Diese Datenbank enthält über 40.000 Dokumente zu Themen aus den Bereichen Formalerschließung – Inhaltserschließung – Information Retrieval.
© 2015 W. Gödert, TH Köln, Institut für Informationswissenschaft / Powered by litecat, BIS Oldenburg (Stand: 28. April 2022)
1Torvik, V.I. ; Weeber, M. ; Swanson, D.R. ; Smalheiser, N.R.: ¬A probabilistic similarity metric for medline mecords : a model for author name disambiguation.
In: Journal of the American Society for Information Science and Technology. 56(2005) no.2, S.140-158.
Abstract: We present a model for estimating the probability that a pair of author names (sharing last name and first initial), appearing an two different Medline articles, refer to the same individual. The model uses a simple yet powerful similarity profile between a pair of articles, based an title, journal name, coauthor names, medical subject headings (MeSH), language, affiliation, and name attributes (prevalence in the literature, middle initial, and suffix). The similarity profile distribution is computed from reference sets consisting of pairs of articles containing almost exclusively author matches versus nonmatches, generated in an unbiased manner. Although the match set is generated automatically and might contain a small proportion of nonmatches, the model is quite robust against contamination with nonmatches. We have created a free, public service ("Author-ity": http://arrowsmith.psych.uic.edu) that takes as input an author's name given an a specific article, and gives as output a list of all articles with that (last name, first initial) ranked by decreasing similarity, with match probability indicated.
2Weeber, M. ; Klein, H. ; Jong-van den Berg, L.T.W. de ; Vos, R.: Using concepts in literature-based discovery : simulating Swanson's Raynaud-Fish Oil and Migraine-Manesium discoveries.
In: Journal of the American Society for Information Science and technology. 52(2001) no.7, S.548-557.
Abstract: Literature-based discovery has resulted in new knowledge. In the biomedical context, Don R. Swanson has generated several literature-based hypotheses that have been corroborated experimentally and clinically. In this paper, we propose a two-step model of the discovery process in which hypotheses are generated and subsequently tested. We have implemented this model in a Natural Language Processing system that uses biomedical Unified Medical Language System (UMLS) concepts as its unit of analysis. We use the semantic information that is provided with these concepts as a powerful filter to successfully simulate Swanson's discoveries of connecting Raynaud's disease with fish oil and migraine with a magnesium deficiency