See the RIDDLE Repository on Identity Uncertainty, Duplicate Detection, and Record Linkage for datasets, bibliography, and more information on this topic.
Many data mining tasks require computing similarity between pairs of objects. Pairwise similarity computations are particularly important in record linkage systems, as well as in clustering and schema mapping algorithms. Because the number of object pairs grows quadratically with the size of the dataset, computing similarity between all pairs is impractical and becomes prohibitive for large datasets and complex similarity functions. Blocking methods alleviate this problem by efficiently selecting approximately similar object pairs for subsequent distance computations, leaving out the remaining pairs as dissimilar. Previously proposed blocking methods require manually constructing an indexbased similarity function or selecting a set of predicates, followed by hand-tuning of parameters. In this paper, we introduce an adaptive framework for automatically learning blocking functions that are efficient and accurate. We describe two predicate-based formulations of learnable blocking functions and provide learning algorithms for training them. The effectiveness of the proposed techniques is demonstrated on real and simulated datasets, on which they prove to be more accurate than non-adaptive blocking methods.
ML ID: 195
Many machine learning and data mining tasks depend on functions that estimate similarity between instances. Similarity computations are particularly important in clustering and information integration applications, where pairwise distances play a central role in many algorithms. Typically, algorithms for these tasks rely on pre-defined similarity measures, such as edit distance or cosine similarity for strings, or Euclidean distance for vector-space data. However, standard distance functions are frequently suboptimal as they do not capture the appropriate notion of similarity for a particular domain, dataset, or application.
In this thesis, we present several approaches for addressing this problem by employing learnable similarity functions. Given supervision in the form of similar or dissimilar pairs of instances, learnable similarity functions can be trained to provide accurate estimates for the domain and task at hand. We study the problem of adapting similarity functions in the context of several tasks: record linkage, clustering, and blocking. For each of these tasks, we present learnable similarity functions and training algorithms that lead to improved performance.
In record linkage, also known as duplicate detection and entity matching, the goal is to identify database records referring to the same underlying entity. This requires estimating similarity between corresponding field values of records, as well as overall similarity between records. For computing field-level similarity between strings, we describe two learnable variants of edit distance that lead to improvements in linkage accuracy. For learning record-level similarity functions, we employ Support Vector Machines to combine similarities of individual record fields in proportion to their relative importance, yielding a high-accuracy linkage system. We also investigate strategies for efficient collection of training data which can be scarce due to the pairwise nature of the record linkage task.
In clustering, similarity functions are essential as they determine the grouping of instances that is the goal of clustering. We describe a framework for integrating learnable similarity functions within a probabilistic model for semi-supervised clustering based on Hidden Markov Random Fields (HMRFs). The framework accommodates learning various distance measures, including those based on Bregman divergences (e.g., parameterized Mahalanobis distance and parameterized KL-divergence), as well as directional measures (e.g., cosine similarity). Thus, it is applicable to a wide range of domains and data representations. Similarity functions are learned within the HMRF-KMeans algorithm derived from the framework, leading to significant improvements in clustering accuracy.
The third application we consider, blocking, is critical in making record linkage and clustering algorithms scalable to large datasets, as it facilitates efficient selection of approximately similar instance pairs without explicitly considering all possible pairs. Previously proposed blocking methods require manually constructing a similarity function or a set of similarity predicates, followed by hand-tuning of parameters. We propose learning blocking functions automatically from linkage and semi-supervised clustering supervision, which allows automatic construction of blocking methods that are efficient and accurate. This approach yields computationally cheap learnable similarity functions that can be used for scaling up in a variety of tasks that rely on pairwise distance computations, including record linkage and clustering.
ML ID: 193
The problem of record linkage focuses on determining whether two object descriptions refer to the same underlying entity. Addressing this problem effectively has many practical applications, e.g., elimination of duplicate records in databases and citation matching for scholarly articles. In this paper, we consider a new domain where the record linkage problem is manifested: Internet comparison shopping. We address the resulting linkage setting that requires learning a similarity function between record pairs from streaming data. The learned similarity function is subsequently used in clustering to determine which records are co-referent and should be linked. We present an online machine learning method for addressing this problem, where a composite similarity function based on a linear combination of basis functions is learned incrementally. We illustrate the efficacy of this approach on several real-world datasets from an Internet comparison shopping site, and show that our method is able to effectively learn various distance functions for product data with differing characteristics. We also provide experimental results that show the importance of considering multiple performance measures in record linkage evaluation.
ML ID: 178
Several problems central to information integration, such as ontology mapping and object matching, can be viewed as alignment tasks where the goal is to find an optimal correspondence between two structured objects and to compute the associated similarity score. The diversity of data sources and domains in the Semantic Web requires solutions to these problems to be highly adaptive, which can be achieved by employing probabilistic machine learning approaches. We present one such approach, Alignment Conditional Random Fields (ACRFs), a new framework for constructing and scoring sequence alignments using undirected graphical models. ACRFs allow incorporating arbitrary features into string edit distance computation, yielding a learnable string similarity function for use in tasks where approximate string matching is needed. We outline possible applications of ACRFs in information integration tasks and describe directions for future work.
