By Norbert Fuhr, Mounia Lalmas, Saadia Malik, Gabriella Kazai
Content-oriented XML retrieval has been receiving expanding curiosity as a result of the common use of eXtensible Markup Language (XML), that's changing into a regular record structure on the net, in electronic libraries,and publishing. by means of exploiting the enriched resource of syntactic and semantic info that XML markup presents, XML details retrieval (IR) structures goal to enforce a extra targeted retrieval process and go back record elements, so-called XML components – rather than entire records – in keeping with a person question. This targeted retrieval procedure is of specific bene?t for collections containing lengthy files or files masking a wide selection of subject matters (e.g., books, person manuals, criminal files, etc.), the place clients’ e?ort to find suitable content material should be diminished by way of directing them to the main suitable components of the records. enforcing this, extra concentrated, retrieval paradigm implies that an XML IR procedure wishes not just to ?nd appropriate info within the XML files, however it additionally has to figure out the suitable point of granularity to be again to the consumer. additionally, the relevance of a retrieved part can be depending on assembly either content material and structural question conditions.
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Additional resources 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
In order to illustrate EPRUM behaviour, we now apply the different formulas with the two different user models we chose for INEX 2005. 1 Focussed and VVCAS, SVCAS In order to illustrate the EPRUM metric, we use the following lists for the focussed, VVCAS and SVCAS tasks (all these tasks do not define precisely the target element, so the hierarchic behaviour makes sense): A List b,h,k: This is the ideal list, composed of the ideal elements - with the “most” ideal first. B List k, h, b: This is the ideal list, but ordered by increasing order of ideality.
However, unlike in previous years a new set of oﬃcial metrics was adopted at INEX 2005, which belong to the eXtended Cumulated Gain (XCG) family of metrics [2, 4]. Two oﬃcial INEX 2005 metrics are nxCG (with the nxCG[r] measure), which for a rank r measures the relative retrieval gain a user has accumulated up to that rank, compared to the gain they could have accumulated if the system had produced the optimal ranking; and ep/gr (with the MAep measure), which for a cumulated gain level measures the amount of relative eﬀort (as the number of visited ranks) a user is required to spend compared to the eﬀort they could have spent while inspecting an optimal ranking .
To obtain this score, we ﬁrst ﬁlter the recall-base to contain only those article nodes that have at least one relevant element according to the chosen quantisation. g. for strict quantisation, only those articles are kept that contain at least one highly exhaustive and fully speciﬁc element. The ideal gain vector is obtained by sorting the ﬁltered set by quantised score. Since articles do not overlap, the process is the same for both overlap=on and oﬀ modes. We compare the obtained ideal gain vector to the list of article nodes that is derived from a system run.