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- ItemEmotional tendencies in online social networking: a statistical analysis(London : Taylor & Francis Open, 2016) Zhang, Xianhan; Zhang, Nan; Zhao, Letong; Zhang, Ruihan; Cao, Jinde; Lu, Jianquan; Kurths, Jürgen; Qian, ChengNumerous previous studies suggested that people's emotional tendency (ET) towards an issue can often be affected by others. But in some cases, people are unwilling to believe opposite points. This paper aims to study whether people's emotional tendencies (ET) are susceptible with exposures to others' ET concerning a special topic. ET contained in 798,057 pieces of private-information-deleted Chinese Weibo posts are carefully investigated via a revised genetic algorithm, a nonlinear method. Note that nearly all of the posts are closely related to a special topic, the terrible earthquake happen in Japan, 11 March 2011. By conducting statistical analysis including coefficient calculations and hypothesis testing, this study shows that concerning this particular topic, Chinese citizens' first impressions about Japan are solid enough to form their ET and would not be easily altered. Moreover, according to analysis and discussion, we discover that node-to-node impact is exaggerated in some theoretical information diffusion models. Instead it is actually the interaction between nodes' properties and the spread information that matters in the process of information diffusions.
- ItemRADAR-Team stellt Testsystem auf zweitem Projekt-Workshop in Frankfurt vor(Karlsruhe : KIT, 2015) Potthoff, Jan; Razum, Matthias; Kraft, AngelinaIm Rahmen des Projekts "Research Data Repository" (RADAR) wurde am 23. Juni 2015 auf dem zweiten Projekt-Workshop der aktuelle Stand des Testsystems, das zur Archivierung und Publikation von Forschungsdaten genutzt werden kann, vorgestellt. Außerdem wurden weitere Anforderungen an das System und allgemeine Fragen des Forschungsdatenmanagements mit den Workshop-Teilnehmern diskutiert.
- ItemWeb-based access, aggregation, and visualization of future climate projections with emphasis on agricultural assessments(Amsterdam : Elsevier B.V., 2018) Villoria, N.B.; Elliott, J.; Müller, C.; Shin, J.; Zhao, L.; Song, C.Access to climate and spatial datasets by non-specialists is restricted by technical barriers involving hardware, software and data formats. We discuss an open-source online tool that facilitates downloading the climate data from the global circulation models used by the Inter-Sectoral Impacts Model Intercomparison Project. The tool also offers temporal and spatial aggregation capabilities for incorporating future climate scenarios in applications where spatial aggregation is important. We hope that streamlined access to these data facilitates analysis of climate related issues while considering the uncertainties derived from future climate projections and temporal aggregation choices.
- ItemCommunication activity in a social network: Relation between long-term correlations and inter-event clustering(London : Nature Publishing Group, 2012) Rybski, D.; Buldyrev, S.V.; Havlin, S.; Liljeros, F.; Makse, H.A.Human communication in social networks is dominated by emergent statistical laws such as non-trivial correlations and temporal clustering. Recently, we found long-term correlations in the user's activity in social communities. Here, we extend this work to study the collective behavior of the whole community with the goal of understanding the origin of clustering and long-term persistence. At the individual level, we find that the correlations in activity are a byproduct of the clustering expressed in the power-law distribution of inter-event times of single users, i.e. short periods of many events are separated by long periods of no events. On the contrary, the activity of the whole community presents long-term correlations that are a true emergent property of the system, i.e. they are not related to the distribution of inter-event times. This result suggests the existence of collective behavior, possibly arising from nontrivial communication patterns through the embedding social network.
- ItemWhy reinvent the wheel: Let's build question answering systems together(New York City : Association for Computing Machinery, 2018) Singh, K.; Radhakrishna, A.S.; Both, A.; Shekarpour, S.; Lytra, I.; Usbeck, R.; Vyas, A.; Khikmatullaev, A.; Punjani, D.; Lange, C.; Vidal, Maria-Esther; Lehmann, J.; Auer, SörenModern question answering (QA) systems need to flexibly integrate a number of components specialised to fulfil specific tasks in a QA pipeline. Key QA tasks include Named Entity Recognition and Disambiguation, Relation Extraction, and Query Building. Since a number of different software components exist that implement different strategies for each of these tasks, it is a major challenge to select and combine the most suitable components into a QA system, given the characteristics of a question. We study this optimisation problem and train classifiers, which take features of a question as input and have the goal of optimising the selection of QA components based on those features. We then devise a greedy algorithm to identify the pipelines that include the suitable components and can effectively answer the given question. We implement this model within Frankenstein, a QA framework able to select QA components and compose QA pipelines. We evaluate the effectiveness of the pipelines generated by Frankenstein using the QALD and LC-QuAD benchmarks. These results not only suggest that Frankenstein precisely solves the QA optimisation problem but also enables the automatic composition of optimised QA pipelines, which outperform the static Baseline QA pipeline. Thanks to this flexible and fully automated pipeline generation process, new QA components can be easily included in Frankenstein, thus improving the performance of the generated pipelines.
- ItemWhen humans and machines collaborate: Cross-lingual Label Editing in Wikidata(New York City : Association for Computing Machinery, 2019) Kaffee, L.-A.; Endris, K.M.; Simperl, E.The quality and maintainability of a knowledge graph are determined by the process in which it is created. There are different approaches to such processes; extraction or conversion of available data in the web (automated extraction of knowledge such as DBpedia from Wikipedia), community-created knowledge graphs, often by a group of experts, and hybrid approaches where humans maintain the knowledge graph alongside bots. We focus in this work on the hybrid approach of human edited knowledge graphs supported by automated tools. In particular, we analyse the editing of natural language data, i.e. labels. Labels are the entry point for humans to understand the information, and therefore need to be carefully maintained. We take a step toward the understanding of collaborative editing of humans and automated tools across languages in a knowledge graph. We use Wikidata as it has a large and active community of humans and bots working together covering over 300 languages. In this work, we analyse the different editor groups and how they interact with the different language data to understand the provenance of the current label data.