Multimedia Tools and Applications

, Volume 78, Issue 21, pp 29853–29866 | Cite as

Locating similar names through locality sensitive hashing and graph theory

  • Fernando Turrado García
  • Luis Javier García VillalbaEmail author
  • Ana Lucila Sandoval Orozco
  • Francisco Damián Aranda Ruiz
  • Andrés Aguirre Juárez
  • Tai-Hoon Kim


Locality Sensitive Hashing is a known technique applied for finding similar texts and it has been applied to plagiarism detection, mirror pages identification or to identify the original source of a news article. In this paper we will show how can Locality Sensitive Hashing be applied to identify misspelled people names (name, middle name and last name) or near duplicates. In our case, and due to the short length of the texts, using two similarity functions (the Jaccard Similarity and the Full Damerau-Levenshtein Distance) for measuring the similarity of the names allowed us to obtain better results than using a single one. All the experimental work was made using the statistical software R and the libraries: textreuse and stringdist.


Information retrieval Locality sensitive hashing Entity deduplication Textual similarity 



This research work was supported by Sungshin Women’s University. In addition, L.J.G.V. and A.L.S.O thanks to RAMSES project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 700326. Website:


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Fernando Turrado García
    • 1
  • Luis Javier García Villalba
    • 1
    Email author
  • Ana Lucila Sandoval Orozco
    • 1
  • Francisco Damián Aranda Ruiz
    • 1
  • Andrés Aguirre Juárez
    • 1
  • Tai-Hoon Kim
    • 2
  1. 1.Group of Analysis, Security and Systems (GASS), Department of Software Engineering and Artificial Intelligence (DISIA), Faculty of Information Technology and Computer Science, Office 431Universidad Complutense de Madrid (UCM)MadridSpain
  2. 2.Department of Convergence SecuritySungshin Women’s UniversitySeoulKorea

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