Combination of Lexical and Structure-Based Similarity Measures to Match Ontologies Automatically

Nguyen, Thi Thuy Anh; Conrad, Stefan

doi:10.1007/978-3-642-54105-6_7

Cited by 3 publications

(1 citation statement)

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“…Three types of operations are used including the substitution of a character of the first string by a character of the second string, the deletion or the insertion of a character of one string into other. The total cost of the used operations is equal to the sum of the costs of each of the operations (Nguyen and Conrad, 2013).…”

Section: Levenshtein Distancementioning

confidence: 99%

Applying Information-theoretic and Edit Distance Approaches to Flexibly Measure Lexical Similarity

Nguyen

Conrad

2014

Proceedings of the International Conference on Knowledge Discovery and Information Retrieval

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Measurement of similarity plays an important role in data mining and information retrieval. Several techniques for calculating the similarities between objects have been proposed so far, for example, lexical-based, structure-based and instance-based measures. Existing lexical similarity measures usually base on either ngrams or Dice's approaches to obtain correspondences between strings. Although these measures are efficient, they are inadequate in situations where strings are quite similar or the sets of characters are the same but their positions are different in strings. In this paper, a lexical similarity approach combining information-theoretic model and edit distance to determine correspondences among the concept labels is developed. Precision, Recall and F-measure as well as partial OAEI benchmark 2008 tests are used to evaluate the proposed method. The results show that our approach is flexible and has some prominent features compared to other lexical-based methods.

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Section: Levenshtein Distancementioning

confidence: 99%