research-article

An Ontology-Based Query Expansion for an Agricultural Expert Retrieval System

Authors:

Maleerat Sodanil,

Pilapan Phonarin,

Nalinpat PorrawatpreyakornAuthors Info & Claims

IIWAS '13: Proceedings of International Conference on Information Integration and Web-based Applications & Services

Pages 358 - 362

https://doi.org/10.1145/2539150.2539220

Published: 02 December 2013 Publication History

Abstract

Query expansion is a technique of information retrieval system which considered to the context of the user's queries in order to improve the retrieval effectiveness. There are several method expansion methods have been investigated. An ontology-based approach is one of the expansion approaches which used as knowledge-based for searching. This paper proposed an ontology-based query expansion using a concept of the combination of an IR technique and association rule mining to examine and evaluate of an agricultural expert retrieval systems. An association rule mining is applied in the inference engine in order to optimize the user's queries. A set of inference rules are also created to support the expertise retrieval task. The result set is depends on the new query which expands from user's queries as keywords using an agricultural ontology structures. The experts who have expertise in topics or keywords, type of plants and problem solving are the elements of the ontology and will be used to search the relevant publications. The experiments were conducted using publications from collections of the Thai National AGRIS center. The results show that the improvement of this expansion method yields better performance than using basic query expansion search with the F-measure equal to 98.87%.

References

[1]

A.H, L., 2009. A boolean model in information retrieval for search engines. In Proceedings of the ICIME '2009 (2009), 385--389.

Digital Library

[2]

Fonseca, B. M.; Golgher, P.; and Possas, B., 2005. Concept-based interactive query expansion. In Proceedings of the ACM international conference on Information and knowledge management (2005), 696--703.

Digital Library

[3]

F.Durao, K.Bayyapu, G.Xu, P.Dolog and R.Lage, 2011. Using tag-neighbors for query expansion in medical information retrieval. In Proceedings of the International Conference on Information Science and Applications (ICISA) (2011), 1--9.

Digital Library

[4]

H.Cui, J. Wen, J.Nie, and W.Ma, 2003. Query expansion by mining user logs. IEEE Transactions on Knowledge and Data Engineering 15, 4.

Digital Library

[5]

J.Wen, N.Lao, W.Ma, 2004. Probabilistic model for contextual retrieval. In Proceedings of the SIGI, 2004 (2004), 57--63.

Digital Library

[6]

Z.Y.e., 2010. Revisiting rocchio's relevance feedback algorithm for probabilistic models. Information Retrieval Technology Lecture Notes in Computer Science, 151--161.

[7]

H.C.e., 2003. Query expansion by mining user logs. IEEE Transactions on Knowledge and Data Engineering 15, 4, 829--839.

Digital Library

[8]

A. Segura N., S.-S., Elena García-Barriocanal b, Manuel Prieto, 2011. An empirical analysis of ontology-based query expansion for learning resource searches using merlot and the gene ontology. Knowledge-Based Systems 24, 15.

Digital Library

[9]

Eguchi, K., 2005. Query expansion experiments using term dependence models. In In Proceedings of the fifth NTCIR.

[10]

Gruber, T.R., 1993. A translation approach to portable ontologies. Knowledge Acquisition 5, 2, 199--220.

Digital Library

[11]

Guihong Cao, J.-Y.N., Jianfeng Gao and Stephen Robertson, 2008. Selecting good expansion terms for pseudo-relevance. In The 31st Annual International ACM SIGIR Conference, 243--250.

Digital Library

[12]

J. Wen, J.N., and H. Zhang, 2002. Query clustering using user logs. ACM Trans. Information Systems 20, 1, 59--81.

Digital Library

[13]

J. Bhogal, A.M., P. Smith, 2007. A review of ontology based query expansion. Information Processing and Management 43, 21.

Digital Library

[14]

Kamel, Y.-Q.M.a.M., 2011. Pairwise optimized rocchio algorithm for text categorization. Journal Pattern Recognition Letters 32, 2, 375--382.

Digital Library

[15]

Karamuftuoglu, O.V.a.M., 2007. Query expansion with terms selected using lexical cohesion analysis of documents. Information Processing and Management 43, 849--865.

