A Comparison of Personal Name Matching: Techniques and Practical Issues

By Peter Christen. Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06), 2006

382 citations recorded by the source corpus.

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, deduplication and data linkage systems. Variations and errors in names make exact string matching problematic, and approximate 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

Subfields: Artificial Intelligence, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/654d51abeb59861dde5f8097127a5b5a12147f9f

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In Artificial Intelligence

In Management Science and Operations Research

Citations in the corpus

A Survey of Indexing Techniques for Scalable Record Linkage and Deduplication (2012, methodology): “7, the normalized edit-distance string measure [35], and a minimum similarity of t 1⁄4 0:85, then the following suffix string pairs and their corresponding record identifier lists will be merged into one block each: “atherina” and “atherine” (with similarity 0.”
Febrl: a freely available record linkage system with a graphical user interface (2008, methodology): “These functions can be as simple as an exact string or a numerical comparison, can take variations and typographical errors into account (Cohen et al. 2003, Christen 2006), or can be as complex as a distance comparison based on look-up tables of geographic locations (longitudes and latitudes).”
Entity Linking with a Knowledge Base: Issues, Techniques, and Solutions (2015, background): “The ratio of the recursively longest common subsequence [98] to the shorter among the entity mention and the candidate entity name.”
Data Matching (2012, methodology): “Such phonetic encoding algorithms, like for example Soundex [57], are commonly used in the indexing step to ensure that records are inserted into the same blocks even if they have some typographical variations in the value of their blocking criteria.”
Privacy-Preserving Data Linkage and Geocoding: Current Approaches and Research Directions (2006, methodology): “It allows for approximate matching of values with typographical errors by calculating the Dice co-efficient [ 13 ] similarity measure between hash-encoded sets of q-grams.”
Entity Disambiguation for Knowledge Base Population (2010, methodology): “included the ratio of the recursive longest common subsequence (Christen, 2006) to the shorter of the mention or entry name, which is effective at handling some deletions or word reorderings (e.”
Entity Linking: Finding Extracted Entities in a Knowledge Base (2013, positive): “We also included the ratio of the recursive longest common subsequence [8] to the shorter of the mention or entry name, which is effective at handling some deletions or word reorderings (e.”
Evaluation of entity resolution approaches on real-world match problems (2010, background): “The authors of [8] present a comparison of FEBRL's string similarity functions for personal name data.”
A taxonomy of privacy-preserving record linkage techniques (2013, methodology): “This work uses a Soundex [5] based fake injection strategy for private blocking, and a modiﬁed version of the Levenshtein edit distance for performing approximate matching .”