Advertisement

Knowledge and Information Systems

, Volume 58, Issue 2, pp 275–294 | Cite as

An improved method of locality-sensitive hashing for scalable instance matching

  • Mehmet AydarEmail author
  • Serkan Ayvaz
Regular Paper

Abstract

In this study, we propose a scalable approach for automatically identifying similar candidate instance pairs in very large datasets. Efficient candidate pair generation is an essential to many computational problems involving calculation of instance similarities. Calculating similarities of instances with a large number of properties and efficiently matching a large number of similar instances in a scalable way are two significant bottlenecks of candidate instance pair generation. In our approach, we utilize locality-sensitive hashing (LSH) technique to greatly improve the scalability of candidate instance pair generation. Based on the candidate similarity threshold, our algorithm automatically discovers the optimum number of hash functions in each band in LSH. Moreover, we evaluated the scalability of our approach and its effectiveness in instance matching task using real-world very large datasets.

Keywords

Scalability Locality-sensitive hashing Instance Matching Instance Similarity Candidate Pairs Generation 

Notes

Acknowledgements

We would like to thank the OAEI 2016 campaign Instance Matching Task organizers, particularly Dr. Manel Achichi, Dr. Daniel Faria and Dr. Ernesto Jimnez-Ruiz, for providing run time evaluations. Also, we thank Dr. Daniel Faria for providing AML’s OAEI 2016 version as a stand-alone JAR for testing purposes.

