Matching Hierarchies Using Shared Objects

Authors:
Robert Ikeda;Kai Zhao;Hector Garcia-Molina
Affiliations:
Computer Science Department, Stanford University, Stanford, USA CA 94305;NEC Laboratories China, Beijing, China;Computer Science Department, Stanford University, Stanford, USA CA 94305
Venue:
ECDL '08 Proceedings of the 12th European conference on Research and Advanced Technology for Digital Libraries
Year:
2008

Citing 7
Cited 0

On integrating catalogs

Proceedings of the 10th international conference on World Wide Web
Learning to map between ontologies on the semantic web

Proceedings of the 11th international conference on World Wide Web
Similarity Flooding: A Versatile Graph Matching Algorithm and Its Application to Schema Matching

ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Cross-training: learning probabilistic mappings between topics

Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Ontology mapping: the state of the art

The Knowledge Engineering Review
Clio grows up: from research prototype to industrial tool

Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Semantic schema matching

OTM'05 Proceedings of the 2005 Confederated international conference on On the Move to Meaningful Internet Systems - Volume >Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

One of the main challenges in integrating two hierarchies (e.g., of books or web pages) is determining the correspondence between the edges of each hierarchy. Traditionally, this process, which we call hierarchy matching, is done by comparing the text associated with each edge. In this paper we instead use the placement of objects present in both hierarchies to infer how the hierarchies relate. We present two algorithms that, given a hierarchy with known facets(attribute-value pairs that define what objects are placed under an edge), determine feasible facets for a second hierarchy, based on shared objects. One algorithm is rule-based and the other is statistics-based. In the experimental section, we compare the results of the two algorithms, and see how their performances vary based on the amount of noise in the hierarchies.