Benoit, G.: Data discretization for novel relationship discovery in information retrieval (2002)
0.01
0.006472671 = product of:
0.045308694 = sum of:
0.011415146 = weight(_text_:information in 5197) [ClassicSimilarity], result of:
0.011415146 = score(doc=5197,freq=4.0), product of:
0.052020688 = queryWeight, product of:
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.029633347 = queryNorm
0.21943474 = fieldWeight in 5197, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.0625 = fieldNorm(doc=5197)
0.033893548 = weight(_text_:retrieval in 5197) [ClassicSimilarity], result of:
0.033893548 = score(doc=5197,freq=4.0), product of:
0.08963835 = queryWeight, product of:
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.029633347 = queryNorm
0.37811437 = fieldWeight in 5197, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.0625 = fieldNorm(doc=5197)
0.14285715 = coord(2/14)
- Abstract
- A sample of 600 Dialog and Swiss-Prot full text records in genetics and molecular biology were parsed and term frequencies calculated to provide data for a test of Benoit's visualization model for retrieval. A retrieved set is displayed graphically allowing for manipulation of document and concept relationships in real time, which hopefully will reveal unanticipated relationships.
- Source
- Journal of the American Society for Information Science and Technology. 53(2002) no.9, S.736-746
