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 turning into a customary rfile layout on the internet, in electronic libraries,and publishing. by means of exploiting the enriched resource of syntactic and semantic details that XML markup presents, XML info retrieval (IR) platforms target to enforce a extra targeted retrieval procedure and go back record parts, so-called XML components – rather than whole records – according to a person question. This concentrated retrieval technique is of specific bene?t for collections containing lengthy files or records overlaying a wide selection of subject matters (e.g., books, consumer manuals, criminal records, etc.), the place clients’ e?ort to find proper content material might be diminished through directing them to the main proper elements of the records. imposing this, extra targeted, retrieval paradigm signifies that an XML IR process wishes not just to ?nd suitable info within the XML records, however it additionally has to figure out the perfect point of granularity to be again to the consumer. moreover, the relevance of a retrieved part should be depending on assembly either content material and structural question conditions.
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Extra 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
C List D2[i,k] D1[b]: the first document returned contains a near miss (i); as i is fully specific (contained in an ideal element), the probability that the user consults the next element (k) is 1. D List D2[g,k] D1[b]: in this list, the first element of the first returned document is an element that overlaps partially with an ideal element; hence, the user will consider EPRUM Metrics and INEX 2005 39 the element k of D2 with a probability inferior to 1. Said otherwise, some users only will continue to consult the second highlighted highlighted elements within D2.
Lalmas, and S. Malik, editors, INitiative for the Evaluation of XML Retrieval (INEX). Proceedings of the Second INEX Workshop, Dagstuhl, France, Dec. 2003.  B. Piwowarski, P. Gallinari, and G. Dupret. An extension of precision-recall with user modelling (PRUM): Application to XML retrieval. submitted for publication, 2005.  V. V. Raghavan, G. S. Jung, and P. Bollmann. A critical investigation of recall and precision as measures of retrieval system performance. ACM Transactions on Information Systems, 7(3):205–229, 1989.
There is a way to combine the two sets of precisions that we do not describe here but give an example instead. 875. 75 6 1 2 2 (excluded) and 3 (included) of the ideal elements, then for 75 % of the users that means seeing 2 ideal elements with a precision 23 , and for 25 % of the users that means seeing 2 ideal elements with a precision 1. 75. The evaluations order the runs in an order which is appropriate: A has the maximum score and C is worse than D (D and C have their two first list item swapped, and D has a fully ideal element as its top ranked element).
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