Duplicate journal title detection in references

Abstract

Our research efforts are oriented towards applying text mining techniques in order to help librarians make more informative decisions when selecting learning resources to be included in the library's offer. The proper selection of learning resources to be included in the library's offer is one of the key factors determining the overall usefulness of the library. Our task was to match abbreviated journal titles from citations with journals in existing digital libraries. The main problem is that for one journal there is often a number of different abbreviated forms in the citation report, hence the matching depends on the detection of duplicate records. We used character-based and token-based metrics together with a generated thesaurus for detecting duplicate records.