In: Proceedings of the 15th ACM International Conference on Information and Knowledge Management, pp. International Society for Scientometrics and Informetrics, Rio de Janeiro (2009)Ĭhi, Y., Tseng, B.L., Tatemura, J.: Eigen-trend: trend analysis in the blogosphere based on singular value decompositions. In: Proceedings of the 12th International Conference on Scientometrics and Informetrics ISSI 2009, pp. Gipp, B., Beel, J.: Citation Proximity Analysis (CPA) - A new approach for identifying related work based on Co-Citation Analysis. ACM (2011)īeel, J., Gipp, B., Stiller, J.-O.: Information Retrieval on Mind Maps - What could it be good for? In: Proceedings of the 5th International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2009, pp. In: Proceedings of the 11th ACM Symposium on Document Engineering, DocEng 2011, pp. In: Proceedings of the 17th World Congress on Ergonomics, IEA 2009 (2009)īeel, J., Langer, S.: An Exploratory Analysis of Mind Maps. Mahler, T., Weber, M.: Dimian-Direct Manipulation and Interaction in Pen Based Mind Mapping. Review of Educational Research 76, 413 (2006)īrucks, C., Schommer, C.: Assembling Actor-based Mind-Maps from Text Stream. Nesbit, J.C., Adesope, O.O.: Learning with concept and knowledge maps: A meta-analysis. In: Proceedings of the 11th International ACM/IEEE Conference on Digital Libraries, pp. ACM (2013)īeel, J., Gipp, B., Langer, S., Genzmehr, M.: Docear: An Academic Literature Suite for Searching, Organizing and Creating Academic Literature. In: Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries, JCDL 2013, pp. Library Trends 56, 784–815 (2008)īeel, J., Langer, S., Genzmehr, M., Nürnberger, A.: Introducing Docear’s Research Paper Recommender System. Jacso, P.: Testing the calculation of a realistic h-index in Google Scholar, Scopus, and Web of Science for FW Lancaster. In: Proceedings of the 9th ACM Symposium on Document Engineering, pp. Zubiaga, A., Martinez, R., Fresno, V.: Getting the most out of social annotations for web page classification. Google: Ads in Gmail and your personal data (2012), This indicates that mind-map based user modelling is promising, but not trivial, and that further research is required to increase effectiveness. click-through rate on recommendations, varied between 0.28% and 6.24%. Depending on the applied user modelling approaches, the effectiveness, i.e. A user modelling prototype – a recommender system for the users of our mind-mapping software Docear – was implemented, and evaluated. We concluded that user modelling is the most promising application with respect to mind-maps. We evaluated the feasibility of the eight ideas, based on estimates of the number of available mind-maps, an analysis of the content of mind-maps, and an evaluation of the users’ acceptance of the ideas. For instance, mind-maps could be utilized to generate user models for recommender systems or expert search, or to calculate relatedness of web-pages that are linked in mind-maps. We believe this to be a rich source for information retrieval applications, and present eight ideas on how mind-maps could be utilized by them. However, there are an estimated two million active mind-map users, who create 5 million mind-maps every year, of which a total of 300,000 is publicly available. Mind-maps have been widely neglected by the information retrieval (IR) community.
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