By Norbert Fuhr, Mounia Lalmas, Saadia Malik, Gabriella Kazai
Content-oriented XML retrieval has been receiving expanding curiosity because of the common use of eXtensible Markup Language (XML), that's changing into a regular rfile structure on the net, in electronic libraries,and publishing. by way of exploiting the enriched resource of syntactic and semantic details that XML markup presents, XML info retrieval (IR) platforms objective to enforce a extra targeted retrieval process and go back rfile elements, so-called XML parts – rather than entire records – in line with a person question. This concentrated retrieval technique is of specific bene?t for collections containing lengthy files or records masking a wide selection of subject matters (e.g., books, consumer manuals, criminal records, etc.), the place clients’ e?ort to find suitable content material might be diminished by way of directing them to the main appropriate elements of the records. enforcing this, extra targeted, retrieval paradigm signifies that an XML IR procedure wishes not just to ?nd appropriate details within the XML files, however it additionally has to figure out the right point of granularity to be back to the consumer. moreover, the relevance of a retrieved part will be depending on assembly either content material and structural question conditions.
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Additional info for Advances in XML Information Retrieval and Evaluation: 4th International Workshop of the Initiative for the Evaluation of XML Retrieval, INEX 2005, Dagstuhl Castle, Germany, November 28-30, 2005. Revised Selected Papers
Piwowarski Recall 1. The minimum number of elements the user has to consult, over all possible lists, is 1. The value is the same for the system list and the user was able to see one element. Precision is 1. Recall 2. The minimum number of elements the user has to consult, over all possible lists, is 2. For the evaluated system, the user will have to consult the list until d - that is, the minimum number of items that he has to consult is 4. 5. Recall 3. In this case, the same process would give us a precision of 3/5 (because the user has to consult the whole list), but has the recall cannot be attained by the user, the achievement indicator is 0 and hence precision is also 0.
Malik, and G. Kazai, editors, Advances in XML Information Retrieval and Evaluation: Fourth Workshop of the INitiative for the Evaluation of XML Retrieval (INEX 2005), Schloss Dagstuhl, 28-30 November 2005, volume 3977 of Lecture Notes in Computer Science, pages 1–15. Springer-Verlag, 2006. 7. C. J. V. Rijsbergen. Information Retrieval. Butterworth-Heinemann, Newton, MA, USA, 1979. html. 8. T. Sakai. New performance metrics based on multigrade relevance: Their application to question answering. In NTCIR Workshop 4 Meeting Working Notes, June 2004.
Near-misses: To consider the retrieval of near-misses within the evaluation, we reward a partial score for the retrieval of non-ideal elements that are structurally related to ideal components. This year, only those relevant elements (as per quantisation function) of the full recall-base were considered near-misses which were not included in the ideal recall-base. Given this set of near-misses and the ideal recall-base, the XCG measures are applied such that the ideal gain vector of a query, xI, is derived from the ideal recallbase, and the gain vectors, xG, corresponding to the system runs under evaluation are based on the full recall-base.
Advances in XML Information Retrieval and Evaluation: 4th International Workshop of the Initiative for the Evaluation of XML Retrieval, INEX 2005, Dagstuhl Castle, Germany, November 28-30, 2005. Revised Selected Papers by Norbert Fuhr, Mounia Lalmas, Saadia Malik, Gabriella Kazai