Abstract
The lexical similarity measure is used for calculating the similarities between strings. Existing lexical-based methods usually base on either n-grams or Dice’s approaches. These measures have a good performance and could be extended by adjusting the parameter. However, they do not return reasonable results in some situations where strings are quite similar or the sets of characters are the same but their positions are different. To deal with this problem, our paper presents an approach to improve a lexical-based measure based on both information-theoretic and edit distance measures. The proposed method is tested on a partial OAEI benchmark 2008. The results show that our approach has some prominent features compared to other lexical-based methods. It is also flexible clearly and convenient in implementation. Moreover, we chose a range of good parameters can be applied in different domains.
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Nguyen, T.T.A., Conrad, S. (2015). An Improved String Similarity Measure Based on Combining Information-Theoretic and Edit Distance Methods. In: Fred, A., Dietz, J., Aveiro, D., Liu, K., Filipe, J. (eds) Knowledge Discovery, Knowledge Engineering and Knowledge Management. IC3K 2014. Communications in Computer and Information Science, vol 553. Springer, Cham. https://doi.org/10.1007/978-3-319-25840-9_15
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