article

A review of ontology based query expansion

Authors:

A. Macfarlane, and

P. SmithAuthors Info & Claims

Information Processing and Management: an International Journal, Volume 43, Issue 4

Pages 866 - 886

https://doi.org/10.1016/j.ipm.2006.09.003

Published: 01 July 2007 Publication History

Abstract

This paper examines the meaning of context in relation to ontology based query expansion and contains a review of query expansion approaches. The various query expansion approaches include relevance feedback, corpus dependent knowledge models and corpus independent knowledge models. Case studies detailing query expansion using domain-specific and domain-independent ontologies are also included. The penultimate section attempts to synthesise the information obtained from the review and provide success factors in using an ontology for query expansion. Finally the area of further research in applying context from an ontology to query expansion within a newswire domain is described.

References

[1]

Scalable feature selection, classification and signature generation for organizing large text databases into hierarchical topic taxonomies. VLDB Journal. v7 i3. 163-178.

[2]

Integration of domain-specific and domain-independent ontologies for colonoscopy video database annotation. In: Proceedings of the international conference on information and knowledge engineering, June 21-24, CSREA Press. pp. 82-90.

[3]

Barzilay, R., & Elhadad, M. (1997). Using lexical chains for text summarization. In Proceedings of the intelligent scalable text summarization workshop, ISTS'97.

[4]

Bateman, J. (2005). John Bateman's ontology Portal {online}. Available from http://www.fb10.uni-bremen.de/anglistik/langpro/webspace/jb/info-pages/ontology/ontology-root.htm {20/07/05}.

[5]

After the Dot-Bomb: getting information retrieval right this time. First Monday - Peer Reviewed journal.

[6]

Bauer, T., & Leake, D. (2001). Real time user context modelling for information retrieval agents, CIKM 2001.

[7]

Baziz, M. et al. (2005). Conceptual indexing based on document content representation information context: nature, impact, and role. In 5th International conference on conceptions of library and information sciences, CoLIS 2005, June 4-8 (p. 171).

[8]

Bentivogli, L., & Pianta, E. (2004). Extending WordNet with syntagmatic information. In Proceedings of the second global WordNet conference, January 20-23 (pp. 47-53).

[9]

SearchPad: explicit capture of search context to support web search. WWW9/Computer Networks. v33 i1-6. 493-501.

[10]

Bhavnani, S. et al. (2001). Towards a unified framework of IR tasks and strategies. In Proceedings of ASIST. (pp 340-354).

[11]

Billerbeck, B., & Zobel, J. (2004). Questioning query expansion: an examination of behaviour and parameters. In Proceedings of the fifteenth australasian database conference, ADC 2004, CRPIT 18-22 January 2004, Australian Computer Society.

[12]

A taxonomy of Web search. SIGIR Forum. v36 i2. 3-10.

[13]

Buckland, M. (2003). Translingual information management using domain ontologies. {online} Available from http://metadata.sims.berkeley.edu/GrantSupported/tides.html.

[14]

Buckley, C. et al. (1995). Automatic query expansion using SMART: TREC 3. In Proceedings of the third text retrieval conference, NIST Special Publication 500-225 (pp. 69-80).

[15]

Callan, J. et al. (1992). The INQUERY Retrieval System DEXA (pp. 78-83).

[16]

Generating, integrating and activating thesauri for concept-based document retrieval. IEEE Expert. 25-34.

[17]

Using perception in managing unstructured documents. Crossroads archive. v10 i2. 5

[18]

Chu, W. et al. (2002). Textual document indexing and retrieval via knowledge sources and data mining communication of the Institute of Information and Computing Machinery (CIICM).

[19]

Climent, S. et al. (1996). Definitions of the links and subsets for Nouns of the EuroWordNet Project. In EuroWordNet, E.C. LE2-4003 Deliverable 005 Universitat de Barcelona, Universitat Politècnica de Catalunya, Universidad Nacional de Educación a Distancia. Barcelona, Madrid.