References

  1. 1.
    Achichi M, Cheatham M, Dragisic Z, Euzenat J, Faria D, Ferrara A, Flouris G, Fundulaki I, Harrow I, Ivanova V, et al. (2016) Results of the ontology alignment evaluation initiative 2016. In: CEUR workshop proceedings vol 1766. RWTH, pp 73–129Google Scholar
  2. 2.
    Aumueller D, Do H-H, Massmann S, Rahm E ( 2005) Schema and ontology matching with coma++. In: Proceedings of the 2005 ACM SIGMOD international conference on Management of data. Acm, pp 906–908Google Scholar
  3. 3.
    Aydar M, Ayvaz S, Melton AC (2015) Automatic weight generation and class predicate stability in rdf summary graphs. In: Workshop on intelligent exploration of semantic data (IESD2015), co-located with ISWC2015’Google Scholar
  4. 4.
    Ayvaz S, Aydar M, Melton A (2015) Building summary graphs of RDF data in semantic web. In: Computer software and applications conference (COMPSAC), 2015 IEEE 39th annual’, vol 2. pp 686–691Google Scholar
  5. 5.
    Berlin J, Motro A (2002) Database schema matching using machine learning with feature selection. In: International conference on advanced information systems engineering. Springer, pp 452–466Google Scholar
  6. 6.
    Bilenko M, Mooney R, Cohen W, Ravikumar P, Fienberg S (2003) Adaptive name matching in information integration. IEEE Intell Syst 18(5):16–23CrossRefGoogle Scholar
  7. 7.
    Bilke A, Naumann F (2005) Schema matching using duplicates. In: Data engineering, 2005. ICDE 2005. Proceedings. 21st international conference on’. IEEE, pp 69–80Google Scholar
  8. 8.
    Bizer C, Heath T, Berners-Lee T (2009) Linked data-the story so far. Int J Semant Web Inf Syst 5(3):1–22CrossRefGoogle Scholar
  9. 9.
    Broder AZ (1997) On the resemblance and containment of documents. In: Compression and complexity of sequences 1997. Proceedings. IEEE, pp 21–29Google Scholar
  10. 10.
    Castano S, Ferrara A, Montanelli S, Lorusso D (2008) Instance matching for ontology population. In: SEBD. pp 121–132Google Scholar
  11. 11.
    Charikar MS (2002) Similarity estimation techniques from rounding algorithms. In: Proceedings of the thirty-fourth annual ACM symposium on theory of computing. ACM, pp 380–388Google Scholar
  12. 12.
    Chierichetti F, Kumar R (2015) Lsh-preserving functions and their applications. J ACM (JACM) 62(5):33MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Chierichetti F, Kumar R, Mahdian M (2014) The complexity of lsh feasibility. Theor Comput Sci 530:89–101MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Chum O, Philbin J, Zisserman A et al (2008) Near duplicate image detection: min-hash and tf-idf weighting. In: BMVC, vol 810. pp 812–815Google Scholar
  15. 15.
    Cochinwala M, Kurien V, Lalk G, Shasha D (2001) Efficient data reconciliation. Inf Sci 137(1):1–15CrossRefzbMATHGoogle Scholar
  16. 16.
    Cohen E, Datar M, Fujiwara S, Gionis A, Indyk P, Motwani R, Ullman JD, Yang C (2001) Finding interesting associations without support pruning. IEEE Trans Knowl Data Eng 13(1):64–78CrossRefGoogle Scholar
  17. 17.
    Das AS, Datar M, Garg A, Rajaram S (2007) Google news personalization: scalable online collaborative filtering. In: Proceedings of the 16th international conference on World Wide Web. ACM, pp 271–280Google Scholar
  18. 18.
    Dean J, Ghemawat S (2008) Mapreduce: simplified data processing on large clusters. Commun ACM 51(1):107–113CrossRefGoogle Scholar
  19. 19.
    Doan A, Madhavan J, Domingos P, Halevy A (2004) Ontology matching: a machine learning approach. In: Handbook on ontologies. Springer, pp 385–403Google Scholar
  20. 20.
    Duan S, Fokoue A, Hassanzadeh O, Kementsietsidis A, Srinivas K, Ward MJ (2012) Instance-based matching of large ontologies using locality-sensitive hashing. In: International semantic web conference. Springer, pp 49–64Google Scholar
  21. 21.
    Engmann D, Massmann S (2007) Instance matching with coma++. In: BTW workshops, vol 7. pp 28–37Google Scholar
  22. 22.
    Faria D, Pesquita C, Balasubramani BS, Martins C, Cardoso J, Curado H, Couto FM, Cruz IF, (2016) OAEI 2016 results of AML. In: Ontology matching, p 138Google Scholar
  23. 23.
    Fernandes K, Vinagre P, Cortez P (2015) A proactive intelligent decision support system for predicting the popularity of online news. In: Portuguese conference on artificial intelligence. Springer, pp 535–546Google Scholar
  24. 24.
    Gasparetti F (2017) Modeling user interests from web browsing activities. Data Min Knowl Discov 31(2):502–547MathSciNetCrossRefGoogle Scholar
  25. 25.
    Gionis A, Indyk P, Motwani R et al (1999) Similarity search in high dimensions via hashing. In: VLDB, vol 99. pp 518–529Google Scholar
  26. 26.
    Grauman K, Darrell T (2007) Pyramid match hashing: sub-linear time indexing over partial correspondences. In: Computer vision and pattern recognition, 2007. CVPR’07. IEEE conference on’. IEEE, pp 1–8Google Scholar
  27. 27.
    Haveliwala T, Gionis A, Indyk P (2000) Scalable techniques for clustering the web (extended abstract). In: Third international workshop on the web and databases (WebDB 2000). http://ilpubs.stanford.edu:8090/445/. Accessed 19 Oct 2017
  28. 28.
    He K, Wen F, Sun J (2013) \(K\)-means hashing: an affinity-preserving quantization method for learning binary compact codes. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 2938–2945Google Scholar
  29. 29.
    Indyk P, Motwani R (1998) Approximate nearest neighbors: towards removing the curse of dimensionality. In: Proceedings of the thirtieth annual ACM symposium on Theory of computing. ACM, pp 604–613Google Scholar
