, Markus Fromherz, Bjorn Carlson
The use of heuristics as a means to improve constraint solver performance has been researched widely. However, most work has been on problem-independent heuristics (e.g., variable and value...
The Collage/Khoros Link: Planning for Image Processing Tasks (1996)
Amy L. Lansky, Mark Friedman, Lise Getoor, Scott Schmidler, Nick Short
This paper describes the application of the Collage planner to the task of generating image processing plans for satellite remote sensing data. In particular, we focus on the linkage of Collage to...
Amy L. Lansky, Mark Friedman, Lise Getoor, Scott Schmidler, Nick Short
This paper describes the application of the Collage planner to the task of generating image processing plans for satellite remote sensing data. In particular, we focus on the linkage of Collage to...
Urszula Chajewska, Lise Getoor
The majority of real-world probabilistic systems are used by more than one user, thus a utility model must be elicited separately for each new user. Utility elicitation is long and tedious,...
SplicePort—An interactive splice-site analysis tool
Dogan, Rezarta Islamaj, Getoor, Lise, Wilbur, W. John, Mount, Stephen M.
SplicePort is a web-based tool for splice-site analysis that allows the user to make splice-site predictions for submitted sequences. In addition, the user can also browse the rich catalog of...