research-article

Web Search Results Visualization: Evaluation of Two Semantic Search Engines

Authors:

Kalliopi Kontiza and

Antonis BikakisAuthors Info & Claims

WIMS '14: Proceedings of the 4th International Conference on Web Intelligence, Mining and Semantics (WIMS14)

June 2014

Article No.: 39, Pages 1 - 12

https://doi.org/10.1145/2611040.2611057

Published: 02 June 2014 Publication History

Abstract

Semantic search engines improve the accuracy of search results by understanding the meaning and context of terms as they appear in web documents. But do they also improve the presentation of search results? We discuss this research question and attempt to address it by evaluating two semantic search engines: Sig.ma and Kngine. Our analysis is based on the theory and methods of explorative evaluation studies, the inspection evaluation approach in information visualization, user-oriented evaluation studies, and on quantitative data analysis. Our main conclusion is that visualization used in semantic search engines improves the understanding of search results and the overall search experience by exploiting the semantics of web data.

References

[1]

Ahn, J. and Brusilovsky, P. "Adaptive visualization of search results: Bringing user models to visual analytics," Inf. Vis., vol. 8, no. 3, pp. 167--179, 2009.

Digital Library

[2]

Al-Maskari, A. and Sanderson, M. "A review of factors influencing user satisfaction in information retrieval," J. Am. Soc. Inf. Sci. Technol., vol. 61, no. 5, pp. 859--868, May 2010.

Digital Library

[3]

Al-Maskari, A. and Sanderson, M. "The effect of user characteristics on search effectiveness in information retrieval," Inf. Process. Manag., vol. 47, no. 5, pp. 719--729, Sep. 2011.

Digital Library

[4]

Antoniou, G. and Van Harmelen, F. A semantic Web primer, Second Edition. Cambridge, Mass.: MIT Press, 2008.

Digital Library

[5]

Bar-Ilan, J., Keenoy, K., Levene, M. and Yaari, E. "Presentation bias is significant in determining user preference for search results-A user study," J. Am. Soc. Inf. Sci. Technol., vol. 60, no. 1, pp. 135--149, Jan. 2009.

Digital Library

[6]

Bates, M. J. Understanding information retrieval systems: management, types, and standards. Boca Raton: CRC Press, Taylor & Francis Group, 2012.

[7]

Broder, A. "A taxonomy of web search," in ACM Sigir forum, 2002, vol. 36, pp. 3--10.

Digital Library

[8]

Buckley, C. and Voorhees, E. M. "Evaluating evaluation measure stability," in Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval, 2000, pp. 33--40.

Digital Library

[9]

Card, S. K., Mackinlay, J. D. and Shneiderman, B. Readings in information visualization: using vision to think. San Francisco, Calif.: Morgan Kaufmann Publishers, 1999.

Digital Library

[10]

Carpendale, S. "Evaluating information visualizations," in Information Visualization, Springer, 2008, pp. 19--45.

Digital Library

[11]

Chen, C. Information visualization: beyond the horizon. London; New York: Springer, 2004.

Digital Library

[12]

Chen, Y., Jeon, G. Y. and Kim, Y.-M. "A Day without a Search Engine: An Experimental Study of Online and Offline Searches," 2013.

[13]

Dadzie A.-S. and Rowe, M. "Approaches to visualising linked data: A survey," Semantic Web, vol. 2, no. 2, pp. 89--124, 2011.

Digital Library

[14]

d' Aquin, M., Baldassarre, C., Gridinoc L., Angeletou, S., Sabou, M. and Motta, E. "Characterizing knowledge on the semantic web with watson," 2007.

[15]

Esmaili, K. S. and Abolhassani, H. "A Categorization Scheme for Semantic Web Search Engines.," in AICCSA, 2006, pp. 171--178.

Digital Library

[16]

Fazzinga, B. and Lukasiewicz, T. "Semantic search on the Web," Semantic Web, vol. 1, no. 1, pp. 89--96, 2010.

