01777nas a2200217   4500008004100000245005300041210005300094260000900147300001400156490000600170520110600176653001901282653002001301653002201321653002701343100001601370700001501386700001701401700002401418856011701442       2010                        eng d00aRule based autonomous citation mining with tierl0 aRule based autonomous citation mining with tierl  c2010  a196 - 2040 v83 aCitations management is an important task in managing digital libraries. Citations provide valuable information e.g., used in evaluating an author's influences or scholarly quality (the impact factor of research journals). But although a reliable and effective autonomous citation management is essential, manual citation management can be extremely costly. Automatic citation mining on the other hand is a non-trivial task mainly due to non-conforming citation styles, spelling errors and the difficulty of reliably extracting text from PDF documents. In this paper we propose a novel rule-based autonomous citation mining technique, to address this important task. We define a set of common heuristics that together allow to improve the state of the art in automatic citation mining. Moreover, by first disambiguating citations based on venues, our technique significantly enhances the correct discovery of citations. Our experiments show that the proposed approach is indeed able to overcome limitations of current leading citation indexes such as ISI Web of Knowledge, Citeseer and Google Scholar.10aCitation index10aCitation mining10aDigital libraries10aInformation extraction1 aAfzal, M.T.1 aMaurer, H.1 aBalke, W.-T.1 aKulathuramaiyer, N.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-79951786283&partnerID=40&md5=4fbea6129a6c4c727c3c0618ccbe6087