01946nas a2200265   4500008004100000245005700041210005700098260000900155300001400164490000600178520115700184653001301341653002901354653002601383653002701409653001701436653000801453653002001461100001401481700001601495700001601511700001601527700002001543856011701563       2010                        eng d00aHarvesting pertinent resources from Linked Open Data0 aHarvesting pertinent resources from Linked Open Data  c2010  a205 - 2120 v83 aLinked Open Data (LOD) is becoming an essential part of the Semantic Web. Although LOD has amassed large quantities of structured data from diverse, openly available data sources, there is still a lack of user-friendly interfaces and mechanisms for exploring this huge resource. In this paper, we describe a methodology for harvesting relevant information from the gigantic LOD cloud. The methodology is based on combination of information: identification, extraction, integration and presentation. Relevant information is identified by using a set of heuristics. The identified information resource is extracted by employing an intelligent URI discovery technique. The extracted information is further integrated with the help of a Concept Aggregation Framework. Then the information is presented to end users in logical informational aspects. Thereby, the proposed system is capable of hiding complex underlying semantic mechanics from end users and reducing the users' cognitive load in locating relevant information. In this paper, we describe the methodology and its implementation in the CAF-SIAL system, and compare it with the state of the art.10aCAF-SIAL10aInformation presentation10aInformation retrieval10aLinked Open Data (LOD)10aSemantic web10aURI10aUser interfaces1 aLatif, A.1 aAfzal, M.T.1 aSaeed, A.U.1 aHoefler, P.1 aTochtermann, K.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-79960685735&partnerID=40&md5=cc886d4db44fdc4f20722b7621aca81801777nas 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