By C. J. Keith van Rijsbergen (auth.), Maristella Agosti, Fabio Crestani, Gabriella Pasi (eds.)
Information Retrieval (IR) is worried with the powerful and effective retrieval of knowledge according to its semantic content material. The imperative challenge in IR is the hunt to discover the set of correct records, between a wide assortment containing the knowledge sought, gratifying a user's details desire often expressed in a normal language question. files can be items or goods in any medium: textual content, photograph, audio, or certainly a mix of all 3. This ebook offers 12 revised lectures given on the 3rd eu summer time college in info Retrieval, ESSIR 2000, held on the Villa Monastero, Varenna, Italy, in September 2000. the 1st a part of the booklet is dedicated to the basis of IR and similar components; the second one half on complicated subject matters addresses a variety of present matters, from usability facets to net looking out and browsing.
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Details Retrieval (IR) is anxious with the potent and effective retrieval of knowledge in keeping with its semantic content material. The important challenge in IR is the hunt to discover the set of appropriate files, between a wide assortment containing the knowledge sought, pleasing a user's info desire frequently expressed in a typical language question.
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Additional resources for Lectures on Information Retrieval: Third European Summer-School, ESSIR 2000 Varenna, Italy, September 11–15, 2000 Revised Lectures
Example text
D1 = {t1 , t3 , t4 } = ˆ t1 ∩ t¯2 ∩ t3 ∩ t4 ∩ t¯5 ∩ · · · ∩ ¯tn By mapping terms onto disjoint concepts, we can represent terms as union of the corresponding basic concepts: ti = mi1 ∪ · · · ∪ mir , For example, term t1 can be expressed as t1 = m1 ∪ m4 ∪ m5 ∪ m7 (see Fig. 5). For a given Boolean query, we construct the corresponding disjunctive normal form, thus giving us a set of minterms. Thus, any query is mapped onto a set of minterms: q= mqi Based on these assumptions, we can compute the probability of implication as follows: P (q ∩ d) P (d) P (q ∩ md ) = P (md ) P (d → q) = = 1 if md ⊆ q, 0 if md ⊆ q.
Assume that a German paper is a paper with at least one German author, which can be expressed as german-paper = (and paper (c-some author german)) (all R C) denotes the set of those individuals whose R’s are all C’s. As an example, assume that a student paper is a paper where all authors are students: student-paper = (and paper (all author student)) (a-not M ) denotes the set of all individuals of the domain that are not denoted by the concept M . For example, a non-German is a person who is not German: non-german = (and person (a-not german)) (top) and (bottom) denote the set of all individuals of the domain of discourse and the empty set, respectively.
Here our vocabulary consists of two terms only, thus leading to four basic concepts which are represented as small squares in this figure. A query representation also corresponds to a basic concept, thus any document in the collection belongs to one of the four basic concepts here. In contrast, documents can be arbitrary subsets of the concept space. 36 Norbert Fuhr ¯ for each term In order to apply (8), we have to estimate P (tβi i |d) and P (tβi i |d) ¯ For example, for βi = 1, the probability P (ti |d) (in addition to P (d) and P (d)).