Automatic metadata generation using associative networks

ABSTRACT

In spite of its tremendous value, metadata is generally sparse and incomplete, thereby hampering the effectiveness of digital information services. Many of the existing mechanisms for the automated creation of metadata rely primarily on content analysis which can be costly and inefficient. The automatic metadata generation system proposed in this article leverages resource relationships generated from existing metadata as a medium for propagation from metadata-rich to metadata-poor resources. Because of its independence from content analysis, it can be applied to a wide variety of resource media types and is shown to be computationally inexpensive. The proposed method operates through two distinct phases. Occurrence and cooccurrence algorithms first generate an associative network of repository resources leveraging existing repository metadata. Second, using the associative network as a substrate, metadata associated with metadata-rich resources is propagated to metadata-poor resources by means of a discrete-form spreading activation algorithm. This article discusses the general framework for building associative networks, an algorithm for disseminating metadata through such networks, and the results of an experiment and validation of the proposed method using a standard bibliographic dataset.

REFERENCES

• 1: Johan Bollen , Herbert Van de Sompel , Joan A. Smith , Rick Luce, Toward alternative metrics of journal impact: a comparison of download and citation data, Information Processing and Management: an International Journal, v.41 n.6, p.1419-1440, December 2005 [doi>10.1016/j.ipm.2005.03.024]
• 2: Paul R. Cohen , Rick Kjeldsen, Information retrieval by constrained spreading activation in semantic networks, Information Processing and Management: an International Journal, v.23 n.4, p.255-268, July 1987 [doi>10.1016/0306-4573(87)90017-3]
• 3: Collins, A. and Loftus, E. 1975. A spreading activation theory of semantic processing. Psych. Rev. 82, 407--428.
• 4: F. Crestani, Application of Spreading Activation Techniques in InformationRetrieval, Artificial Intelligence Review, v.11 n.6, p.453-482, Dec. 1997 [doi>10.1023/A:1006569829653]
• 5: Fabio Crestani , Puay Leng Lee, Searching the Web by constrained spreading activation, Information Processing and Management: an International Journal, v.36 n.4, p.585-605, July 2000 [doi>10.1016/S0306-4573(99)00073-4]
• 6: de Lin, S. and Chalupsky, H. 2004. Issues of verification for unsupervised discovery systems. In Proceedings of the ACM/SIKDD International Conference on Knowledge Discovery and Data Mining (KDD'04) Workshop on Link Discovery.
• 7: Duval, E., Hodgins, W., Sutton, S., and Weibel, S. L. 2002. Metadata principles and practices. D-Lib Mag. 8, 4.
• 8: Giovanni Giuffrida , Eddie C. Shek , Jihoon Yang, Knowledge-based metadata extraction from PostScript files, Proceedings of the fifth ACM conference on Digital libraries, p.77-84, June 02-07, 2000, San Antonio, Texas, United States [doi>10.1145/336597.336639]
• 9: Scott A. Golder , Bernardo A. Huberman, Usage patterns of collaborative tagging systems, Journal of Information Science, v.32 n.2, p.198-208, April 2006 [doi>10.1177/0165551506062337]
• 10: Greenberg, J. 2004. Metadata extraction and harvesting: A comparison of two automatic metadata generation applications. J. Intern. Catalog. 6, 4, 59--82.
• 11: Hui Han , C. Lee Giles , Eren Manavoglu , Hongyuan Zha , Zhenyue Zhang , Edward A. Fox, Automatic document metadata extraction using support vector machines, Proceedings of the 3rd ACM/IEEE-CS joint conference on Digital libraries, May 27-31, 2003, Houston, Texas
• 12: Jonathan L. Herlocker , Joseph A. Konstan , Loren G. Terveen , John T. Riedl, Evaluating collaborative filtering recommender systems, ACM Transactions on Information Systems (TOIS), v.22 n.1, p.5-53, January 2004 [doi>10.1145/963770.963772]