[20]

Coates-Stephens, S. (1991). Analysis and acquisition of proper names for robust text understanding, Computer Science, City University, London.

[21]

Special issue: Ontology applications and design. Communications of the ACM. v45 i2. 39-65.

[22]

Croft, B. et al. (2001). Relevance feedback and personalization: A language modelling perspective DELOS workshop: personalisation and recommended systems in Diglibs.

[23]

Query expansion by mining user logs. IEEE Transactions on Knowledge and Data Engineering. v15 i4.

[24]

Davies, J., & Weeks, R. (2004). QuizRDF: Search technology for the semantic web. In Proceedings of the 37th Hawaii international conference on system sciences.

[25]

Indexing by latent semantic analysis. Journal of the Society for Information Science. v41 i6. 391-407.

[26]

Efthimiadis, E. (1992). Interactive query expansion and relevance feedback for document retrieval systems. London, City University.

[27]

Efthimiadis, E. (1996). Query expansion. In M.E. Williams (Ed.), Annual review of information systems and technology (ARIST). vol. v31. Information Today (pp. 121-187).

[28]

Eguchi, K. (2005). Query expansion experiments using term dependence models. In Proceedings of the fifth NTCIR workshop meeting on evaluation of information access technologies: information retrieval, question answering and cross-lingual information access.

[29]

Linking in context. Journal of Digital Information. v2 i3.

[30]

WordNet: An electronic lexical database and some of its applications. MIT Press, Cambridge, MA.

[31]

Placing search in context: the concept revisited. TOIS. v20 i1. 116-131.

[32]

Fu, G. et al. (2005). Ontology-Based Spatial Query Expansion in Information Retrieval ODBASE: OTM Confederated International Conferences, 4 November 2005.

[33]

Fu, L. et al. (2005). Evaluating the effectiveness of a collaborative querying environment. In Proceedings of the 8th international conference on asian digital libraries ICADL. Lecture notes in computer science (pp. 342-351).

[34]

Gangemi, A. et al. (2001). Conceptual analysis of lexical taxonomies: the case of WordNet top-level. In Proceedings ACM 2nd international conference on formal ontology in information systems, FOIS 2001, October 17-19 (pp. 285-296).

[35]

Gonzalo, J. et al. (1998). Indexing with WordNet synsets can improve text retrieval Coling-ACL 98.

[36]

Conceptual query expansion. Data and Knowledge Engineering. v56 i2. 174-193.

[37]

Grover, C. et al. (2003). Use of ontologies for cross-lingual information management in the web. In Proceedings of the ontologies and information extraction international workshop held as part of the EUROLAN 2003, July 28-August 8, 2003.

[38]

A translation approach to portable ontologies. Knowledge Acquisition. v5 i2. 199-220.

[39]

An evaluation of automatic query expansion in an online library catalogue. Journal of Documentation. v48 i4. 406-421.

[40]

Harabagiu, S. et al. (1999). WordNet 2 a morphologically and semantically enhanced resource. In Proceedings of SIGLEX-99, June 1999 (pp. 1-8).

[41]

Harman, D. K. (1988). Towards interactive query expansion annual ACM conference on research and development in information retrieval archive. In: Proceedings of the 11th annual international ACM SIGIR conference on research and development in information retrieval (pp. 321-331).

[42]

. In: Relevance feedback revisited ACM SIGIR 15th conference on research and development in information retrieval, June 21-24, ACM Press. pp. 1-10.

[43]

Hearst, M. (1992). Automatic acquisition of hyponyms from large text corpora. In 14th International conference on computational linguistics. France.

[44]

Hersh, W. et al. (2003). Phrases, Boosting, and Query Expansion Using External Knowledge Resources for Genomic Information Retrieval TREC (pp. 503-509).

[45]

Hoashi, K. et al. (2001). Query Expansion Based on Predictive Algorithms for Collaborative Filtering, SIGIR (pp. 414-415).

[46]

Huang, L. (2000). A survey on web information retrieval technologies. In ECSL. State University of New York, New York.

