A Multi-strategy Approachfor Catalog Integration
Ryutaro Ichise1,2 , Masahiro Hamasaki2 , and Hideaki Takeda1,212

National Institute of Informatics, Tokyo 101-8430, JapanThe Graduate University for Advanced Studies, Tokyo 101-8430, Japan{ichise,takeda}@nii.ac.jp, hamasaki@grad.nii.ac.jp

Abstract. When we have a large amount of information, we usually use
categories with a hierarchy, in which all information is assigned. This paper proposes a new method of integrating two catalogs with hierarchicalcategories. The proposed method uses not only the contents of information but also the structures of both hierarchical categories. We conducted
experiments using two actual Internet directories, and the results show
improved performance compared with the previous approach.

In this paper, we introduce a novel approach for catalog integration problem.
The problem addressed in this paper is ﬁnding an appropriate category Ct in
the target catalog TC for each information instance Isi in the source catalog
SC . What we need to do is determine an appropriate category in TC for aninformation instance. In order to solve the problem, we proposed the Similaritybased integration (SBI) [3]. SBI has a higher performance compared with theNaive Bayes (NB) approach, even with the extension proposed by [1]. In thispaper, we propose a method which combines the SBI approach and the NBapproach. In order to combine handling the meaning of information, we propose
using NB after SBI.
A problem of SBI is that it is hard to learn a mapping rule when the destination category is in a lower category in the target concept hierarchy. In otherwords, the learned rules are likely to assign relatively general categories in thetarget catalog. In order to avoid this type of rules, we propose to combine acontents-based classiﬁcation method after we apply the SBI algorithm. Since NBis very popular and easy to use, we adopt NB as the contents-based classiﬁcation
method. In order to apply the NB algorithm for hierarchical classiﬁcation, weutilize the simple method of the Pachinko Machine NB. The Pachinko Machineclassiﬁes instances at internal nodes of the tree, and greedily selects sub-branches
until it reaches a leaf [4]. This method is applied after the rule induced by SBI
decides the starting category for the Pachinko Machine NB.
In order to evaluate the proposed algorithm, we conducted experiments using
real Internet directories collected from Yahoo! [5] and Google [2]. The data wascollected during the period from December 2003 to January 2004. The locations
in Yahoo! and Google are Photography. We conducted ten-fold cross validations
for the links appeared in both directories. The shared links were divided intoC. Zhang, H.W. Guesgen, W.K. Yeap (Eds.): PRICAI 2004, LNAI 3157, pp. 944–945, 2004.
c Springer-Verlag Berlin Heidelberg 2004
A Multi-strategy Approach for Catalog Integration

945Fig. 1. Experimental Results

ten data sets; nine of which were used to construct rules, and the remaining
set was used for testing. Ten experiments were conducted for each data set,
and the average accuracy is shown in Figure 1. The accuracy is measured foreach depth of the Internet directories. The vertical axes in Figure 1 show theaccuracy and horizontal axes show the depth of the concept hierarchies. The left
side of Figure 1 shows the results obtained using Google as the source catalogand Yahoo! as the target catalog, and the right side of Figure 1 shows the results
obtained using Yahoo! as the source catalog and Google as the target catalog.
For comparison, these graphs also include the results of SBI. SBI-NB denotes theresults of the method proposed in this paper. The proposed algorithm performs
much better in accuracy than the original SBI. One reason for this is that theNB works well. In other words, the contents-based classiﬁcation is suited forthis domain. According to [3], the NB method does not achieve the performanceof SBI in the Photography domain. However, our proposed algorithm eﬀectively
combines the contents-based method with the category similarity-based method.
In this paper, a new technique was proposed for integrating multiple catalogs. The proposed method uses not only the similarity of the categorization ofcatalogs but also the contents of information instances. The performance of theproposed method was tested using actual Internet directories, and the results ofthese tests show that the performance of the proposed method is more accuratefor the experiments.

References
1. R. Agrawal and R. Srikant. On integrating catalogs. In Proc. of the 10th Int. World
Wide Web Conf., 603–612, 2001.
2. Google. http://directory.google.com/, 2003.
3. R. Ichise, H. Takeda and S. Honiden. Integrating Multiple Internet Directories by
Instance-based Learning. In Proc. of the 18th Int. Joint Conf. on AI, 22–28, 2003.
4. A. K. McCallum, et al. Improving text classification by shrinkage in a hierarchy ofclasses. In Proc. of the 15th Int. Conf. on Machine Learning, 359–367, 1998.
5. Yahoo! http://www.yahoo.com/, 2003.