Digital Library

[17]

Fekete, J.-D., Van Wijk, J. J., Stasko, J. T. and C. North, "The value of information visualization," in Information Visualization, Springer, 2008, pp. 1--18.

Digital Library

[18]

Fielding, N., Lee, R. M. and Blank, The SAGE handbook of online research methods. Los Angeles; London: SAGE, 2008.

[19]

Garoufallou, E. "Evaluating search engines: A comparative study between international and Greek SE by Greek librarians," Program Electron. Libr. Inf. Syst., vol. 46, no. 2, pp. 182--198, 2012.

[20]

Girit, H., Eberhard, R., Michelberger, B. and Mutschler, B. "On the Precision of Search Engines: Results from a Controlled Experiment," in Business Information Systems, 2012, pp. 201--212.

[21]

Guha, R., McCool, R. and Miller, E. "Semantic search," in Proceedings of the 12th international conference on World Wide Web, 2003, pp. 700--709.

Digital Library

[22]

Heath, T. and Motta, E. "Revyu: Linking reviews and ratings into the Web of Data," Web Semant. Sci. Serv. Agents World Wide Web, vol. 6, no. 4, pp. 266--273, Nov. 2008.

Digital Library

[23]

Jaeschke, G., Leissler, M. and Hemmje, M. "Modeling interactive, 3-dimensional information visualizations supporting information seeking behaviors," in Knowledge and Information Visualization, Springer, 2005, pp. 119--135.

Digital Library

[24]

Joho, H and Jose, J. M. "Effectiveness of additional representations for the search result presentation on the web," Inf. Process. Manag., vol. 44, no. 1, pp. 226--241, 2008.

Digital Library

[25]

Keller, T. and Tergan, S.-O. "Visualizing knowledge and information: An introduction," in Knowledge and information visualization, Springer, 2005, pp. 1--23.

Digital Library

[26]

Kelly, D. Methods for evaluating interactive information retrieval systems with users. Hanover, MA: Now Publishers, 2009.

Digital Library

[27]

Mann, T. M. and Reiterer, H. "Case study: a combined visualization approach for www-search results," 1999.

[28]

Manuja, M. and Garg, D. "Semantic web mining of unstructured data: Challenges and opportunities," Int. J. Eng. IJE, vol. 5, no. 3, p. 268, 2011.

[29]

Plaisant, C. "The challenge of information visualization evaluation," in Proceedings of the working conference on Advanced visual interfaces, 2004, pp. 109--116.

Digital Library

[30]

Price, S. L., Lykke Nielsen, M., Delcambre, L. M. L., Vedsted, P. and Steinhauer, J. "Using semantic components to search for domain-specific documents: An evaluation from the system perspective and the user perspective," Inf. Syst., vol. 34, no. 8, pp. 724--752, Dec. 2009.

Digital Library

[31]

Ruthven, I. Interactive information seeking, behaviour and retrieval. London: Facet, 2011.

[32]

Shneiderman, B "The eyes have it: A task by data type taxonomy for information visualizations," in Visual Languages, 1996. Proceedings., IEEE Symposium on, 1996, pp. 336--343.

Digital Library

[33]

Shneiderman, B. "Designing information-abundant web sites: issues and recommendations," Int. J. Hum.-Comput. Stud., vol. 47, no. 1, pp. 5--29, Jul. 1997.

Digital Library

[34]

Shneiderman, B. and Plaisant, C. "Strategies for evaluating information visualization tools: multi-dimensional in-depth long-term case studies," in Proceedings of the 2006 AVI workshop on BEyond time and errors: novel evaluation methods for information visualization, 2006, pp. 1--7.

Digital Library

[35]

Singer, G., Norbisrath, U. and Lewandowski, D. "Ordinary search engine users carrying out complex search tasks," J. Inf. Sci., vol. 39, no. 3, pp. 346--358, Dec. 2012.

