By Norbert Fuhr, Mounia Lalmas, Saadia Malik, Gabriella Kazai
Content-oriented XML retrieval has been receiving expanding curiosity as a result of common use of eXtensible Markup Language (XML), that is turning into a regular record layout on the net, in electronic libraries,and publishing. through exploiting the enriched resource of syntactic and semantic details that XML markup offers, XML details retrieval (IR) structures goal to enforce a extra concentrated retrieval technique and go back record elements, so-called XML components – rather than entire files – in accordance with a person question. This targeted retrieval technique is of specific bene?t for collections containing lengthy records or records overlaying a large choice of themes (e.g., books, person manuals, criminal files, etc.), the place clients’ e?ort to find correct content material might be lowered through directing them to the main appropriate elements of the files. enforcing this, extra concentrated, retrieval paradigm signifies that an XML IR procedure wishes not just to ?nd appropriate info within the XML files, however it additionally has to figure out the fitting point of granularity to be again to the person. moreover, the relevance of a retrieved part should 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
Let’s take an example to illustrate this point: Let a, b, and c be three elements; b and c are ideal. The list is composed of elements in the corpus. 9. For a recall 1, an ideal list would EPRUM Metrics and INEX 2005 41 be a simple list restricted to one of the ideal elements, b or c, with an expected length of 1. 29 (instead of 2 with a list composed of b and c). Overlap. The preceding example also illustrate another consequence of the EPRUM user model: Overlap might be a good property of the returned list.
The probability that a user finds a unit ideal. This probability is closely related to the concept of quantisation but has a well defined meaning in EPRUM: In a user experiment, its value would be the proportion of users that would find the given unit ideal if they had exactly the same information need. Unfortunately, we still don’t have enough user data to compute even an approximated user model. Nevertheless, it is possible to define simple yet realistic behaviours. In EPRUM Metrics and INEX 2005 33 INEX 2005, we chose user models close to the ones implied by xCG (where only elements overlapping with an ideal unit can be rewarded) which was the official INEX 2005 metric.
9. 9 2 , the three terms of the sum being the case where (1) the user sees h and k, (2) the user sees k but not h, and (3) the user sees h but not k. Note also that the only real difference between lists C and D is that the first element is not fully specific (because the same proportion of users will browse from element g to h and from element i to h). Table 3. Evolution of the different probabilities, with respect to the different lists (A, B, C, D) for the Fetch&Browse task. The three columns below probability P(x ∈ Si ) correspond respectively to the probability that element a, b, or c is seen by the user after rank i.