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 focus in machine learning research has been to extend traditional machine learning classification techniques to classify nodes in such data. In this report, we attempt to provide a brief introduction to this area of research and how it has progressed during the past decade. We introduce four of the most widely used inference algorithms for classifying networked data and empirically compare them on both synthetic and real-world data.

Many databases contain imprecise references to real-world entities. For example, a social-network database records names of people. But different people can go by the same name and there may be different observed names referring to the same person. The goal of entity resolution is to determine the mapping from database references to discovered real-world entities.Traditional entity resolution approaches consider approximate matches between attributes of individual references, but this does not always work well. In many domains, such as social networks and academic circles, the underlying entities exhibit strong ties to each other, and as a result, their references often co-occur in the data. In this dissertation, I focus on the use of such co-occurrence relationships for jointly resolving entities. I refer to this problem as 'collective entity resolution'.First, I propose a relational clustering algorithm for iteratively discovering entities by clustering references taking into account the clusters of co-occurring references.Next, I propose a probabilistic generative model for collective resolution that finds hidden group structures among the entities and uses the latent groups as evidence for entity resolution. One of my contributions is an efficient unsupervised inference algorithm for this model using Gibbs Sampling techniques that discovers the most likely number of entities. Both of these approaches improve performance over attribute-only baselines in multiple real world and synthetic datasets. I also perform a theoretical analysis of how the structural properties of the data affect collective entity resolution and verify the predicted trends experimentally. In addition, I motivate the problem of query-time entity resolution. I propose an adaptive algorithm that uses collective resolution for answering queries by recursively exploring and resolving related references. This enables resolution at query-time, while preserving the performance benefits of collective resolution. Finally, as an application of entity resolution in the domain of natural language processing, I study the sense disambiguation problem and propose models for collective sense disambiguation using multiple languages that outperform other unsupervised approaches.

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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 arises in cost-based query optimization, query profiling, and approximate query answering. In this paper, we show how probabilistic graphical models can be effectively used for this task as an accurate and compact approximation of the joint frequency distribution of multiple attributes across multiple relations. Probabilistic Relational Models (PRMs) are a recent development that extends graphical statistical models such as Bayesian Networks to relational domains. They represent the statistical dependencies between attributes within a table, and between attributes across foreign-key joins. We provide an efficient algorithm for constructing a PRM from a database, and show how a PRM can be used to compute selectivity estimates for a broad class of queries. One of the major contributions of this work is a unified framework for the estimation of queries involving both select and foreign-key join operations. Furthermore, our approach is not limited to answering a small set of predetermined queries; a single model can be used to effectively estimate the sizes of a wide collection of potential queries across multiple tables. We present results for our approach on several real-world databases. For both single-table multi-attribute queries and a general class of select-join queries, our approach produces more accurate estimates than standard approaches to selectivity estimation, using comparable space and time.

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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 probabilistic model for collective entity resolution. Our approach differs from other recently proposed entity resolution approaches in that it is a) unsupervised, b) generative and c) introduces a hidden 'group' variable to capture collections of entities which are commonly observed together. The entity resolution decisions are not considered on an independent pairwise basis, but instead decisions are made collectively. We focus on how the use of relational links among the references can be exploited. We show how we can use Gibbs Sampling to infer the collaboration groups and the entities jointly from the observed co-author relationships among entity references and how this improves entity resolution performance. We demonstrate the utility of our approach on two real-world bibliographic datasets. In addition, we present preliminary results on characterizing conditions under which collaborative information is useful.

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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 features that underlies these predictions, and which we have found capable of providing high classification accuracy on human splice sites. Feature selection is optimized for human splice sites, but the selected features are likely to be predictive for other mammals as well. With our interactive feature browsing and visualization tool, the user can view and explore subsets of features used in splice-site prediction (either the features that account for the classification of a specific input sequence or the complete collection of features). Selected feature sets can be searched, ranked or displayed easily. The user can group features into clusters and frequency plot WebLogos can be generated for each cluster. The user can browse the identified clusters and their contributing elements, looking for new interesting signals, or can validate previously observed signals. The SplicePort web server can be accessed at http://www.cs.umd.edu/projects/SplicePort and http://www.spliceport.org.