[47]

A dual index model for contextual information retrieval. In: Proceedings of the 28th annual international ACM SIGIR conference on research and development in information retrieval, ACM Press. pp. 613-614.

[48]

Hwang, C. H. (1999). Incompletely and imprecisely speaking: Using dynamic ontologies for representing and retrieving information. In Proceedings of the 6th international workshop on knowledge representation meets databases (KRDB'99), July 29-30.

[49]

Hyvonen, E. (2003). Ontology based image retrieval. In www2003 (pp. 199).

[50]

Information seeking research needs extension towards tasks and technology. Information Research. v10 i1.

[51]

ExpansionTool: concept-based query expansion and construction. Information Retrieval. i4. 231-255.

[52]

Jones, G. J. F., & Brown, P. J. (2003). Context-aware retrieval for ubiquitous computing environments. In Mobile HCI workshop on mobile and ubiquitous information access (pp. 227-243).

[53]

A thesaurus data model for an intelligent retrieval system. Journal of Information Science. v19. 167-178.

[54]

Interactive thesaurus navigation: intelligence rules OK?. Journal of the American Society for Information Science. v46 i1. 52-59.

[55]

Query type classification for web document retrieval. In: Proceedings of the 26th annual international ACM SIGIR conference on research and development in information retrieval, annual ACM conference on research and development in information retrieval, ACM Press. pp. 64-71.

[56]

Kashyap, V. (2001). Design and creation of ontologies for environmental information retrieval. In AOS Workshop, Rome, November 2001.

[57]

Khan, L., & Wang, L. (2002). Automatic ontology derivation using clustering for image classification. In 8th International w/s on Multimedia information systems, October (pp. 56-65).

[58]

Improving document transformation techniques with collaborative learned term-based concepts. In: Reading and learning: adaptive content recognition, Vol. 2956. Springer-Verlag, Berlin, Heidelberg, New York. pp. 281-305.

[59]

Lexical ambiguity in information retrieval. ACM Transactions on Information Systems. v10 i2. 115-141.

[60]

Lame, G. (2003). Using text analysis techniques to identify legal ontologie's components Legont2003, ICAIL 2003 w/s Legal ontologies and web base legal information management.

[61]

Leger, A. et al. (2001). MKBEEM-Developing multilingual knowledge-based marketplace {online} (46). Available from http://mkbeem.elibel.tm.fr/ {20th Dec}.

[62]

Customizable and ontology-enhanced medical information retrieval interfaces. Methods of Info in Medicine.

[63]

Word-word associations in document retrieval systems. American Documentation. v20 i1. 27-38.

[64]

Lifantsev, M. (1998). The Open GRiD Project {online}. Available from http://www.ecsl.cs.sunysb.edu/~maxim/OpenGRiD/ {Feb 2006}.

[65]

Lin, D. (1999). Automatic identification of non-compositional phrases. In Proceedings of ACL, 1999 (pp. 317-324).

Digital Library

[66]

Liu, X., & Croft, B. (2002). Passage retrieval based on language models. In CIKM 2002.

Digital Library

[67]

Thesaurus-based structural thematic summary in multilingual information systems. Machine Translation Review. i11. 10-20.

[68]

Lowe, D. (2000). Improving web search relevance: using navigational structures to provide search context {online}. Available from http://ausweb.scu.edu.au/aw2k/papers/lowe/paper.html.

[69]

Magnini, B., & Speranza, M. (2002). Merging global and specialized linguistic ontologies. In Proceedings of the workshop Ontolex-2002 ontologies and lexical knowledge bases, LREC-2002 (pp. 43-48).

[70]

Mandala, R. et al. (1998). In The use of WordNet in information retrieval use of WordNet in natural language processing systems: Proceedings of the conference (pp. 191-197).

[71]

Combining multiple evidence from different types of thesaurus for query expansion. Research and Development in Information Retrieval.

[72]

Imprecise answers in distributed environments: estimation of information loss for multi-ontology based query processing. International Journal of Cooperative Information Systems.

