A New Approach of Intelligent Data Retrieval Paradigm

Authors

  • Falah Al-akashi Faculty of Engineering, University of Kufa, Iraq
  • Diana Inkpen Faculty of Engineering, University of Ottawa, Canada

DOI:

https://doi.org/10.30564/aia.v3i2.3219

Abstract

What is a real time agent, how does it remedy ongoing daily frustrations for users, and how does it improve the retrieval performance in World Wide Web? These are the main question we focus on this manuscript. In many distributed information retrieval systems, information in agents should be ranked based on a combination of multiple criteria. Linear combination of ranks has been the dominant approach due to its simplicity and effectiveness. Such a combination scheme in distributed infrastructure requires that the ranks in resources or agents are comparable to each other before combined. The main challenge is transforming the raw rank values of different criteria appropriately to make them comparable before any combination. Different ways for ranking agents make this strategy difficult. In this research, we will demonstrate how to rank Web documents based on resource-provided information how to combine several resources raking schemas in one time. The proposed system was implemented specifically in data provided by agents to create a comparable combination for different attributes. The proposed approach was tested on the queries provided by Text Retrieval Conference (TREC). Experimental results showed that our approach is effective and robust compared with offline search platforms.

Keywords:

Intelligent agents; Ranking schema; Distributed approach; Vector space model

References

[1] YIGAL, A., CHIN, C., CHUN, H., and CRAIG, K. (1993). “Retrieving and Integrating Data from Multiple Information Sources”. Intelligent and Cooperative Information Systems, Vol. 2, No. 2, pp. 127-158.

[2] Gerard, S., Wong, A., and Yang, C. (1975). “A Vector Space Model for Information Retrieval”. Communications of the ACM, 18 (11): pp. 613-620.

[3] Gasser, L. (1988). “Large-scale concurrent computing in artificial intelligence research”. In Proceedings of the third conference on Hypercube concurrent computers and applications.

[4] Al-akashi, F. (2014). “Using Wikipedia Knowledge and Query Types in a New Indexing Approach for Web Search Engines”. PhD dissertation, University of Ottawa.

[5] Lenat, D., and Guha, V. (1990). “Building Large Knowledge-Based Systems: Representation and Inference in the Cyc Project,Addison-Wesley, Reading, Mass.

[6] Knoblock, A. and Ambite, L. (1997). “Agents for Information Gathering,” in Software Agents, J. Bradshaw, ed., AAAI/MIT Press.

[7] Knoblock, A. (1996). “Building a Planner for Information Gathering: A Report from the Trenches,” Proc. Third Int’l Conf. AI Planning Systems, AAAI Press, pp. 134-141.

[8] Hazem, M., Alaa, R., Ahmed, A., Sameh, G., and Nikos, M. (2015). “A New Automated Information Retrieval System by using Intelligent Mobile Agent”. RECENT ADVANCES in ARTIFICIAL INTELLIGENCE, KNOWLEDGE ENGINEERING and DATA BASES.

[9] Dhanapal, R. (2008). “An intelligent information retrieval agent”. Knowledge-Based Systems, Volume 21, Issue 6, pp. 466-470.

[10] Jidé, O., David, K., Stuart, W., Nagi, W., Sadanand, S., Chris, G., and JoAnna, G. (1997). ”SAIRE - A SCALABLE AGENT-BASED INFORMATION RETRIEVAL ENGINE”, Proceedings of the first international conference on Autonomous agents.

[11] Rhodes, B. and Maes, P. (2000). “Just-in-time information retrieval agents“. IBM Systems Journal , Volume: 39 Issue: 3.4.

[12] Qian, G. and Young-Im, C. ( 2013) . “A Multi-Agent Information Retrieval System Based on Ontology”. Advances in Intelligent Systems and Computing book series (AISC, volume 194). Intelligent Autonomous Systems 12, pp. 593-602.

[13] Norbert, F., Mounia, L., Saadia, M., and Zoltan, S. (2004) “Advances in XML Information Retrieval”. Third International Workshop of the Initiative for the Evaluation of XML Retrieval, INEX, Springer.

[14] Ambite, J. and Knoblock, C. (2000). “Flexible and Scalable Cost-Based Query Planning in Mediators: A Transformational Approach”, Artificial Intelligence Journal.

[15] Pang, L., Lan, Y., Guo, J., Xu, j., and Cheng, X. (2017). “Deep Rank: A new Deep Architecture for Relevance Ranking in Information Retrieval”. In Proceeding of CIKM.

[16] Diaz, F., Arguello, J., Callan, J., and Crespo, J. (2009). “Sources of evidence for vertical selection”. In Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval (SIGIR '09).

[17] Arens, Y., Knoblock, A., and Shen, M., (1996). “Query Reformulation for Dynamic information Integration,” J. Intelligent Information Systems, S p e c i a l I s s u e o n I n t e l l i g e n t I n f o r m a t i o n Integration,Vol. 6, Nos. 2-3, pp. 99-130.

[18] MacGregor, R., (1990). “The Evolving Technology of Classification-Based Knowledge Representation Systems,” in Principles of Semantic Networks: Explorations in the Representation of Knowledge, pp. 385-400.

[19] F i n i n , T. ( 1 9 9 4 ) . “ K Q M L a s a n A g e n c y Communication Language,” Proc. Third Int’l Conf. Information and Knowledge Management, ACM Press, pp. 456-463.

[20] Arens, Y., and Knoblock, C. (1994). “Intelligent Caching: Selecting, Representing, and Reusing Data in an Information Server,”Proc. Third Int’l Conf. Information and Knowledge Management, Nat’l Inst. of Standards and Technology, pp. 433-438.

[21] Hsu, C. and Knoblock, C. (1996). “Using Inductive Learning to Generate Rules for Semantic Query Optimization,” in Advances in Knowledge Discovery and Data Mining, G. Piatetsky-Shapiro et al., eds., AAAI Press, pp. 201-218.

[22] Owen, K., Alistair, M., Tim, S., and Justin, Z. (1998). “Methodologies for Distributed Information Retrieval”. In Proceeding of ICDCS of the 18th International Conference on Distributed Computing Systems, pp.66.

[23] Duhan, N., Sharma, K., and Bhatia, K., (1009). “Page Ranking Algorithms: A Survey”, Proceedings of the IEEE International Conference on Advance Computing, 978-1-4244-1888-6.

[24] Brin, S., and Page, L., (1998). ‘The Anatomy of a Large Scale Hypertextual Web Search Engine’, Computer Network and ISDN Systems, Vol. 30, Issue 1-7, pp. 107-117.

[25] Brin, S., and Page, L., Rajeev, M., Terry, W., (1998). ‘The PageRank Citation Ranking: Bring Order to the ‘. Technical report in Stanford University.

Downloads

How to Cite

Al-akashi, F., & Inkpen, D. (2021). A New Approach of Intelligent Data Retrieval Paradigm. Artificial Intelligence Advances, 3(2), 1–12. https://doi.org/10.30564/aia.v3i2.3219

Issue

Vol. 3 , Iss. 2 (October 2021)

Article Type

Article
Crossref
Scopus
Google Scholar
Europe PMC