skip to main content
10.1145/2345396.2345523acmotherconferencesArticle/Chapter ViewAbstractPublication PagesacciciConference Proceedingsconference-collections
research-article

Reformulation of Telugu web query using word semantic relationships

Published: 03 August 2012 Publication History

Abstract

Use of Internet becomes more popular in India to avail the information needs. A major area of Information browsing includes Education, Medical, Agriculture, Geographical, Business and other social domains. Availability of electronic documents for Indian Language is growing day by day. The people living throughout India speak different languages. The government of India has given "languages of the 8th Schedule" official status for 22 languages. Compare to European languages and other Indian languages, processing of Telugu language electronic documents is more difficult in nature. This is due to multi --- encoding formats of the text. Indian languages are encoded using Unicode, ISCII. To fasten the retrieval process the Unicode or ISCII is need to be converted into simple and standard encoding which makes Information Retrieval as easy task. Once the information processing system is build for a mono-lingual, it is the base to go for Multi-lingual and Cross --- lingual information processing. In Information Retrieval process users expects exact results for the given query. It depends on the vocabulary expertization of the end user in building the root query. Word mismatch is common problem of all languages in Information Retrieval process. Query Expansion gives a solution to the word mismatch problem. In Query Expansion the top ranked documents are used to expand the query terms. Sometimes user need to judge the relevance of the expanded query to iterate search. The relevance judgment of the user depends on the knowledge (i.e Language knowledge to describe the con text of the query) of the user. If the con cept hierarchy is properly defined, then user involvement is void in this scenario. This can be easily test on English language, but applying Query Reformulation technique directly on Indian languages is not stands good, because the nature of Indian languages is not simple like English. The Paper is aimed to reduce the mismatch between user query and retrieved documents by using semantic relationships between query terms and document terms. To test the proposed model, Telugu language, one of the Indian languages is taken as a case study. True translation from English to Telugu and vice versa is not possible due to high word conflation in Indian languages. This paper is an attempt to adopt Semantic Network with semantic relationships between terms of a query to reformulate and iterate the search. Method of Relevance Feedback improves recall without compromising precision, but it works well on limited corpus. Reformulation of query by embedding WordNet, ConceptNet relationsh ips gave better results, but great fall of precision is observed. Comparison between initial query test results and reformulated query search results are made in result analysis.