  30. 30.
    Isaac A, Van Der Meij L, Schlobach S, Wang S (2007) An empirical study of instance-based ontology matching. In: The semantic web. Springer, pp 253–266Google Scholar
  31. 31.
    Jaccard P (1901) Étude comparative de la distribution florale dans une portion des alpes et des jura. Bull Soc Vaudoise Sci Nat 37:547–579Google Scholar
  32. 32.
    Jain P, Hitzler P, Sheth AP, Verma K, Yeh PZ (2010) Ontology alignment for linked open data. In: International semantic web conference. Springer, pp 402–417Google Scholar
  33. 33.
    Jain P, Kulis B, Grauman K (2008) Fast image search for learned metrics. In: Computer vision and pattern recognition, 2008. CVPR 2008. IEEE conference on. IEEE, pp 1–8Google Scholar
  34. 34.
    Jain P, Yeh PZ, Verma K, Vasquez RG, Damova M, Hitzler P, Sheth AP (2011) Contextual ontology alignment of lod with an upper ontology: a case study with proton. In: Extended semantic web conference. Springer, pp 80–92Google Scholar
  35. 35.
    Jiménez-Ruiz E, Grau BC, Cross V (2016) Logmap family participation in the OAEI 2016. In: Ontology matching, p 185Google Scholar
  36. 36.
    Kulis B, Grauman K (2012) Kernelized locality-sensitive hashing. IEEE Trans Pattern Anal Mach Intell 34(6):1092–1104CrossRefGoogle Scholar
  37. 37.
    Leskovec J, Rajaraman A, Ullman JD (2014) Mining of massive datasets. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  38. 38.
    Li J, Tang J, Li Y, Luo Q (2009) Rimom: a dynamic multistrategy ontology alignment framework. IEEE Trans Knowl Data Eng 21(8):1218–1232CrossRefGoogle Scholar
  39. 39.
    Li W-S, Clifton C (2000) Semint: a tool for identifying attribute correspondences in heterogeneous databases using neural networks. Data Knowl Eng 33(1):49–84CrossRefzbMATHGoogle Scholar
  40. 40.
    Lichman M (2013) UCI machine learning repository. http://archive.ics.uci.edu/ml. Accessed 15 Feb 2017
  41. 41.
    Lin J (2009) Brute force and indexed approaches to pairwise document similarity comparisons with MapReduce. In: Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval. ACM, pp 155–162Google Scholar
  42. 42.
    Madhavan J, Bernstein PA, Rahm E (2001) Generic schema matching with cupid. In: vldb vol 1. pp 49–58Google Scholar
  43. 43.
    Manber U et al (1994) Finding similar files in a large file system. In: Usenix winter, vol 94. pp 1–10Google Scholar
  44. 44.
    McAuley J, Pandey R, Leskovec J (2015) , Inferring networks of substitutable and complementary products. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 785–794Google Scholar
  45. 45.
    McAuley J, Targett C, Shi Q, van den Hengel A (2015) Image-based recommendations on styles and substitutes. In: Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval. ACM, pp 43–52Google Scholar
  46. 46.
    Melnik S, Garcia-Molina H, Rahm E (2002) , Similarity flooding: a versatile graph matching algorithm and its application to schema matching. In: Data engineering 2002. Proceedings. 18th international conference on. IEEE, pp 117–128Google Scholar
  47. 47.
    Rajaraman A, Ullman JD (2011) Mining of massive datasets. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  48. 48.
    Ravichandran D, Pantel P, Hovy E (2005) Randomized algorithms and nlp: using locality sensitive hash function for high speed noun clustering. In: Proceedings of the 43rd annual meeting on association for computational linguistics, Association for Computational Linguistics, pp 622–629Google Scholar
  49. 49.
    Rong S, Niu X, Xiang EW, Wang H, Yang Q, Yu Y (2012) A machine learning approach for instance matching based on similarity metrics. In: International semantic web conference. Springer, pp 460–475Google Scholar
  50. 50.
    Seddiqui M, Nath R, PD, Aono M et al (2015) An efficient metric of automatic weight generation for properties in instance matching technique. ArXiv preprint arXiv:1502.03556
  51. 51.
    Spohr D, Hollink L, Cimiano P (2011) A machine learning approach to multilingual and cross-lingual ontology matching. In: International semantic web conference. Springer, pp 665–680Google Scholar
  52. 52.
    Stoilos G, Stamou G, Kollias S (2005) A string metric for ontology alignment. In: International semantic web conference. Springer, pp 624–637Google Scholar
  53. 53.
    Wang C, Lu J, Zhang G (2006) Integration of ontology data through learning instance matching. In: Web intelligence, 2006. WI 2006. IEEE/WIC/ACM international conference on. IEEE, pp 536–539Google Scholar
  54. 54.
    Wang S, Englebienne G, Schlobach S (2008) Learning concept mappings from instance similarity. In: The semantic web-ISWC 2008. pp 339–355Google Scholar
  55. 55.
    Wrigley SN, García-Castro R, Nixon L (2012) Semantic evaluation at large scale (seals). In: Proceedings of the 21st international conference on world wide web. ACM, pp 299–302Google Scholar
  56. 56.
    Xu D, Wu J, Li D, Tian Y, Zhu X, Wu X (2017) SALE: Self-adaptive LSH encoding for multi-instance learning. Pattern Recognit 71:460–482CrossRefGoogle Scholar
  57. 57.
    Zhang W, Ji J, Zhu J, Xu H, Zhang B (2015) Large scale sentiment analysis with locality sensitive BitHash. In: Asia information retrieval symposium. Springer, pp 29–40Google Scholar
  58. 58.
    Zhu E, Nargesian F, Pu KQ, Miller RJ (2016) LSH ensemble: internet-scale domain search. Proc VLDB Endow 9(12):1185–1196CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceKent State UniversityKentUSA
  2. 2.Department of Software EngineeringBahcesehir UniversityBeşiktaş, IstanbulTurkey

Personalised recommendations