MobEx: A System for Exploratory Search on the Mobile Web

  • Günter Neumann
  • Sven Schmeier
Part of the Communications in Computer and Information Science book series (CCIS, volume 358)


We present MobEx, a mobile touchable application for exploratory search on the mobile web. The system has been implemented for operation on a tablet computer, i.e. an Apple iPad, and on a mobile device, i.e. Apple iPhone or iPod touch. Starting from a topic issued by the user the system collects web snippets that have been determined by a standard search engine in a first step and extracts associated topics to the initial query in an unsupervised way on-demand and highly performant. This process is recursive in priciple as it furthermore determines other topics associated to the newly found ones and so forth. As a result MobEx creates a dense web of associated topics that is presented to the user as an interactive topic graph. We consider the extraction of topics as a specific empirical collocation extraction task where collocations are extracted between chunks combined with the cluster descriptions of an online clustering algorithm. Our measure of association strength is based on the pointwise mutual information between chunk pairs which explicitly takes their distance into account. These syntactically–oriented chunk pairs are then semantically ranked and filtered using the cluster descriptions created by a Singular Value Decomposition (SVD) approach. An initial user evaluation shows that this system is especially helpful for finding new interesting information on topics about which the user has only a vague idea or even no idea at all.


Web mining Information extraction Topic graph exploration Mobile device 


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  1. 1.
    Banko, M., Cafarella, M.J., Soderland, S., Broadhead, M., Etzioni, O.: Open information extraction from the web. In: Proceedings of IJCAI 2007, pp. 2670–2676 (2007)Google Scholar
  2. 2.
    Baroni, M., Evert, S.: Statistical methods for corpus exploitation. In: Lüdeling, A., Kytö, M. (eds.) Corpus Linguistics. An International Handbook. Mouton de Gruyter, Berlin (2008)Google Scholar
  3. 3.
    Dingare, S., Nissim, M., Finkel, J., Grover, C., Manning, C.D.: A system for identifying named entities in biomedical text: How results from two evaluations reflect on both the system and the evaluations. Comparative and Functional Genomics 6, 77–85 (2004)CrossRefGoogle Scholar
  4. 4.
    Drozdzynski, W., Krieger, H.-U., Piskorski, J., Schäfer, U., Xu, F.: Shallow processing with unification and typed feature structures — foundations and applications. Künstliche Intelligenz, 17–23 (2004)Google Scholar
  5. 5.
    Etzioni, O.: Machine reading of web text. In: Proceedings of the 4th International Conference on Knowledge Capture, Whistler, BC, Canada, pp. 1–4 (2007)Google Scholar
  6. 6.
    Geraci, F., Pellegrini, M., Maggini, M., Sebastiani, F.: Cluster generation and labeling for web snippets: A fast, accurate hierarchical solution. Journal of Internet Mathematics 4(4), 413–443 (2006)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Giesbrecht, E., Evert, S.: Part-of-speech tagging - a solved task? an evaluation of pos taggers for the web as corpus. In: Proceedings of the 5th Web as Corpus Workshop (2009)Google Scholar
  8. 8.
    Gimenez, J., Marquez., L.: Svmtool: A general pos tagger generator based on support vector machines. In: Proceedings of LREC 2004, pp. 43–46 (2004)Google Scholar
  9. 9.
    Manning, C.D., Raghavan, P., Schütze, H.: Introduction to information retrieval. Cambridge University Press (2008)Google Scholar
  10. 10.
    Marchionini, G.: Exploratory search: from finding to understanding. Commun. ACM 49(4), 41–46 (2006)CrossRefGoogle Scholar
  11. 11.
    Nadeau, D., Sekine, S.: A survey of named entity recognition and classification. Journal of Linguisticae Investigationes 30(1), 1–20 (2007)CrossRefGoogle Scholar
  12. 12.
    Neumann, G., Schmeier, S.: A mobile touchable application for online topic graph extraction and exploration of web content. In: Proceedings of the ACL-HLT 2011 System Demonstrations (2011)Google Scholar
  13. 13.
    Osinski, S., Stefanowski, J., Weiss, D.: Lingo: Search results clustering algorithm based on singular value decomposition. In: Proceedings of the International IIS: Intelligent Information Processing and Web Mining Conference. Springer (2004)Google Scholar
  14. 14.
    Osinski, S., Weiss, D.: Carrot2: Making sense of the haystack. In: ERCIM News (2008)Google Scholar
  15. 15.
    Turney, P.D.: Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL. In: Flach, P.A., De Raedt, L. (eds.) ECML 2001. LNCS (LNAI), vol. 2167, pp. 491–502. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  16. 16.
    Yates, A.: Information extraction from the web: Techniques and applications. Ph.D. Thesis, University of Washington, Computer Science and Engineering (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Günter Neumann
    • 1
  • Sven Schmeier
    • 1
  1. 1.DFKI - German Research Center for Artificial IntelligenceSaarbrückenGermany

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