[73]

Merriam-Webster Online dictionary {online}. Available from http://www.m-w.com/dictionary/context {02/01/06}.

[74]

Lexical cohesion computed by thesaural relations as an indicator of the structure of text. Computational Linguistics. v17. 21-43.

[75]

Lexical cohesion, the thesaurus, and the structure of text. Computational linguistics. v17 i1. 21-48.

[76]

Navigli, R., & Velardi, P. (2003). An analysis of ontology-based query expansion strategies workshop on adaptive text extraction and mining (ATEM 2003). In 14th European conference on machine learning (ECML 2003), September 22-26.

[77]

Nilsson, K. et al. (2005). SUiS - cross-language ontology-driven information retrieval in a restricted domain. In Proceedings of the 15th NODALIDA conference.

[78]

O'Donovan, J., & Smyth, B. (2005). Trust in recommender systems. In Proceedings of the 10th international conference on Intelligent user interfaces (pp. 167-174).

[79]

Ogawa, Y. et al. (2000). Structuring and expanding queries in the probabilistic model. In TREC 2000, November 13-16.

[80]

O'Sullivan, D. et al. (1995). Augmenting the Princeton WordNet with a domain specific ontology. In Proceedings of the workshop on basic ontological issues in knowledge sharing, international joint conference on artificial intelligence (IJCAI-95), August 19-20.

[81]

Pavel, S. et al. (2003). Thesauri and ontologies for digital libraries. In Proceedings of the 5th Russian conference on digital libraries RCDL.

[82]

The limitations of term co-occurrence data for query expansion in document retrieval systems. American Society for Information Science and Technology (JASIST). v42 i5. 378-383.

[83]

Pustejovsky, J. (1995). The core lexical engine: The contextual determination of word sense {online} Available from http://www.cs.tufts.edu/~jacob/isgw/Pustejovsky.html.

[84]

Qui, Y., & Frei, H. (1993). Concept base query expansion. In Proceedings of the sixteenth annual international ACM-SIGIR conference on research and development in information retrieval (pp. 160-169).

[85]

Just-in-time information retrieval agents. IBM Systems Journal. v39 i3&4.

[86]

A comparison of spelling-correction methods for the identification of word forms in historical text databases. Literary & Linguistic Computing. v8 i3. 143-152.

[87]

On relevance weight estimation and query expansion. Journal of Documentation. v42 i3. 182-188.

[88]

On term selection for query expansion. Journal of Documentation. v46 i4. 359-364.

[89]

Relevance weighting of search terms. Journal of the American Society of Information Science. v27. 129-146.

[90]

. In: Rocchio, R. (Ed.), Relevance feedback in information retrieval, Prentice-Hall, Englewood Cliffs NJ.

[91]

Ruthven, I. (2004). "¿ and this set of words represents the user's context ¿" Sigir, Information retrieval in context workshop, ACM.

[92]

A survey on the use of relevance feedback for information access systems. The Knowledge Engineering Review archive. v18 i2. 95-145.

[93]

Semantic enrichment of a web legal information retrieval system Jurix 2002. In: Conference on legal knowledge and IS, December 16-17, IOS press. pp. 11-20.

[94]

Automatic text processing: The transformation, analysis, and retrieval of information by computer. Addison-Wesley, Reading, MA.

[95]

Introduction to modern information retrieval. Mcgraw Hill, New York.

[96]

Sanderson, M. (1994). Word sense disambiguation and information retrieval. In Proceedings of the 17th ACM SIGIR conference (pp. 142-151).

[97]

A study of user interaction with a concept based interactive query expansion support tool (CiQuest) which is integrated into Okapi. In: Lecture notes in computer science, Springer-verlag, Heidelberg. pp. 42-56.

Digital Library

[98]

. In: Sanderson, M., Lawrie, D. (Eds.), Building, testing and applying concept hierarchies, Kluwer Academic Publishers.

