%0 Conference Paper %1 ceroni2014information %A Ceroni, Andrea %A Georgescu, Mihai %A Gadiraju, Ujwal %A Naini, Kaweh Djafari %A Fisichella, Marco %B In Proceedings of the 10th International Symposium on Open Collaboration OpenSym 2014 %D 2014 %I ACM Press %K myown %T Information Evolution in Wikipedia %X The Web of data is constantly evolving based on the dynamics of its content. Current Web search engine technologies consider static collections and do not factor in explicitly or implicitly available temporal information, that can be leveraged to gain insights into the dynamics of the data. In this paper, we hypothesize that by employing the temporal aspect as the primary means for capturing the evolution of entities, it is possible to provide entity-based accessibility to Web archives. We empirically show that the edit activity on Wikipedia can be exploited to provide evidence of the evolution of Wikipedia pages over time, both in terms of their content and in terms of their temporally defined relationships, classified in literature as events. Finally, we present results from our extensive analysis of a dataset consisting of 31,998 Wikipedia pages describing politicians, and observations from in-depth case studies. Our findings reflect the usefulness of leveraging temporal information in order to study the evolution of entities and breed promising grounds for further research.

@inproceedings{ceroni2014information, abstract = {The Web of data is constantly evolving based on the dynamics of its content. Current Web search engine technologies consider static collections and do not factor in explicitly or implicitly available temporal information, that can be leveraged to gain insights into the dynamics of the data. In this paper, we hypothesize that by employing the temporal aspect as the primary means for capturing the evolution of entities, it is possible to provide entity-based accessibility to Web archives. We empirically show that the edit activity on Wikipedia can be exploited to provide evidence of the evolution of Wikipedia pages over time, both in terms of their content and in terms of their temporally defined relationships, classified in literature as events. Finally, we present results from our extensive analysis of a dataset consisting of 31,998 Wikipedia pages describing politicians, and observations from in-depth case studies. Our findings reflect the usefulness of leveraging temporal information in order to study the evolution of entities and breed promising grounds for further research.}, added-at = {2015-03-31T17:11:39.000+0200}, author = {Ceroni, Andrea and Georgescu, Mihai and Gadiraju, Ujwal and Naini, Kaweh Djafari and Fisichella, Marco}, biburl = {https://www.bibsonomy.org/bibtex/267299a481a8807c786cc460e4ecfa27d/ujwal}, booktitle = {In Proceedings of the 10th International Symposium on Open Collaboration OpenSym 2014}, day = 27, interhash = {39c14f63bef187d98149f81a28e1d06e}, intrahash = {67299a481a8807c786cc460e4ecfa27d}, keywords = {myown}, month = {August}, publisher = {ACM Press}, timestamp = {2015-03-31T17:11:39.000+0200}, title = {Information Evolution in Wikipedia}, year = 2014 }