By Norbert Fuhr, Mounia Lalmas, Saadia Malik, Gabriella Kazai
Content-oriented XML retrieval has been receiving expanding curiosity as a result of frequent use of eXtensible Markup Language (XML), that's turning into a customary record structure on the internet, in electronic libraries,and publishing. via exploiting the enriched resource of syntactic and semantic info that XML markup offers, XML details retrieval (IR) platforms objective to enforce a extra centred retrieval procedure and go back record parts, so-called XML components – rather than whole files – in keeping with a consumer question. This centred retrieval method is of specific bene?t for collections containing lengthy files or files masking a wide selection of subject matters (e.g., books, person manuals, felony files, etc.), the place clients’ e?ort to find correct content material should be decreased through directing them to the main appropriate elements of the files. imposing this, extra concentrated, retrieval paradigm signifies that an XML IR approach wishes not just to ?nd proper details within the XML files, however it additionally has to figure out the perfect point of granularity to be lower back to the consumer. additionally, the relevance of a retrieved part could 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
9 2 2 Like in the previous Section, we distinguish two cases: 1. If the user is satisfied with an element of exhaustivity at least 2 (75% of the users – there is a justification that we don’t present here): Recall 1 (level 1/2). (E1) is 1; (E2) is resp. 1, 1, 1 × ( 12 − 0) + 12 × (1 − 12 ) = 34 , and 34 for lists A, B, C and D. Precision is 1, 1, 34 , and 34 . Recall 2 (level 1). (E1) is 2; (E2) is resp. 12 , 12 , 12 × ( 12 − 0) = 14 , and 14 for lists A, B, C and D. Precisions is 1, 1, 12 and 12 .
Note also that a user always browse from a considered element x to x (it implies that the fact that x is consulted is equivalent to the fact that x is seen by the user) as P(x → x) = 1. 3. 5 if exhaustivity is 1 ⎪ ⎪ ⎩ 1 if exhaustivity is 2 This algorithm was chosen for its simplicity, and because it produces intuitively correct sets of ideal elements. Starting from the original assessments, the process is as follows. e. a path from the deepest element with a non zero quantisation to the root of the document), the element with the higher quantisation within that path is added to the ideal set.
The interest of this formulation is that we can define and use more complex user and relevance models, and starting from the same definition, derive a generalisation of precision-recall. It is possible to prove that, using the final formula of EPRUM and setting its parameters so as to mimic the standard user behaviour in “flat” IR, we get exactly the same result as trec_eval . 3 What is Needed to Compute EPRUM? EPRUM can be computed given three different sets of parameters: 1. The probability that a user considers an element of the corpus.
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