ML ID: 177
ML ID: 148
Many machine learning tasks require similarity functions that estimate likeness between observations. Similarity computations are particularly important for clustering and record linkage algorithms that depend on accurate estimates of the distance between datapoints. However, standard measures such as string edit distance and Euclidean distance often fail to capture an appropriate notion of similarity for a particular domain or dataset. This problem can be alleviated by employing learnable similarity functions that adapt using training data. In this proposal, we introduce two adaptive string similarity measures: (1) Learnable Edit Distance with Affine Gaps, and (2) Learnable Vector-Space Similarity Based on Pairwise Classification. These similarity functions can be trained using a corpus of labeled pairs of equivalent and non-equivalent strings. We illustrate the accuracy improvements obtained with these measures using MARLIN, our system for record linkage in databases that learns to combine adaptive and static string similarity functions in a two-level learning framework.
Obtaining useful training examples for learnable similarity functions can be problematic due to scarcity of informative similar and dissimilar object pairs. We propose two strategies, Static-Active Selection and Weakly-Labeled Negatives, that facilitate efficient training data collection for record linkage. These strategies significantly outperform random selection on real datasets without the computational cost of traditional active learning methods. Additionally, we describe a method for combining seeding with Euclidean distance learning for semi-supervised k-means clustering. Experimental evaluation demonstrates that our method outperforms unsupervised clustering and semi-supervised clustering that employs seeding or metric learning separately.
In future research, we intend to pursue several directions in developing accurate learnable similarity functions and applying them to record linkage and clustering problems. This work will involve improving the proposed string similarity functions as well as introducing several novel approaches to adaptive string distance computation. We also plan to extend our initial work on learnable similarity functions for clustering, particularly for high-dimensional data. Finally, we will investigate the utility of various active learning strategies for learning similarity functions, as well as extend the preliminary work on static-active selection of training pairs.
ML ID: 133
Identifying approximately duplicate database records that refer to the same entity is essential for information integration. The authors compare and describe methods for combining and learning textual similarity measures for name matching.
ML ID: 131
A variety of experimental methodologies have been used to evaluate the accuracy of duplicate-detection systems. We advocate presenting precision-recall curves as the most informative evaluation methodology. We also discuss a number of issues that arise when evaluating and assembling training data for adaptive systems that use machine learning to tune themselves to specific applications. We consider several different application scenarios and experimentally examine the effectiveness of alternative methods of collecting training data under each scenario. We propose two new approaches to collecting training data called static-active learning and weakly-labeled non-duplicates, and present experimental results on their effectiveness.
ML ID: 129
The problem of identifying approximately duplicate records in databases is an essential step for data cleaning and data integration processes. Most existing approaches have relied on generic or manually tuned distance metrics for estimating the similarity of potential duplicates. In this paper, we present a framework for improving duplicate detection using trainable measures of textual similarity. We propose to employ learnable text distance functions for each database field, and show that such measures are capable of adapting to the specific notion of similarity that is appropriate for the field's domain. We present two learnable text similarity measures suitable for this task: an extended variant of learnable string edit distance, and a novel vector-space based measure that employs a Support Vector Machine (SVM) for training. Experimental results on a range of datasets show that our framework can improve duplicate detection accuracy over traditional techniques.
ML ID: 127
The problem of identifying approximately duplicate records in databases is an essential step for the information integration processes. Most existing approaches have relied on generic or manually tuned distance metrics for estimating the similarity of potential duplicates. In this paper, we present a framework for improving duplicate detection using trainable measures of textual similarity. We propose to employ learnable text distance functions for each data field, and introduce an extended variant of learnable string edit distance based on an Expectation-Maximization(EM) training algorithm. Experimental results on a range of datasets show that this similarity metric is capable of adapting to the specific notions of similarity that are appropriate for different domains. Our overall system, MARLIN utilizes support vector machines to combine multiple similarity metrics, which are shown to perform better than ensembles of decision trees, which were employed for this task previously.
ML ID: 123
Variation and noise in textual database entries can prevent text mining algorithms from discovering important regularities. We present two novel methods to cope with this problem: (1) an adaptive approach to ``hardening'' noisy databases by identifying duplicate records, and (2) mining ``soft'' association rules. For identifying approximately duplicate records, we present a domain-independent two-level method for improving duplicate detection accuracy based on machine learning. For mining soft matching rules, we introduce an algorithm that discovers association rules by allowing partial matching of items based on a textual similarity metric such as edit distance or cosine similarity. Experimental results on real and synthetic datasets show that our methods outperform traditional techniques for noisy textual databases.
ML ID: 115
The problem of identifying approximately duplicate records in databases has previously been studied as record linkage, the merge/purge problem, hardening soft databases, and field matching. Most existing approaches have focused on efficient algorithms for locating potential duplicates rather than precise similarity metrics for comparing records. In this paper, we present a domain-independent method for improving duplicate detection accuracy using machine learning. First, trainable distance metrics are learned for each field, adapting to the specific notion of similarity that is appropriate for the field's domain. Second, a classifier is employed that uses several diverse metrics for each field as distance features and classifies pairs of records as duplicates or non-duplicates. We also propose an extended model of learnable string distance which improves over an existing approach. Experimental results on real and synthetic datasets show that our method outperforms traditional techniques.
ML ID: 110