Open AccessExploiting cascading citations for retrieval
Author(s) -
Dervos Dimitris A.,
Klimis Leonidas
Publication year - 2008
Publication title -
proceedings of the american society for information science and technology
Language(s) - English
Resource type - Journals
eISSN - 1550-8390
pISSN - 0044-7870
DOI - 10.1002/meet.2008.1450450239
Subject(s) - chaining , information retrieval , search engine indexing , computer science , citation , testbed , identification (biology) , population , similarity (geometry) , index (typography) , value (mathematics) , science citation index , data science , library science , world wide web , image (mathematics) , artificial intelligence , psychology , psychotherapist , machine learning , botany , biology , demography , sociology
We report on the utilization of the cascading citations indexing framework (C 2 IF) for the identification of similarities among items (in this case research articles) in a bibliographic database. More specifically, the problem of chaining forward from a given focal article is addressed by considering the direct as well as the indirect citations that target the article in question. From the population of articles that cite the given article directly, those associated with a larger number of higher‐level C 2 IF constructs are found to be more similar to it. The findings also appear to be of value for the mirror image problem of chaining backward from the focal article to a population of referenced articles. Cited publications for which the focal article represents/hosts a larger number of higher‐level C 2 IF constructs are likely to be more similar to it. As a testbed, sixty (60) highly cited computer science research articles are considered together with their associated bibliographic links over a six‐year period 1999‐2005) in the Science Citation Index Expanded (SCIE) data. The dataset has been made available by Thomson Scientific for conducting research along the lines of the Cascading Citations Analysis Project (C‐CAP). Similarity values are calculated by considering author‐supplied as well as automatically generated keywords registered in the SCIE dataset. The purpose of this research is to develop a strategy that will improve the effectiveness of retrieval in digital libraries that incorporate bibliographic citations.