[99]

Schatz, B.R., et al. (1996). In Interactive term suggestion for users of digital libraries ACM digital library conference.

[100]

Scott, S., & Matwin, S. (1998). In Text classification using WordNet Hypernyms Coling - ACL workshop. Usage of WordNet in NLP Systems.

[101]

Principles, procedures and rules in an expert system for information retrieval. Information Processing and Management. v21. 475-487.

[102]

Subject knowledge improves interactive query expansion assisted by a thesaurus. Journal of Documentation. v60 i6. 673-690.

[103]

The retrieval effects of query expansion on a feedback document retrieval system. Computer Journal. v26 i3. 239-246.

[104]

The rise of ontologies or the reinvention of classification. Journal of the American Society for Information Science. v50 i12. 1119-1120.

[105]

An evaluation of query expansion by addition of clustered terms for a document retrieval system. Information Storage and Retrieval. v9 i6. 339

[106]

A probabilistic model of information retrieval: development and status. Information Processing and Management. v36 i6. 809-840.

[107]

Study of interactive feedback during mediated information retrieval. JASIS. v48 i5. 382-394.

[108]

Textual context analysis for information retrieval. SIGIR. 140-147.

[109]

Ontology as a search-tool: A study of real users' query formulation with and without conceptual support. In: ECIR 2005, Springer-Verlag, Berlin, Heidelberg. pp. 315-329.

[110]

Tombros, A., et al. (2001) A study on the use of summaries and summary based query expansion for a question answering task. In 23rd BCS European annual colloquium on IR research (ECIR).

[111]

Tombros, A., & Sanderson, M. (1998). the advantages of query-biased summaries in information retrieval. In 21st annual international ACM SIGIR (pp. 2-10).

[112]

Toms, E., et al. (2004). Identifying the significant contextual factors of search, SIGIR. In Information retrieval in context workshop.

[113]

Torrissen, B. C. (1998). Dewey goes surfing: Agent-based information retrieval and classification support. Norwegian University of Science and Technology.

[114]

Query Exhaustivity, relevance feedback and search success in automatic and interactive query expansion. American Society for Information Science and Technology (JASIST). v60 i2.

[115]

Query expansion with long-span collocates Information Retrieval. Kluwer academic publishers.

[116]

Using wordnet to disambiguate word senses for text retrieval. ACM SIGIR. 171-180.

[117]

Voorhees, E. (1994). Query expansion using lexical-semantic relations. In Proceedings of the 17th annual international ACM SIGIR conference on research and development in information retrieval table of contents (pp. 61-69).

[118]

Walker, S., & Jones, R. (1987). Improving subject retrieval in online catalogues: 1. Stemming, automatic spelling correction and cross-reference tables. British Library, London.

[119]

Improving effectiveness of information retrieval with local context analysis. ACM Transactions on Information Systems. v18 i1. 79-112.

[120]

Efficient and effective metasearch for text databases incorporating linkages among documents. SIGMOD.

Cited By

Abdollahi SKuculo TGottschalk S(2024)Event-Specific Document Ranking Through Multi-stage Query Expansion Using an Event Knowledge GraphAdvances in Information Retrieval10.1007/978-3-031-56060-6_22(333-348)Online publication date: 24-Mar-2024
https://dl.acm.org/doi/10.1007/978-3-031-56060-6_22
Aklouche BBounhas ISlimani Y(2023)A discriminative method for global query expansion and term reweighting using co-occurrence graphsJournal of Information Science10.1177/016555152199804749:1(183-206)Online publication date: 1-Feb-2023
https://dl.acm.org/doi/10.1177/0165551521998047
Chatterjee U(2023)Context-based understanding of food-related queries using a culinary knowledge modelJournal of Information Science10.1177/0165551521102216349:3(831-852)Online publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1177/01655515211022163
Show More Cited By

Index Terms

A review of ontology based query expansion
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information retrieval
    1. Evaluation of retrieval results

Recommendations

Lexical Co-Occurrence and Contextual Window-Based Approach with Semantic Similarity for Query Expansion