Digital Library

[16]

Peng, X., 2010. Automated chinese essay scoring using vector space models. In Proceedings of the Universal Communication Symposium (IUCS) (2010), 149- 153.

[17]

P.Phonarin, S.N., and C.Haruechaiyasak, 2012. Agrix: An ontology based agricultural expertise retrieval framework. Advanced Materials Research 403--408.

[18]

Vakkari, S.a., 2004. Subject knowledge improves interactive query expansion assisted by a thesaurus. Journal of Documentation 60, 6, 18.

Cited By

Pinet FPapajorgji P(2019)Research in Agricultural and Environmental Information SystemsGeospatial Intelligence10.4018/978-1-5225-8054-6.ch064(1456-1477)Online publication date: 2019
https://doi.org/10.4018/978-1-5225-8054-6.ch064
ENESI FISHAQ OMUHAMMAD BMUHAMMADU T(2019)ENHANCED QUERY EXPANSION ALGORITHM: FRAMEWORK FOR EFFECTIVE ONTOLOGY BASED INFORMATION RETRIEVAL SYSTEMi-manager's Journal on Computer Science10.26634/jcom.6.4.157216:4(1)Online publication date: 2019
https://doi.org/10.26634/jcom.6.4.15721
Raza MMokhtar RAhmad NPasha MPasha U(2019)A Taxonomy and Survey of Semantic Approaches for Query ExpansionIEEE Access10.1109/ACCESS.2019.28946797(17823-17833)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2894679

Index Terms

An Ontology-Based Query Expansion for an Agricultural Expert Retrieval System
1. Applied computing
  1. Computers in other domains
    1. Digital libraries and archives
2. Information systems
  1. Information retrieval
    1. Document representation
    2. Information retrieval query processing
  2. Information systems applications
    1. Digital libraries and archives

Recommendations

Ontology Based Query Expansion with a Probabilistic Retrieval Model
IRFC 2013: Proceedings of the 6th Information Retrieval Facility Conference on Multidisciplinary Information Retrieval - Volume 8201

This paper examines the use of ontologies for defining query context. The information retrieval system used is based on the probabilistic retrieval model. We extend the use of relevance feedback RFB and pseudo-relevance feedback PF query expansion ...
Query expansion with a medical ontology to improve a multimodal information retrieval system

Searching biomedical information in a large collection of medical data is a complex task. The use of tools and biomedical resources could ease the retrieval of the information desired. In this paper, we use the medical ontology MeSH to improve a ...
Query expansion techniques for information retrieval: A survey
Abstract
With the ever increasing size of the web, relevant information extraction on the Internet with a query formed by a few keywords has become a big challenge. Query Expansion (QE) plays a crucial role in improving searches on the ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

IIWAS '13: Proceedings of International Conference on Information Integration and Web-based Applications & Services

December 2013

753 pages

ISBN:9781450321136

DOI:10.1145/2539150

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

@WAS: International Organization of Information Integration and Web-based Applications and Services

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 December 2013

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

IIWAS '13

IIWAS '13: The 15th International Conference on Information Integration and Web-based Applications & Services

December 2 - 4, 2013

Vienna, Austria

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
153
Total Downloads

Downloads (Last 12 months)1
Downloads (Last 6 weeks)0

Reflects downloads up to 08 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Pinet FPapajorgji P(2019)Research in Agricultural and Environmental Information SystemsGeospatial Intelligence10.4018/978-1-5225-8054-6.ch064(1456-1477)Online publication date: 2019
https://doi.org/10.4018/978-1-5225-8054-6.ch064
ENESI FISHAQ OMUHAMMAD BMUHAMMADU T(2019)ENHANCED QUERY EXPANSION ALGORITHM: FRAMEWORK FOR EFFECTIVE ONTOLOGY BASED INFORMATION RETRIEVAL SYSTEMi-manager's Journal on Computer Science10.26634/jcom.6.4.157216:4(1)Online publication date: 2019
https://doi.org/10.26634/jcom.6.4.15721
Raza MMokhtar RAhmad NPasha MPasha U(2019)A Taxonomy and Survey of Semantic Approaches for Query ExpansionIEEE Access10.1109/ACCESS.2019.28946797(17823-17833)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2894679

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten