Uncertain schema matching based on interval fuzzy similarities

被引：0

作者：

Weng, Nian-Feng ^{[1
,2
]}

Diao, Xing-Chun ^{[2
]}

机构：

[1] PLA University of Science and Technology, Nanjing

[2] Nanjing Telecommunication Technology Institute, Nanjing

来源：

International Journal of Advancements in Computing Technology | 2012年 / 4卷 / 01期

关键词：

Fuzzy decision making; Interval fuzzy similarity; Schema matching; Uncertainty;

D O I：

10.4156/ijact.vol4.issue1.18

中图分类号：

学科分类号：

摘要：

Schema matching is very important in many database applications. Due to the fuzziness of knowledge representation, schema matching contains uncertainty inherently. In order to manage the uncertainty of schema matching process, fuzzy decision making theory is employed and the similarity of schema element pair is given in the form of interval fuzzy number. According to the concept of composite schema matching, interval fuzzy similarities are constructed and a candidate mapping selection procedure is proposed based on the priority of interval fuzzy similarities. After candidate mappings are selected, we propose a semantic conflict elimination procedure to remove false positive candidate mappings. Many existing schema matching algorithms can only identify 1:1 mappings, while our algorithm can identify both 1:1 and 1:n mappings. The quality of our uncertain schema matching algorithm is verified by experiments.

引用

页码：163 / 171

页数：8

共 16 条

[1]

Rahm E., Bernstein P., A Survey of Approaches to Automatic Schema Matching, The VLDB Journal, 10, 4, pp. 334-350, (2001)

[2]

Lilac A., Al-Safadi E., Electronic Medical Record Ontology Mapper, International Journal of Advancements in Computing Technology, AICIT, 1, 1, pp. 85-97, (2009)

[3]

Pan D., Metadata Version Management for DW 2.0 Environment, Journal of Convergence Information Technology, AICIT, 5, 3, pp. 54-60, (2010)

[4]

Saber B., Okba K., A Scalable and Efficient Query Answering for a Context and Schema Mediation, Journal of Convergence Information Technology, AICIT, 5, 1, pp. 23-32, (2010)

[5]

Sorrentino S., Bergamanschi S., Gawinecki M., Po L., Schema Label Normalization for Improving Schema Matching, Data & Knowledge Engineering, Elsevier, 69, 12, pp. 1254-1273, (2010)

[6]

Gal A., Modica G., Jamil H., Eyal A., Automatic Ontology Matching using Application Semantics, AI Magazine, AAAI, 26, 1, pp. 21-32, (2005)

[7]

Chen K., Zuo W., He F., Chen Y., Hybrid Schema Matching for Deep Web, Communications in Computer and Information Science, 135, pp. 165-170, (2011)

[8]

Madhavan J., Bernstein P., Rahm E., Generic Schema Matching with Cupid, International Conference on Very Large Data Bases, pp. 49-58, (2001)

[9]

Do H.-H., Rahm E., COMA-A System for Flexible Combination of Schema Matching Approaches, International Conference on Very Large Data Bases, pp. 610-621, (2002)

[10]

Mork P., Seligman L., Rosenthal A., Korb J., Wolf C., The Harmony Integration Workbench, Journal of Data Semantics XI, LNCS, 5383, pp. 65-93, (2008)

← 1 2 →