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's changing into a customary rfile structure on the internet, in electronic libraries,and publishing. through exploiting the enriched resource of syntactic and semantic details that XML markup presents, XML details retrieval (IR) platforms target to enforce a extra centred retrieval process and go back record elements, so-called XML parts – rather than whole records – in line with a consumer question. This concentrated retrieval strategy is of specific bene?t for collections containing lengthy records or files protecting a large choice of themes (e.g., books, person manuals, criminal files, etc.), the place clients’ e?ort to find appropriate content material may be diminished via directing them to the main appropriate elements of the records. imposing this, extra concentrated, retrieval paradigm implies that an XML IR procedure wishes not just to ?nd appropriate info within the XML records, however it additionally has to figure out the proper point of granularity to be back to the person. additionally, 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
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. As shown in this example, this definition of precision-recall gives the same results as the standard definition.
EPRUM Metrics and INEX 2005 35 4 EPRUM Formulas and Examples We present in this section the evaluation of four lists for the Focussed (and SVCAS, VVCAS) and Fetch&Browse tasks. We used a small database where only two (or three) elements are ideal, as illustrated in Fig. 1. We first give the different formulas needed to compute EPRUM given an ideal set of elements I and a particular user model instantiation. Starting from formula (1), the precision at a given recall ( is the number of ideal units the user wants to see) can be rewritten: Precision( ) = E Minimum number of consulted list items (E1) for achieving a recall (over all lists) ⎡ ⎤⎫ Achievement indicator ⎪ ⎪ ⎢ ⎥⎬ for a recall ⎢ ⎥ × E⎣ (E2) Minimum number of consulted list items ⎦⎪ ⎪ ⎭ for achieving a recall (system list) It can be shown that: (E1) = ∑ ∗ i P(Fi∗ ≥ r) − P(Fi−1 ≥ r) rank i (E2) = 1 (P(Fi ≥ r) − P(Fi−1 ≥ r)) rank i i ∑ where r is the smallest integer superior or equal to × (number of ideal elements) and Fi (resp.
Au Abstract. This paper describes our proposal for an evaluation metric for XML retrieval that is solely based on the highlighted text. We support our decision of ignoring the exhaustivity dimension by undertaking a critical investigation of the two INEX 2005 relevance dimensions. We present a ﬁne grained empirical analysis of the level of assessor agreement of the ﬁve topics double-judged at INEX 2005, and show that the agreement is higher for speciﬁcity than for exhaustivity. We use the proposed metric to evaluate the INEX 2005 runs for each retrieval strategy of the CO and CAS retrieval tasks.
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