Edit distance based string similarity join is a fundamental operator in string databases. Increasingly, many applications in data cleaning, data integration, and scientific computing have to deal with fuzzy information in string attributes. Despite the intensive efforts devoted in processing

'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

Abstract — Similarity Join is an important operation in data integration and cleansing, record linkage, data deduplication and pattern matching. It finds similar sting pairs from two collections of strings. Number of approaches have been proposed as well as compared for string similarity joins

Ontology alignment is an important part of enabling the se-mantic web to reach its full potential. The vast majority of ontology alignment systems use one or more string similarity metrics, but often the choice of which metrics to use is not given much attention. In this work we evaluate a wide

A string similarity measure quantifies the similarity between two text strings for approximate string matching or comparison. For example, the strings “Sam ” and “Samuel ” can be considered similar. Most existing work that computes the similarity of two strings only considers syntactic similarities

String similarity join is an important operation in data integration and cleansing that finds similar string pairs from two collections of strings. More than ten algorithms have been proposed to address this problem in the recent two decades. However, existing algorithms have not been thoroughly

-based discriminative framework for string similarity. We gather features from substring pairs consistent with a character-based alignment of the two strings. This approach achieves exceptional performance; on nine separate cognate identification experiments using six language pairs, we more than double the precision

String similarity metrics are used for several purposes in text-processing. One task is the extraction of cognates from bilingual text. In this paper three approaches to the automatic generation of language dependent string matching functions are presented.

Measuring string similarity is a frequently used technique in various Language Technology (LT) applications, such as: Spell checkers, Translation

This paper proposes a statistical model for defining string similarity. The proposed model is based on hidden Markov model and defines string similarity as the combination of similarities of substrings. The proposed model has an advantage that the similarity is flexibly defined and complex