Finding and matching personal names is at the core of an increasing number of applications: from text and Web mining, search engines, to information extraction, dedupli- cation and data linkage systems Variations and errors in names make exact string matching problematic, and ap- proximate matching techniques have to be applied When compared to general text, however, personal names have different characteristics that need to be considered In this paper we discuss the characteristics of personal names and present potential sources of variations and errors We then overview a comprehensive number of commonly used, as well as some recently developed name matching techniques Experimental comparisons using four large name data sets indicate that there is no clear best matching technique

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A Comparison of Personal Name Matching: Techniques and Practical Issues

Given two strings X and Y over a finite alphabet, the normalized edit distance between X and Y, d(X,Y) is defined as the minimum of W(P)/L(P), where P is an editing path between X and Y, W(P) is the sum of the weights of the elementary edit operations of P, and L(P) is the number of these operations (length of P). It is shown that in general, d(X,Y) cannot be computed by first obtaining the conventional (unnormalized) edit distance between X and Y and then normalizing this value by the length of the corresponding editing path. In order to compute normalized edit distances, an algorithm that can be implemented to work in O(m*n/sup 2/) time and O(n/sup 2/) memory space is proposed, where m and n are the lengths of the strings under consideration, and m>or=n. Experiments in hand-written digit recognition are presented, revealing that the normalized edit distance consistently provides better results than both unnormalized or post-normalized classical edit distances. >

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Computation of normalized edit distance and applications

Phonetic matching is used in applications such as name retrieval, where the spelling of a name is used to identify other strings that are likely to be of similar pronunciation. In this paper we explain the parallels between information retrieval and phonetic matching, and describe our new phonetic matching techniques. Our experimental comparison with existing techniques such as Soundex and edit distances, which is based on recall and precision, demonstrates that the new techniques are superior. In addition, reasoning from the similarity of phonetic matching and information retrieval, we have applied combination of evidence to phonetic matching. Our experiments with combining demonstrate that it leads to substantial improvements in effectiveness.

Phonetic string matching: lessons from information retrieval

With the growth in digital representations of music, and of music stored in these representations, it is increasingly attractive to search collections of music. One mode of search is by similarity, but, for music, similarity search presents several difficulties: in particular, for melodic query support, deciding what part of the music is likely to be perceived as the theme by a listener, and deciding whether two pieces of music with different sequences of notes represent the same theme. In this paper we propose a three-stage framework for matching pieces of music. We use the framework to compare a range of techniques for determining whether two pieces of music are similar, by experimentally testing their ability to retrieve different transcriptions of the same piece of music from a large collection of MIDI files. These experiments show that different comparison techniques differ widely in their effectiveness; and that, by instantiating the framework with appropriate music manipulation and comparison techniques, pieces of music that match a query can be identified in a large collection.

Melodic matching techniques for large music databases

Schema evolution is a problem that is faced by long-lived data. When a schema changes, existing persistent data can become inaccessible unless the database system provides mechanisms to access data created with previous versions of the schema. Most existing systems that support schema evolution focus on changes local to individual types within the schema, thereby limiting the changes that the database maintainer can perform. We have developed a model of type changes involving multiple types. The model describes both type changes and their impact on data by defining derivation rules to initialize new data based on the existing data. The derivation rules can describe local and nonlocal changes to types to capture the intent of a large class of type change operations. We have built a system called Tess (Type Evolution Software System) that uses this model to recognize type changes by comparing schemas and then produces a transformer that can update data in a database to correspond to a newer version of the schema.

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Graham A Stephen

Papers

Approximate String Matching