Digital Library

[36]

Stab, C., Nazemi, K., Breyer, M. Burkhardt, D. and Kohlhammer, J. "Semantics visualization for fostering search result comprehension," in The Semantic Web: Research and Applications, Springer, 2012, pp. 633--646.

Digital Library

[37]

Steiner, T., Troncy, R. and Hausenblas, M. "How Google is using linked data today and vision for tomorrow," Linked Data Future Internet, 2010.

[38]

TechSmith, "TechSmith | Screencast.com, online video sharing, Home," 12-Aug-2013. {Online}. Available: http://www.screencast.com/. {Accessed: 12-Aug-2013}.

[39]

Treharne, K. and Powers, D. M. W. "Search Engine Result Visualisation: Challenges and Opportunities," 2009, pp. 633--638.

Digital Library

[40]

Tummarello, G., Cyganiak, R., Catasta, M., Danielczyk, S., Delbru, R. and Decker, S. "Sig.ma: Live views on the Web of Data," Web Semant. Sci. Serv. Agents World Wide Web, vol. 8, no. 4, pp. 355--364, Nov. 2010.

Digital Library

[41]

Vegas, J., Crestani, F., and de la Fuente, P. "Context representation for web search results," J. Inf. Sci., vol. 33, no. 1, pp. 77--94, Feb. 2007.

Digital Library

[42]

Ware, C. Information visualization: perception for design. San Francisco: Morgan Kaufman, 2000.

Digital Library

[43]

Ware, C. "Visual Queries: The Foundation of Visual Thinking," in Knowledge and Information Visualization, S.-O. Tergan and T. Keller, Eds. Springer Berlin Heidelberg, 2005, pp. 27--35.

Digital Library

Cited By

Hajian Hoseinabadi ACheshmehSohrabi M(2022)Proposing a New Combined Indicator for Measuring Search Engine Performance and Evaluating Google, Yahoo, DuckDuckGo, and Bing Search Engines based on Combined IndicatorJournal of Librarianship and Information Science10.1177/0961000622113857956:1(178-197)Online publication date: 8-Dec-2022
https://doi.org/10.1177/09610006221138579
Nikas CKadilierakis GFafalios PTzitzikas Y(2020)Keyword Search over RDF: Is a Single Perspective Enough?Big Data and Cognitive Computing10.3390/bdcc40300224:3(22)Online publication date: 27-Aug-2020
https://doi.org/10.3390/bdcc4030022

Index Terms

Web Search Results Visualization: Evaluation of Two Semantic Search Engines
1. Human-centered computing
  1. Human computer interaction (HCI)
2. Information systems
  1. Information retrieval

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WIMS '14: Proceedings of the 4th International Conference on Web Intelligence, Mining and Semantics (WIMS14)

June 2014

506 pages

ISBN:9781450325387

DOI:10.1145/2611040

Copyright © 2014 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 the author(s) 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].

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Aristotle University of Thessaloniki

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Published: 02 June 2014

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WIMS '14: 4th International Conference on Web Intelligence, Mining and Semantics

June 2 - 4, 2014

Thessaloniki, Greece

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WIMS '14 Paper Acceptance Rate 41 of 90 submissions, 46%;

Overall Acceptance Rate 140 of 278 submissions, 50%

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Cited By

Hajian Hoseinabadi ACheshmehSohrabi M(2022)Proposing a New Combined Indicator for Measuring Search Engine Performance and Evaluating Google, Yahoo, DuckDuckGo, and Bing Search Engines based on Combined IndicatorJournal of Librarianship and Information Science10.1177/0961000622113857956:1(178-197)Online publication date: 8-Dec-2022
https://doi.org/10.1177/09610006221138579
Nikas CKadilierakis GFafalios PTzitzikas Y(2020)Keyword Search over RDF: Is a Single Perspective Enough?Big Data and Cognitive Computing10.3390/bdcc40300224:3(22)Online publication date: 27-Aug-2020
https://doi.org/10.3390/bdcc4030022

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