Abstract
In the presence of dirty data, a search for specific information by a standard query (e.g., search for a name that is misspelled or mistyped) does not return all needed information. This is an issue of grave importance in homeland security, criminology, medical applications, GIS (geographic information systems) and so on. Different techniques, such as soundex, phonix, n-grams, edit-distance, have been used to improve the matching rate in these name-matching applications. There is a pressing need for name matching approaches that provide high levels of accuracy, while at the same time maintaining the computational complexity of achieving this goal reasonably low. In this paper, we present ANSWER, a name matching approach that utilizes a prefix-tree of available names in the database. Creating and searching the name dictionary tree is fast and accurate and, thus, ANSWER is superior to other techniques of retrieving fuzzy name matches in large databases.
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References
Kim, W.: On Database Technology for US Homeland Security. Journal of Object Technology 1(5), 43–49 (2002)
Taipale, K.A.: Data Mining & Domestic Security: Connecting the Dots to Make Sense of Data. The Columbia Science & Technology Law Review 5, 1–83 (2003)
Bilenko, M., Mooney, R., Cohen, W., Ravikumar, P., Fienberg, S.: Adaptive name matching in information integration. IEEE Intelligent Systems 18(5), 16–23 (2003)
Wang, G., Chen, H., Atabakhsh, H.: Automatically detecting deceptive criminal identities. Communications of the ACM 47(3), 70–76 (2004)
Pfeifer, U., Poersch, T., Fuhr, N.: Searching Proper Names in Databases. In: Proceedings of the Hypertext - Information Retrieval – Multimedia (HIM 1995), vol. 20, pp. 259–276 (1995)
Winkler, W.E.: The state of record linkage and current research problems. In: Proceedings of the Section on Survey Methods of the Statistical Society of Canada (1999)
Monge, A.E., Elkan, C.P.: An Efficient Domain-Independent Algorithm for Detecting Approximately Duplicate Database Records. In: Proceedings of the ACM-SIGMOD Workshop on Research Issues on Knowledge Discovery and Data Mining, Tucson, AZ (1997)
Newcombe, H.B., Kennedy, J.M., Axford, S.J., James, A.P.: Automatic linkage of vital records. In: Science, vol. 3381, pp. 954–959 (1959)
Levenshtein, V.L.: Binary codes capable of correcting deletions, insertions, and reversals. Soviet Physics, Doklady 10, 707–710 (1966)
Jaro, M.A.: “UNIMATCH: A Record Linkage System: User’s Manual. Technical Report”, U.S. Bureau of the Census, Washington, DC (1976)
Zobel, J., Dart, P.: Finding approximate matches in large lexicons. Software-Practice and Experience 25(3), 331–345 (1995)
Wilcox, J.: Police Agencies Join Forces To Build Data-Sharing Networks: Local, State, and Federal Crimefighters Establish IT Posses, Government Computer News (September 1997)
Maxwell, T.: Information, Data Mining, and National Security: False Positives and Unidentified Negatives. In: Proceedings of the 38th Hawaii International Conference on System Science (2005)
Hernandez, M., Stolfo, S.: Real-world Data is Dirty: Data Cleansing and the Merge/purge Problems. Data Mining Knowledge Discovery 2, 9–37 (1998)
Mihov, S., Schulz, K.U.: Fast Approximate Search in Large Dictionaries. Journal of Computational Linguistics 30(4), 451–477 (2004)
Aoe, J., Morimoto, K., Shishibori, M., Park, K.: A Trie Compaction Algorithm for a Large Set of Keys. IEEE Transactions on Knowledge and Data Engineering 8(3), 476–491 (2001)
Navarro, G.: A Guided Tour to Approximate String Matching. ACM Computing Surveys 33(1), 31–88 (2001)
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Kursun, O., Koufakou, A., Chen, B., Georgiopoulos, M., Reynolds, K.M., Eaglin, R. (2006). A Dictionary-Based Approach to Fast and Accurate Name Matching in Large Law Enforcement Databases. In: Mehrotra, S., Zeng, D.D., Chen, H., Thuraisingham, B., Wang, FY. (eds) Intelligence and Security Informatics. ISI 2006. Lecture Notes in Computer Science, vol 3975. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760146_7
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DOI: https://doi.org/10.1007/11760146_7
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