Using an open-source, Java toolkit of name-matching methods, we experimentally compare string distance metrics on the task of matching entity names. We investigate a number of different metrics proposed by different communities, including edit-distance metrics, fast heuristic string comparators, token-based distance metrics, and hybrid methods. Overall, the best-performing method is a hybrid scheme combining a TFIDF weighting scheme, which is widely used in information retrieval, with the Jaro-Winkler string-distance scheme, which was developed in the probabilistic record linkage community.

We describe an open-source Java toolkit of methods for matching names and records. We summarize results obtained from using various string distance metrics on the task of matching entity names. These metrics include distance functions proposed by several different communities, such as edit-distance metrics, fast heuristic string comparators, token-based distance metrics, and hybrid methods. We then describe an extension to the toolkit which allows records to be compared. We discuss

Abstract. Ontologies are today a key part of every knowledge based system. They provide a source of shared and precisely defined terms, resulting in system interoperability by knowledge sharing and reuse. Unfortunately, the variety of ways that a domain can be conceptualized results in the creation of different ontologies with contradicting or overlapping parts. For this reason ontologies need to be brought into mutual agreement (aligned). One important method for ontology alignment is the comparison of class and property names of ontologies using stringdistance metrics. Today quite a lot of such metrics exist in literature. But all of them have been initially developed for different applications and fields, resulting in poor performance when applied in this new domain. In the current paper we present a new string metric for the comparison of names which performs better on the process of ontology alignment as well as to many other field matching problems. 1

Many applications need to solve the following problem of approximate string matching: from a collection of strings, how to find those similar to a given string, or the strings in another (possibly the same) collection of strings? Many algorithms are developed using fixed-length grams, which are substrings of a string used as signatures to identify similar strings. In this paper we develop a novel technique, called VGRAM, to improve the performance of these algorithms. Its main idea is to judiciously choose high-quality grams of variable lengths from a collection of strings to support queries on the collection. We give a full specification of this technique, including how to select high-quality grams from the collection, how to generate variable-length grams for a string based on the preselected grams, and what is the relationship between the similarity of the gram sets of two strings and their edit distance. A primary advantage of the technique is that it can be adopted by a plethora of approximate string algorithms without the need to modify them substantially. We present our extensive experiments on real data sets to evaluate the technique, and show the significant performance improvements on three existing algorithms. 1.

Duplicate detection is the problem of detecting di#erent entries in a data source representing the same real-world entity. While research abounds in the realm of duplicate detection in relational data, there is yet little work for duplicates in other, more complex data models, such as XML. In this paper, we present a generalized framework for duplicate detection, dividing the problem into three components: candidate definition defining which objects are to be compared, duplicate definition defining when two duplicate candidates are in fact duplicates, and duplicate detection specifying how to e#ciently find those duplicates.

Record linkage is the task of quickly and accurately identifying records corresponding to the same entity from one or more data sources. Record linkage is also known as data cleaning, entity reconciliation or identification and the merge/purge problem. This paper presents the "standard" probabilistic record linkage model and the associated algorithm. Recent work in information retrieval, federated database systems and data mining have proposed alternatives to key components of the standard algorithm. The impact of these alternatives on the standard approach are assessed. The key question is whether and how these new alternatives are better in terms of time, accuracy and degree of automation for a particular record linkage application.

In this article, we address the problem of reference disambiguation. Specifically, we consider a situation where entities in the database are referred to using descriptions (e.g., a set of instantiated attributes). The objective of reference disambiguation is to identify the unique entity to which each description corresponds. The key difference between the approach we propose (called RELDC) and the traditional techniques is that RELDC analyzes not only object features but also inter-object relationships to improve the disambiguation quality. Our extensive experiments over two real data sets and over synthetic datasets show that analysis of relationships significantly improves quality of the result.

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We present the system that we submitted to the 3rd Pascal Recognizing Textual Entailment Challenge. It uses four Support Vector Machines, one for each subtask of the challenge, with features that correspond to string similarity measures operating at the lexical and shallow syntactic level. 1

Abstract. The growing expanse of e-commerce and the widespread availability of online databases raise many fears regarding loss of privacy and many statistical challenges. Even with encryption and other nominal forms of protection for individual databases, we still need to protect against the violation of privacy through linkages across multiple databases. These issues parallel those that have arisen and received some attention in the context of homeland security. Following the events of September 11, 2001, there has been heightened attention in the United States and elsewhere to the use of multiple government and private databases for the identification of possible perpetrators of future attacks, as well as an unprecedented expansion of federal government data mining activities, many involving databases containing personal information. We present an overview of some proposals that have surfaced for the search of multiple databases which supposedly do not compromise possible pledges of confidentiality to the individuals whose data are included. We also explore their link to the related literature on privacypreserving data mining. In particular, we focus on the matching problem across databases and the concept of “selective revelation ” and their confidentiality implications. Key words and phrases: Encryption, multiparty computation, privacypreserving data mining, record linkage, R–U confidentiality map, selective revelation. 1.