Topic
Approximate string matching
About: Approximate string matching is a research topic. Over the lifetime, 1903 publications have been published within this topic receiving 62352 citations. The topic is also known as: fuzzy string-searching algorithm & fuzzy string-matching algorithm.
Papers published on a yearly basis
Papers
More filters
TL;DR: Two very simple algorithms for the Longest Common Extension problem are given that require no preprocessing and are 5 times faster than the best previous algorithms on the average whereas the second is faster on virtually all inputs.
48 citations
02 Nov 2009
TL;DR: Experimental results on a real-world database indicate that the total size of all data structures of this novel index approach grows sub-linearly with the size of the database, and that it allows matching of query records in sub-second time, more than two orders of magnitude faster than a traditional entity resolution index approach.
Abstract: Entity resolution, also known as data matching or record linkage, is the task of identifying and matching records from several databases that refer to the same entities. Traditionally, entity resolution has been applied in batch-mode and on static databases. However, many organisations are increasingly faced with the challenge of having large databases containing entities that need to be matched in real-time with a stream of query records also containing entities, such that the best matching records are retrieved. Example applications include online law enforcement and national security databases, public health surveillance and emergency response systems, financial verification systems, online retail stores, eGovernment services, and digital libraries. A novel inverted index based approach for real-time entity resolution is presented in this paper. At build time, similarities between attribute values are computed and stored to support the fast matching of records at query time. The presented approach differs from other approaches to approximate query matching in that it allows any similarity comparison function, and any 'blocking' (encoding) function, both possibly domain specific, to be incorporated. Experimental results on a real-world database indicate that the total size of all data structures of this novel index approach grows sub-linearly with the size of the database, and that it allows matching of query records in sub-second time, more than two orders of magnitude faster than a traditional entity resolution index approach. The interested reader is referred to the longer version of this paper [5].
48 citations
TL;DR: This paper defines the reduction of s-gram profiles to binary profiles in order to precisely define the (extended) Jaccard similarity function for s- grams, and shows that n-gram similarity/distance computations are special cases of the generalized definitions.
Abstract: n-grams have been used widely and successfully for approximate string matching in many areas. s-grams have been introduced recently as an n-gram based matching technique, where di-grams are formed of both adjacent and non-adjacent characters. s-grams have proved successful in approximate string matching across language boundaries in Information Retrieval (IR). s-grams however lack precise definitions. Also their similarity comparison lacks precise definition. In this paper, we give precise definitions for both. Our definitions are developed in a bottom-up manner, only assuming character strings and elementary mathematical concepts. Extending established practices, we provide novel definitions of s-gram profiles and the L"1 distance metric for them. This is a stronger string proximity measure than the popular Jaccard similarity measure because Jaccard is insensitive to the counts of each n-gram in the strings to be compared. However, due to the popularity of Jaccard in IR experiments, we define the reduction of s-gram profiles to binary profiles in order to precisely define the (extended) Jaccard similarity function for s-grams. We also show that n-gram similarity/distance computations are special cases of our generalized definitions.
48 citations
IBM1
TL;DR: This paper describes an algorithm which is composed of an encoding stage and an alignment stage, and shows how to reduce the O(n?) alignment work, for each appearance of the common substring Y in a source string, to O-at the cost of O( n?) encoding work, which is executed only once.
47 citations
TL;DR: This paper presents an algorithm for the Two-Dimensional Dictionary Problem, that of finding each occurrence of a set of two-dimensional patterns in a text.
47 citations