Abstract
In the Transportation and Logistics (TL) industry, address validation is crucial. Indeed, due to the huge number of parcel shipments that are moving worldwide everyday, incorrect addresses generates several shipment returns, leading to useless financial and ecological costs. In this paper, we propose an entity-matching approach and system for validating TL entities. The approach is based on Word Embedding and Supervised Learning techniques. Experiments carried out on a real dataset demonstrate the effectiveness of the approach.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
- 3.
- 4.
- 5.
- 6.
- 7.
- 8.
- 9.
- 10.
- 11.
- 12.
- 13.
- 14.
- 15.
- 16.
- 17.
- 18.
- 19.
- 20.
- 21.
- 22.
- 23.
- 24.
- 25.
References
Comber, S., Arribas-Bel, D.: Machine learning innovations in address matching: a practical comparison of word2vec and CRFs. Trans. GIS 23(2), 334–348 (2019)
Ebraheem, M., Thirumuruganathan, S., Joty, S., Ouzzani, M., Tang, N.: DeepER-Deep Entity Resolution. arXiv preprint arXiv:1710.00597 (2017)
Konda, P., et al. Magellan.: toward building entity matching management systems [Technical report]. http://www.cs.wisc.edu/anhai/papers/magellan-tr.pdf (2016)
Fan, W., Jia, X., Li, J., Ma, S.: Reasoning about record matching rules. Proc. VLDB Endowment 2(1), 407–418 (2009)
Govind, Y., et al.: Entity matching meets data science: a progress report from the magellan project. In: Proceedings of the 2019 International Conference on Management of Data, pp. 389–403. June 2019
Gschwind, T., Miksovic, C., Minder, J., Mirylenka, K., Scotton, P.: Fast record linkage for company entities. In: 2019 IEEE International Conference on Big Data, pp. 623–630. IEEE, December 2019
Jaccard, P.: Nouvelles recherches sur la distribution florale. Bull. Soc. Vaud. Sci. Nat. 44, 223–270 (1908)
Konda, P., et al.: Magellan: toward building entity matching management systems. Proc. VLDB Endowment 9(12), 1197–1208 (2016)
Köpcke, H., Thor, A., Rahm, E.: Evaluation of entity resolution approaches on real-world match problems. Proc. VLDB Endow. 3(1–2), 484–493 (2010)
Koumarelas, I., Kroschk, A., Mosley, C., Naumann, F.: Experience: enhancing address matching with geocoding and similarity measure selection. J. Data Inf. Qual. (JDIQ) 10(2), 1–16 (2018)
Levenshtein, V.I.: Binary codes capable of correcting deletions, insertions, and reversals. Soviet physics doklady 10(8), 707–710 (1966)
Lin, Y., Kang, M., Wu, Y., Du, Q., Liu, T.: A deep learning architecture for semantic address matching. Int. J. Geogr. Inf. Sci. 34(3), 559–576 (2020)
Magnani, M., Montesi, D.: A study on company name matching for database integration. Technical report UBLCS-07-15 (2007)
Matci, D.K., Avdan, U.: Address standardization using the natural language process for improving geocoding results. Comput. Environ. Urban Syst. 70, 1–8 (2018)
McInnes, B.T., Pedersen, T.: Evaluating measures of semantic similarity and relatedness to disambiguate terms in biomedical text. J. Biomed. Inform. 46(6), 1116–1124 (2013)
Medvedev, T., Ulanov, A.: Company Names Matching in the Large Patents Dataset. Hewlett-Packard Development Company, LP (2011)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
Mudgal, S., et al.: Deep learning for entity matching: a design space exploration. In: Proceedings of the 2018 International Conference on Management of Data, pp. 19–34, May 2018
Papadakis, G., Tsekouras, L., Thanos, E., Giannakopoulos, G., Palpanas, T., Koubarakis, M.: The return of JedAl: end-to-end entity resolution for structured and semi-structured data. Proc. VLDB Endow. 11(12), 1950–1953 (2018)
Papadakis, G., Tsekouras, L., Thanos, E., Giannakopoulos, G., Palpanas, T., Koubarakis, M.: Domain-and structure-agnostic end-to-end entity resolution with JedAI. ACM SIGMOD Rec. 48(4), 30–36 (2020)
Pennington, J., Socher, R., Manning, C. D. Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543, October 2014
Rehurek, R., Sojka, P.: Software framework for topic modelling with large corpora. In: Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks (2010)
Shan, S., et al.: Geographical address representation learning for address matching. World Wide Web 23(3), 2005–2022 (2020). https://doi.org/10.1007/s11280-020-00782-2
Singh, R., et al.: Generating concise entity matching rules. In: Proceedings of the 2017 ACM International Conference on Management of Data, pp. 1635–1638, May 2017
Singh, R., et al.: Synthesizing entity matching rules by examples. Proc. VLDB Endow. 11(2), 189–202 (2017)
Srivastava, R.K., Greff, K., Schmidhuber, J.: Highway networks. arXiv preprint arXiv:1505.00387 (2015)
Winkler, W.E.: String Comparator Metrics and Enhanced Decision Rules in the Fellegi-Sunter Model of Record Linkage (1990)
Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. Trans. Assoc. Comput. Linguist. 5, 135–146 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Guermazi, Y., Sellami, S., Boucelma, O. (2020). Address Validation in Transportation and Logistics: A Machine Learning Based Entity Matching Approach. In: Koprinska, I., et al. ECML PKDD 2020 Workshops. ECML PKDD 2020. Communications in Computer and Information Science, vol 1323. Springer, Cham. https://doi.org/10.1007/978-3-030-65965-3_21
Download citation
DOI: https://doi.org/10.1007/978-3-030-65965-3_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-65964-6
Online ISBN: 978-3-030-65965-3
eBook Packages: Computer ScienceComputer Science (R0)
