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Diversity-Aware Search

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Health Web Science

Part of the book series: Health Information Science ((HIS))

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Abstract

To support users in identifying relevant information in the medical web, on the one hand, standards for medical web content were defined to allow certification of quality health information (e.g. the HONcode certification[30]). However, such certificate does not help a user to assess the diverse aspects of web content. Therefore, on the other hand, users need support in finding relevant content by more sophisticated search facilities. Existing web and medical blog search engines (e.g., Medworm [162]) list search results matching query keywords in a flat list. Diversity is—if at all—only considered by presenting different results in the top N positions. In this chapter, a system architecture for a retrieval engine is introduced that allows to exploit (medical) domain specific notions of diversity.

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References

  1. HON. Health on the net foundation. http://www.hon.ch, last accessed: 14.03.2015.

  2. X. Zhou, X. Zhang, and X. Hu. Dragon toolkit: Incorporating auto-learned semantic knowledge into large-scale text retrieval and mining. In Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence (ICTAI), October 29–31, 2007, Patras, Greece, 2007.

    Google Scholar 

  3. Jamila Hanan. Medworm. http://www.medworm.com/, last accessed: 14.03.2015.

  4. K. Denecke. An architecture for diversity-aware search for medical web content. Methods of Information in Medicine, 51(6):549–56, 2012.

    Article  Google Scholar 

  5. T. Vanhecke, M. Barnes, J. Zimmerman, and S. Shoichet. PubMed vs. HighWire Press: A head-to-head comparison of two medical literature search engines. Computers in Biology and Medicine, 37(9):1252–1258, September 2007.

    Google Scholar 

  6. P. Daumke, S. Schulz, M.L. Müller, W. Dzeyk, L. Prinzen, E.J. Pacheco, P.S. Cancian, P. Nohama, and K. Marko. Subword-based semantic retrieval of clinical and bibliograpic documents. Methods Inf Med, 49(2):141–47, 2010.

    Article  MATH  Google Scholar 

  7. Martin Krallinger and Alfonso Valencia. Text-mining and information-retrieval services for molecular biology. Genome Biology, 6(7):224+, 2005.

    Google Scholar 

  8. T.G. Vit Novacek and S. Handschuh. Coraal towards deep exploitation of textual resources in life sciences. Lecture Notes in Computer Science. Berlin/Heidelberg, 5651/2009:206–215, 2009.

    Google Scholar 

  9. S. Dumais F. Radlinski. Improving personalized web search using result diversification. In SIGIR 2006: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval. New York, USA, pages 691–692, 2006.

    Google Scholar 

  10. D. Carmel A. Anagnostopoulos, A.Z. Broder. Sampling search-engine results. In Proceedings of the 14th international conference on World Wide Web: New York, USA, pages 245–256, 2005.

    Google Scholar 

  11. Chein-Shung Hwang and Show-Fen Lin. Hill climbing for diversity retrieval. Computer Science and Information Engineering, World Congress on, 5:154–158, 2009.

    Google Scholar 

  12. Rakesh Agrawal, Sreenivas Gollapudi, Alan Halverson, and Samuel Ieong. Diversifying search results. In Proceedings of the Second ACM International Conference on Web Search and Data Mining, WSDM ’09, pages 5–14, New York, NY, USA, 2009. ACM.

    Google Scholar 

  13. Sreenivas Gollapudi and Aneesh Sharma. An axiomatic approach for result diversification. In Proceedings of the 18th International Conference on World Wide Web, WWW ’09, pages 381–390, New York, NY, USA, 2009. ACM.

    Google Scholar 

  14. Enrico Minack, Gianluca Demartini, and Wolfgang Nejdl. Current approaches to search result diversification. In Proc. of 1st Intl. Workshop on Living Web, 2009.

    Google Scholar 

  15. Wisam Dakka and Panagiotis G. Ipeirotis. Automatic extraction of useful facet hierarchies from text databases. In Proceedings of the 2008 IEEE 24th International Conference on Data Engineering, ICDE ’08, pages 466–475, Washington, DC, USA, 2008. IEEE Computer Society.

    Google Scholar 

  16. Marti A. Hearst. Clustering versus faceted categories for information exploration. Commun. ACM, 49(4):59–61, April 2006.

    Article  MATH  Google Scholar 

  17. J. Diederich and W. T. Balke. Automatically created concept graphs using descriptive keywords in the medical domain. Methods of Information in Medicine, 47(3):241–50, 2008.

    Google Scholar 

  18. SJ. Darmoni, JP. Leroy, F. Baudic, M. Douyere, J. Piot, and B. Thrion. Cismef: a structured health resource guide. Methods of Information in Medicine, 39(1):30–35, 2000.

    Google Scholar 

  19. Angelos Hliaoutakis, Giannis Varelas, Euripides G. M. Petrakis, and Evangelos Milios. Medsearch: A retrieval system for medical information based on semantic similarity. In In Proceedings of ECDL, pages 512–515, 2006.

    Google Scholar 

  20. Gang Luo. Design and evaluation of the imed intelligent medical search engine. In Proceedings of the 2009 IEEE International Conference on Data Engineering, ICDE ’09, pages 1379–1390, Washington, DC, USA, 2009. IEEE Computer Society.

    Google Scholar 

  21. AlchemyAPI. Alchemyapi. http://www.alchemyapi.com/, last accessed: 14.03.2015.

  22. Andrea Esuli and Fabrizio Sebastiani. Sentiwordnet: A publicly available lexical resource for opinion mining. In In Proceedings of the 5th Conference on Language Resources and Evaluation (LREC 2006), pages 417–422, 2006.

    Google Scholar 

  23. Mark Hall, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, and Ian H. Witten. The weka data mining software: An update. SIGKDD Explor. Newsl., 11(1):10–18, November 2009.

    Google Scholar 

  24. Ian H. Witten and Eibe Frank. Data Mining: Practical Machine Learning Tools and Techniques. The Morgan Kaufmann Series in Data Management Systems. Morgan Kaufmann Publishers, San Francisco, CA, 2nd edition, 2005.

    Google Scholar 

  25. H. Mueller, C. Boyer, A. Gaudinat, W. Hersh, and A. Geissbuhler. Analyzing web log files of the health on the net honmedia search engine to define typical image search tasks for image retrieval evaluation. In Stud Health Technol Inform 129 (Pt 2), pages 1319–1323, 2007.

    Google Scholar 

  26. Filip Radlinski and Nick Craswell. Comparing the sensitivity of information retrieval metrics. In Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’10, pages 667–674, New York, NY, USA, 2010. ACM.

    Google Scholar 

  27. Kalervo Järvelin and Jaana Kekäläinen. Ir evaluation methods for retrieving highly relevant documents. In Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’00, pages 41–48, New York, NY, USA, 2000. ACM.

    Google Scholar 

  28. N.H. Shah C. Jonquet and M.A. Musen. The open biomedical annotator. In Summit on Translat Bioinforma, pages 56–60, 2009.

    Google Scholar 

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Denecke, K. (2015). Diversity-Aware Search. In: Health Web Science. Health Information Science. Springer, Cham. https://doi.org/10.1007/978-3-319-20582-3_12

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  • DOI: https://doi.org/10.1007/978-3-319-20582-3_12

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20581-6

  • Online ISBN: 978-3-319-20582-3

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