• 13: Zan Huang , Hsinchun Chen , Daniel Zeng, Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering, ACM Transactions on Information Systems (TOIS), v.22 n.1, p.116-142, January 2004 [doi>10.1145/963770.963775]
• 14: Zan Huang , Xin Li , Hsinchun Chen, Link prediction approach to collaborative filtering, Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries, June 07-11, 2005, Denver, CO, USA [doi>10.1145/1065385.1065415]
• 15: Kuwano, H., Matsuo, Y., and Kawazoe, K. 2004. Reducing the cost of metadata generation by using video/audio indexing and natural language processing techniques. NTT Technical Review, 68--74.
• 16: David Liben-Nowell , Jon Kleinberg, The link prediction problem for social networks, Proceedings of the twelfth international conference on Information and knowledge management, November 03-08, 2003, New Orleans, LA, USA [doi>10.1145/956863.956972]
• 17: Song Mao , Jong Woo Kim , George R. Thoma, A Dynamic Feature Generation System for Automated Metadata Extraction in Preservation of Digital Materials, Proceedings of the First International Workshop on Document Image Analysis for Libraries (DIAL'04), p.225, January 23-24, 2004
• 18: Mathes, A. 2004. Folksonomies—cooperative classification and communication through shared metadata. Computer Mediated Communication - LIS590CMC (graduate course), Univerity of Illinois Urbana-Champaisn.
• 19: Amy McGovern , Lisa Friedland , Michael Hay , Brian Gallagher , Andrew Fast , Jennifer Neville , David Jensen, Exploiting relational structure to understand publication patterns in high-energy physics, ACM SIGKDD Explorations Newsletter, v.5 n.2, December 2003 [doi>10.1145/980972.980999]
• 20: Mor Naaman , Ron B. Yeh , Hector Garcia-Molina , Andreas Paepcke, Leveraging context to resolve identity in photo albums, Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries, June 07-11, 2005, Denver, CO, USA [doi>10.1145/1065385.1065430]
• 21: Camille Prime-Claverie , Michel Beigbeder , Thierry Lafouge, Metadata Propagation in the Web Using Co-Citations, Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence, p.602-605, September 19-22, 2005 [doi>10.1109/WI.2005.95]
• 22: Marko A. Rodriguez, Social Decision Making with Multi-Relational Networks and Grammar-Based Particle Swarms, Proceedings of the 40th Annual Hawaii International Conference on System Sciences, p.39, January 03-06, 2007 [doi>10.1109/HICSS.2007.487]
• 23: Marko A. Rodriguez, Grammar-based random walkers in semantic networks, Knowledge-Based Systems, v.21 n.7, p.727-739, October, 2008 [doi>10.1016/j.knosys.2008.03.030]
• 24: Rorvig, M., Jeong, K.-T., Pachlag, A., Anusuri, R., and Oyarce, S. 2002. Content based image retrieval by integration of metadata encoded multimedia (image and text) features. In Proceedings of the World Wide Web Conference (WWW'02). ACM.
• 25: Fabrizio Sebastiani, Machine learning in automated text categorization, ACM Computing Surveys (CSUR), v.34 n.1, p.1-47, March 2002 [doi>10.1145/505282.505283]
• 26: Jewel Ward, A quantitative analysis of unqualified dublin core metadata element set usage within data providers registered with the open archives initiative, Proceedings of the 3rd ACM/IEEE-CS joint conference on Digital libraries, May 27-31, 2003, Houston, Texas
• 27: Hsin-Chang Yang , Chung-Hong Lee, Automatic Metadata Generation forWeb Pages Using a Text Mining Approach, Proceedings of the International Workshop on Challenges in Web Information Retrieval and Integration, p.186-194, April 08-09, 2005

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.1 Content Analysis and Indexing

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.7 Digital Libraries

General Terms:
Algorithms, Experimentation

Keywords:
Associative networks, metadata generation, particle-swarms