Query expansion QE is an efficient method for enhancing the efficiency of information retrieval system. In this work, we try to capture the limitations of pseudo-feedback based QE approach and propose a hybrid approach for enhancing the efficiency of ...
Read More
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 ...
Read More
Effects of Using a Research Context Ontology for Query Expansion
ESWC 2009 Heraklion: Proceedings of the 6th European Semantic Web Conference on The Semantic Web: Research and Applications

This thesis investigates the question whether and how ontologies such as the ones currently evolving in the Semantic Web can serve as knowledge structures for the generation of query expansion terms in information retrieval systems. This issue is ...
Read More

Comments

Information & Contributors

Information

Published In

cover image Information Processing and Management: an International Journal

Information Processing and Management: an International Journal Volume 43, Issue 4

July, 2007

303 pages

ISSN:0306-4573

Issue’s Table of Contents

Copyright © Elsevier Ltd © 2006.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 01 July 2007

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

90
Total Citations
View Citations
0
Total Downloads

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

Other Metrics

View Author Metrics

Citations

Cited By

Abdollahi SKuculo TGottschalk S(2024)Event-Specific Document Ranking Through Multi-stage Query Expansion Using an Event Knowledge GraphAdvances in Information Retrieval10.1007/978-3-031-56060-6_22(333-348)Online publication date: 24-Mar-2024
https://dl.acm.org/doi/10.1007/978-3-031-56060-6_22
Aklouche BBounhas ISlimani Y(2023)A discriminative method for global query expansion and term reweighting using co-occurrence graphsJournal of Information Science10.1177/016555152199804749:1(183-206)Online publication date: 1-Feb-2023
https://dl.acm.org/doi/10.1177/0165551521998047
Chatterjee U(2023)Context-based understanding of food-related queries using a culinary knowledge modelJournal of Information Science10.1177/0165551521102216349:3(831-852)Online publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1177/01655515211022163
Poppink BFrasincar FRobal T(2023)An experimental study on re-ranking web shop search results using semantic segmentation of user profilesElectronic Commerce Research and Applications10.1016/j.elerap.2023.10131062:COnline publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1016/j.elerap.2023.101310
Jain MSuvarna AJain A(2022)An evolutionary game theory based approach for query expansionMultimedia Tools and Applications10.1007/s11042-021-11297-x81:2(1971-1995)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1007/s11042-021-11297-x
Sharma DPamula RChauhan D(2022)A contemporary combined approach for query expansionMultimedia Tools and Applications10.1007/s11042-020-09172-281:24(35195-35221)Online publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1007/s11042-020-09172-2
Tamine LGoeuriot L(2021)Semantic Information Retrieval on Medical TextsACM Computing Surveys10.1145/346247654:7(1-38)Online publication date: 17-Sep-2021
https://dl.acm.org/doi/10.1145/3462476
Xu QWu S(2021)Improving Medical Record Search Performance by Particle Swarm Optimization Based Data Fusion TechniquesWeb Information Systems and Applications10.1007/978-3-030-87571-8_8(87-98)Online publication date: 24-Sep-2021
https://dl.acm.org/doi/10.1007/978-3-030-87571-8_8
Baglioni MManghi PMannocci A(2020)Context-Driven Discoverability of Research DataDigital Libraries for Open Knowledge10.1007/978-3-030-54956-5_15(197-211)Online publication date: 25-Aug-2020
https://dl.acm.org/doi/10.1007/978-3-030-54956-5_15
Bonab HAliannejadi MFoley JAllan JFang YZhang YAllan JBalog KCarterette BGuo J(2019)Incorporating Hierarchical Domain Information to Disambiguate Very Short QueriesProceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval10.1145/3341981.3344251(51-54)Online publication date: 26-Sep-2019
https://dl.acm.org/doi/10.1145/3341981.3344251
Show More Cited By

View Options

View options

Get Access

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

Media

Figures

Other

Tables

View Issue’s Table of Contents