OGIR: an ontology‐based grid information retrieval framework
Author:
Hung Chihli,Tsai Chih‐Fong,Hung Shin‐Yuan,Ku Chang‐Jiang
Abstract
PurposeA grid information retrieval model has benefits for sharing resources and processing mass information, but cannot handle conceptual heterogeneity without integration of semantic information. The purpose of this research is to propose a concept‐based retrieval mechanism to catch the user's query intentions in a grid environment. This research re‐ranks documents over distributed data sources and evaluates performance based on the user judgment and processing time.Design/methodology/approachThis research uses the ontology lookup service to build the concept set in the ontology and captures the user's query intentions as a means of query expansion for searching. The Globus toolkit is used to implement the grid service. The modification of the collection retrieval inference (CORI) algorithm is used for re‐ranking documents over distributed data sources.FindingsThe experiments demonstrate that this proposed approach successfully describes the user's query intentions evaluated by user judgment. For processing time, building a grid information retrieval model is a suitable strategy for the ontology‐based retrieval model.Originality/valueMost current semantic grid models focus on construction of the semantic grid, and do not consider re‐ranking search results from distributed data sources. The significance of evaluation from the user's viewpoint is also ignored. This research proposes a method that captures the user's query intentions and re‐ranks documents in a grid based on the CORI algorithm. This proposed ontology‐based retrieval mechanism calculates the global relevance score of all documents in a grid and displays those documents with higher relevance to users.
Subject
Library and Information Sciences,Computer Science Applications,Information Systems
Reference50 articles.
1. Aloisio, G., Cafaro, M., Epicoco, I., Fiore, S. and Mirto, M. (2005), “A semantic grid‐based data access and integration service for bioinformatics”, Proceedings of 2005 IEEE International Symposium on Cluster Computing and the Grid, IEEE Computer Society Press, Los Alamitos, CA, pp. 196‐203. 2. Basirat, A.H. and Khan, A.I. (2010), “Evolution of information retrieval in cloud computing by redesigning data management architecture from a scalable associative computing perspective”, Neural Information Processing, Models and Applications, Lecture Notes in Computer Science, Vol. 6444, Springer, Berlin, pp. 275‐82. 3. Belkin, N.J., Cool, C., Kelly, D., Lin, S.‐J., Park, S.Y., Perez‐Carballo, J. and Sikora, C. (2001), “Iterative exploration, design and evaluation of support for query reformulation in interactive information retrieval”, Information Processing & Management, Vol. 37 No. 3, pp. 403‐34. 4. Callan, J.P., Lu, Z. and Croft, W.B. (1995), “Searching distributed collections with inference networks”, Proceedings of the 18th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM Press, New York, NY, pp. 21‐8. 5. Castells, P., Perdrix, F., Pulido, E., Rico, M., Benjamins, V.R., Contreras, J. and Lorés, J. (2004), “Neptuno: semantic web technologies for a digital newspaper archive”, The Semantic Web: Research and Applications, Lecture Notes in Computer Science, Vol. 3053, Springer, Berlin, pp. 445‐58.
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
|
|