An Automated Approach to Product Taxonomy Mapping in E-Commerce
Due to the ever-growing amount of information available on Web shops, it has become increasingly difficult to get an overview of Web-based product information. There are clear indications that better search capabilities, such as the exploitation of annotated data, are needed to keep online shopping transparent for the user. For example, annotations can help present information from multiple sources in a uniform manner. This paper proposes an algorithm that can autonomously map heterogeneous product taxonomies forWeb shop data integration purposes. The proposed approach uses word sense disambiguation techniques, approximate lexical matching, and a mechanism that deals with composite categories. Our algorithm’s performance on three real-life datasets was compared favourably against two other state-of-the-art taxonomy mapping algorithms. The experiments show that our algorithm performs at least twice as good compared to the other algorithms w.r.t. precision and F-measure.
Keywordse-commerce taxonomy mapping word sense disambiguation
Unable to display preview. Download preview PDF.
- 1.Aumueller, D., Do, H.H., Massmann, S., Rahm, E.: Schema and Ontology Matching with COMA++. In: ACM SIGMOD International Conference on Management of Data 2005 (SIGMOD 2005), pp. 906–908. ACM (2005)Google Scholar
- 4.Horrigan, J.B.: Online Shopping. Pew Internet & American Life Project Report 36 (2008)Google Scholar
- 5.Lesk, M.: Automatic Sense Disambiguation using Machine Readable Dictionaries: How to tell a Pine Cone from an Ice Cream Cone. In: 5th Annual ACM SIGDOC International Conference on Systems Documentation (SIGDOC 1986), pp. 24–26. ACM (1986)Google Scholar
- 6.Li, J.: LOM: A Lexicon-based Ontology Mapping Tool. In: 5th Workshop Performance Metrics for Intelligent Systems, PerMIS 2004 (2004)Google Scholar
- 7.Madhavan, J., Bernstein, P.A., Rahm, E.: Generic Schema Matching with Cupid. In: 27th International Conference on Very Large Data Bases (VLDB 2001), pp. 49–58. Morgan Kaufmann Publishers Inc. (2001)Google Scholar
- 8.Melnik, S., Garcia-Molina, H., Rahm, E.: Similarity Flooding: A Versatile Graph Matching Algorithm and its Application to Schema Matching. In: 18th International Conference on Data Engineering (ICDE 2002), pp. 117–128. IEEE Computer Society (2002)Google Scholar
- 12.Shopping.com: Online Shopping Comparison Website (2011), http://www.shopping.com
- 13.Zhang, G.Q., Zhang, G.Q., Yang, Q.F., Cheng, S.Q., Zhou, T.: Evolution of the Internet and its Cores. New Journal of Physics 10(12), 123027 (2008)Google Scholar