Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30172
Analyzing Methods of the Relation between Concepts based on a Concept Hierarchy

Authors: Ke Lu, Tetsuya Furukawa

Abstract:

Data objects are usually organized hierarchically, and the relations between them are analyzed based on a corresponding concept hierarchy. The relation between data objects, for example how similar they are, are usually analyzed based on the conceptual distance in the hierarchy. If a node is an ancestor of another node, it is enough to analyze how close they are by calculating the distance vertically. However, if there is not such relation between two nodes, the vertical distance cannot express their relation explicitly. This paper tries to fill this gap by improving the analysis method for data objects based on hierarchy. The contributions of this paper include: (1) proposing an improved method to evaluate the vertical distance between concepts; (2) defining the concept horizontal distance and a method to calculate the horizontal distance; and (3) discussing the methods to confine a range by the horizontal distance and the vertical distance, and evaluating the relation between concepts.

Keywords: Concept Hierarchy, Horizontal Distance, Relation Analysis, Vertical Distance

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1057539

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 849

References:


[1] M. Kuzunishi, T. Furukawa, and K. Lu, "Analyzing Multi-Labeled Data Based on the Roll of a Concept against a Semantic Range," in Proc. of the Int'l Conf. on World Academy of Sciences, Engineering and Technology, Singapore, 2010, pp. 498-504.
[2] D. Koller, and M. Sahami, "Hierarchically Classifying Documents Using Very Few Words," in Proc. of the Fourteenth Int'l Conf. on Machine Learning, 1997, pp.170-178.
[3] S. Amit, "Modern information retrieval: a brief overview," IEEE Data Eng. Bull., vol. 24, Dec. 2001, pp. 35-43.
[4] T. Li, S. Zhu, and M. Ogihara, "Topic hierarchy generation via linear discriminant projection," in Proc. of the 26th annual international ACM SIGIR Conf. on Research and Development in Information Retrieval, 2003, pp. 421-422.
[5] Y. Wang, and Z. Gong, "Hierarchical Classification of Web Pages Using Support Vector Machine," in Proc. of the 11th Int'l Conf. on Asian Digital Libraries, 2008, pp. 12-32.
[6] J. R. Rose, and J. Gasteiger, "Hierarchical classification as an aid to database and hit-list browsing," in Proc. of the third Int'l Conf. on Information and Knowledge Management, 1994, pp. 408-414.
[7] S. Dumais, and H. Chen, "Hierarchical classification of Web content," in Proc. of the 23rd annual Int'l ACM SIGIR Conf. on Research and Development in Information Retrieval, 2000, pp. 256-263.
[8] C. C. Hsu, and Y. P. Huang, "Incremental clustering of mixed data based on distance hierarchy," Expert Syst. Appl. Vol.35, 2008, pp. 1177-1185.
[9] A. X. Sun, and E. P. Lim, "Hierarchical Text Classification and Evaluation," in Proc. of the 2001 IEEE Int'l Conf. on Data Mining, 2001, pp. 521-528.
[10] A. El Sayed, H. Hacid, and D. Zighed, "Using semantic distance in a content-based heterogeneous information retrieval system," in Proc. of the 3rd ECML/PKDD Int'l Conf. on Mining Complex Data, 2008, pp. 224-237.
[11] M. Kuzunishi, and T. Furukawa, "Representation for multiple classified data," in Proc. of the 24th IASTED Int'l Conf. on Database and Applications, 2006, pp.135-142.
[12] B. Catherine, and P. Wanda, "Better Rules, Few Features: A Semantic Approach to Selecting Features from Text," in Proc. of the 2001 IEEE Int'l Conf. on Data Mining, 2001, pp. 59-66.
[13] K. Bade, and A. N├╝rnberger, "Creating a Cluster Hierarchy under Constraints of a Partially Known Hierarchy," in Proc. of the 2008 SIAM Int'l Conf. on Data Mining, 2008, pp. 13-24.
[14] A. M. Funes, C. Ferri, J. Hernández-Orallo, and M. J. Ramírez-Quintana, "Hierarchical Distance-Based Conceptual Clustering," in Proc. of the Conf. on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2008, pp. 349-364.
[15] K. Toutanova, F. Chen, K. Popat, and T. Hofmann, "Text Classification in a Hierarchical Mixture Model for Small Training Sets," in Proc. of Int'l Conf. on Information and Knowledge Management, 2001, pp. 105-112.