A kernel method for measuring structural similarity between XML documents

Authors:
Buhwan Jeong;Daewon Lee;Hyunbo Cho;Boonserm Kulvatunyou
Affiliations:
Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang, South Korea;Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang, South Korea;Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang, South Korea;Manufacturing Engineering Laboratory, National Institute of Standards and Technology, Gaithersburg, MD
Venue:
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
Year:
2007

Citing 8
Cited 1

Syllables and other String Kernel Extensions

ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Preparations for Semantics-Based XML Mining

ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Text classification using string kernels

The Journal of Machine Learning Research
Word sequence kernels

The Journal of Machine Learning Research
Fast Detection of XML Structural Similarity

IEEE Transactions on Knowledge and Data Engineering
Learning the Kernel Matrix for XML Document Clustering

EEE '05 Proceedings of the 2005 IEEE International Conference on e-Technology, e-Commerce and e-Service (EEE'05) on e-Technology, e-Commerce and e-Service
A survey of schema-based matching approaches

Journal on Data Semantics IV
An introduction to kernel-based learning algorithms

IEEE Transactions on Neural Networks

On the functional quality of service (FQoS) to discover and compose interoperable web services

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

Measuring structural similarity between XML documents has become a key component in various applications, including XML data mining, schema matching, web service discovery, among others. The paper presents a novel structural similarity measure between XML documents using kernel methods. Results on preliminary simulations show that this outperforms conventional ones.