References

[1]
C. Fellbaum, WordNet: An Electronic Lexical Database, the MIT, Press, Cambridge, London, England.
[2]
Chris Buckley, Gerard Salton, James Allan, and Amit Singhal. Automatic query expansion using smart: Trec 3. In TREC, 1994b.
[3]
Current research issues and trends in non-English Web searching, Journal of IR, Vol 12, Issue 3.
[4]
Dimple Patel 1 and Devika P. Madalli, Information Retrieval in Indian Languages: A Case Study of Plural Resolution in Telugu Language, ICSD.
[5]
Ingrid Zukerman, Bhavani Raskutti, Query Expansion and Query Reduction in Document Retrieval, Research Paper, and Australian Research Council grant DP0209565.
[6]
Jian-Yun Nie, Query Expansion and Query Translation as Logical Inference.
[7]
Jin xi Xu, Croft, Query Expansion using Local an d Global Document Analysis.
[8]
Kolikipogu Ramakrishna, B. Padmaja Rani, Information Retrieval in Indian Languages: Query Expansion models for Telugu language as a case study, IEEEIITA2010, china.
[9]
Kolikipogu Ramakrishna, B. Padmaja Rani, WordNet based Term Selection for PRF Query Expansion, ICCMS,Jan 2011,Vol 1, Pp: 127--131.
[10]
Prasad Pingali, Jagadeesh Jagarlamudi, A Dictionary Based Approach with Query Expansion to Cross Language Query Based Multi-Document Summarization: Experiments in Telugu --- English Nation al Worksh op on Artificial Intelligence, Mumbai, India.
[11]
Reza Hemayati, Weiyi Meng and Clement Yu, Semantic-Based Grouping of Search Engine Results Using WordNet, Advances in Data and Web Management.
[12]
Roberto Navigli and Paola Velardi, An Analysis of Ontology-based Query Expansion Strategies
[13]
Rocchio, J. Relevance feedback in information retrieval. In The Smart Retrieval System: Experiments in Automatic Document Processing, G. Salton, Ed. Prentice-Hall, Englewood Cliffs, NJ, 313--323.
[14]
William R. Hersh and David Hickam. Information retrieval in medicine: The SAPHIRE experience. Journal of the American Society for Information S cience, 46:743--747.
[15]
Xiaoyun Wang, User Ontology and Word Sense Disambiguation for Query Expansion, ICCASM.
[16]
Yang Xu, Gareth J. F. Jones., Query Dependent Pseudo Relevance Feedback based on Wikipedia, SIGIR'09.
[17]
Yiming Yang and C G Chute. Words or concepts: the features of indexing units and their optimal use in information retrieval. In Proceedings of the Annual Symposium on Computer Application in Medical Care, pages 685--689, (1993).
[18]
Zhiguo Gong, Chan Wa Cheang, and Leong Hou U, Web Query Expansion by WordNet, LNCS 3588, pp. 166 --- 175.
[19]
V. Uren et al., "Semantic annotation for knowledge management: Requirements and a survey of the state of the art," Web Semantics: Science, Services and Agents on the World Wide Web, vol. 4(1), pp. 14--28.
[20]
M. Fernández et al., "Semantically enhanced Information Retrieval: An ontology-based approach," Web Semantics: Science, Services and Agents on the World Wide Web, in press.
[21]
A. Kiryakov, B. Popov, I. Terziev, D. Manov, D. Ognyanoff, "Semantic annotation, indexing, and retrieval," Web Semantics: Science, Services and Agents on the World Wide Web, vol. 2(1), pp. 49--79.
[22]
A. M. Rinaldi, "An ontology-driven approach for semantic information retrieval on the Web," ACM Transactions on Internet Technology (TOIT), vol. 9(3), article no. 10.
[23]
Xing Jiang, A. H. Tan, "Learning and inferencing in user ontology for personalized Semantic Web search," Information Sciences, vol. 179(16), pp. 2794--2808.
[24]
Davide Buscaldi, Paolo Rosso, Emilio Sanchis Arnal, A WordNet-based Query Expansion method for Geographical Information Retrieval, August --- 31, 2005.
[25]
Atanas Kiryakov, Borislav Popov, Damyan Ognyanoff, Dimitar Manov, Angel, Kirilov, Miroslav Goranov, Semantic Annotation, Indexing, and Retrieval, ISWC, 2004.
[26]
Jinxi Xu and W. Bruce Croft, Query Expansion Using Local and Global Document Analysis.
[27]
Deerwester, S., Dumais, S., Furnas, G., Landauer, T.l & Harshman, R. Indexing by latent semantic analysis. Journal of the American Society for information Science, 41,391--407.
[28]
Jinxi Xu and W. Broce Croft, Improving the Effectiveness of Informat ional R etrieval with Local Context An alysis, SIGIR96.
[29]
Relevance feedback and query Expansion, DRAFT! © April 1, 2009 Cambridge University Press. Feedback welcome.
[30]
F.A. Grootjen, Th.P. van der Weide, Conceptual Query Expansion, Preprint submitted to Data & Knowledge Engineering 28 February 2005.
[31]
R. Mandala, T. Tokunaga, H. Tanaka, Combining multiple evidence from different types of thesaurus for query expansion, in: SIGIR '99: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Developmen t in Information Retrieval, August 15-19, 1999, Berkeley, CA, USA,ACM, 1999, pp. 191--197.
[32]
E. Fox, Lexical relations enhancing effectiveness of information retrieval systems, SIGIR Forum 26 (5) 629--640.
[33]
E. Voorhees, Query expansion using lexical-semantic relations, in: SIGIR '94: Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, 1994, pp. 61--- 69.
[34]
M Ganapathiraju, TelMore: Morphological Generator for Telugu Nouns and Verbs, www.ulib.org/conference/2006/7.pdf.
[35]
Resnik, P, Using Information Content to Evaluate Semantic Similarity in Taxonomy, In Proceedings of the 14th International Joint Conference on Artificial Intelligence (IJCAI-95), pp. 448--453.

Cited By

View all
  • (2019)A Taxonomy and Survey of Semantic Approaches for Query ExpansionIEEE Access10.1109/ACCESS.2019.28946797(17823-17833)Online publication date: 2019
  • (2013)Tuning of Expansion Terms by PRF and WordNet Integrated Approach for AQEProceedings of the First International Conference on Mining Intelligence and Knowledge Exploration - Volume 828410.1007/978-3-319-03844-5_63(640-651)Online publication date: 18-Dec-2013
  1. Reformulation of Telugu web query using word semantic relationships

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image ACM Other conferences
      ICACCI '12: Proceedings of the International Conference on Advances in Computing, Communications and Informatics
      August 2012
      1307 pages
      ISBN:9781450311960
      DOI:10.1145/2345396
      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]

      Sponsors

      • ISCA: International Society for Computers and Their Applications
      • RPS: Research Publishing Services

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 03 August 2012

      Permissions

      Request permissions for this article.

      Check for updates

      Author Tags

      1. Boolean model
      2. POS tagging
      3. WordNet
      4. indic scripts
      5. information retrievals
      6. lemmatization
      7. query reformulation
      8. semantic network
      9. synset
      10. tokenization
      11. vector space model
      12. web query

      Qualifiers

      • Research-article

      Conference

      ICACCI '12
      Sponsor:
      • ISCA
      • RPS

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • Downloads (Last 12 months)1
      • Downloads (Last 6 weeks)0
      Reflects downloads up to 17 Feb 2025

      Other Metrics

      Citations

      Cited By

      View all
      • (2019)A Taxonomy and Survey of Semantic Approaches for Query ExpansionIEEE Access10.1109/ACCESS.2019.28946797(17823-17833)Online publication date: 2019
      • (2013)Tuning of Expansion Terms by PRF and WordNet Integrated Approach for AQEProceedings of the First International Conference on Mining Intelligence and Knowledge Exploration - Volume 828410.1007/978-3-319-03844-5_63(640-651)Online publication date: 18-Dec-2013

      View Options

      Login options

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      Figures

      Tables

      Media

      Share

      Share

      Share this Publication link

      Share on social media