De Vocht, Laurens

Dimou, Anastasia

Mannens, Erik

Van de Walle, Rik

Verborgh, Ruben

English

Ghent University Academic Bibliography

  biblio.ugent.be   The UGent Institutional Repository is the electronic archiving and dissemination platform for allUGent research publications. Ghent University has implemented a mandate stipulating that allacademic publications of UGent researchers should be deposited and archived in this repository.Except for items where current copyright restrictions apply, these papers are available in OpenAccess.   This item is the archived peer-reviewed author-version of:   Visualizing Research Networks evolution over time   Anastasia Dimou, Laurens De Vocht, Ruben Verborgh, Erik Mannes, and Rik Van de Walle     To refer to or to cite this work, please use the citation to the published version:   Dimou, A., De Vocht, L., Verborgh, R., Mannes, E., and Van de Walle, R. (2016). Visualizing ResearchNetworks evolution over time. Visualizing Research Networksevolution over timeAnastasia Dimou, Laurens De Vocht,Ruben Verborgh, Erik Mannens, and Rik Van de WalleGhent University - iMinds, Belgium {firstname.lastname}@ugent.beAbstract. Linked Data visualizations present a rather static view ofentities and their relations, despite the semantically rich data. In contrastto the spatial dimension, which is well-exploited, the temporal dimensionis neglected. In this demo, we address the temporal dimension, enablingtime-dependant visualizations, applied to research networks based onsemantically annotated research metadata. Such visualizations can revealinsightful information otherwise not able to be observed.1 IntroductionNon-Semantic Web experts lack in-depth knowledge of Semantic Web technolo-gies and Linked Data: they cannot form queries, nor traverse the semanticallyannotated information. Thus, they cannot conceive the potential this interlinkedinformation offers. Linked Data visualizations aim to support those users, yetmost visualizations offer a rather static view of entities and their relations. Eventhough the Linked Data spatial dimension is well-exploited so far, e.g., visual-izing Linked Data on different geographical maps, the temporal dimension isneglected. Most visualizations focus on a snapshot, i.e., the latest state of alldata accumulated, or just list time-stamped data on timelines or calendars.With this demo, we propose interactive visualizations which take into con-sideration the Linked Data temporal dimension. In our exemplary case, visual-izations reveal the existence and the nature of research networks, as well as thecollaborations and the emerging communities of practice, as they evolve overtime. They thereby provide insightful information regarding the evolution ofpatterns and trends over time, that allow non-Semantic Web experts to identifyrelationships and draw conclusions that could not be otherwise observed.2 State of the ArtIn most cases, visualizations are presented in a complementary fashion to theinformation presented. In terms of graph-based research network visualizations,the Semantic Web Journal [6], for instance, relying on its own dataset, providesresearch network visualizations as researchers author papers together or reviewdifferent submissions. ArnetMiner [9] distinguishes the networks (unlinked co-authors) from the communities of researchers (linked co-authors) into two differ-ent types of visualizations. ResXplorer [4] differs significantly, as its visualizationsprovide the optimal path between two entities (researchers, publications, papers,or conferences) presenting the same graph from different perspectives. However,none of these solutions capture the research networks evolution over time as ourvisualizations do, nor provide alternative paths between two specific researchers.Shneiderman [7] proposed seven basic datatypes of visualisations, assumingthat users view collections of items, where items have multiple attributes. Tempo-ral datatype is listed among them, covering (i) timestamped data (i.e., a precisemoment in time), but also (ii) items spanning through time with defined startingand end date. Proportionately, temporal data is suitable to be displayed in calen-dars and timelines, but time-series data is a perfect candidate to be mixed withother dimensions, allowing new analyses over data changing over time to detectpatterns and trends. Most existing solutions focus on the calendar/timeline typeof visualizations. For instance, the Linked Data Visualization Model (LDVM) [2]mapped temporal datatypes to timelines and calendars, in the same way as LD-VizWiz [1] and Sgvizler [8] that visualize data’s temporal dimensions only withtimelines, and VizBoard [10] that visualize them in calendars. On the contrary,our visualization suite provides both types of visualizations. In this demo, wefocus on time as a dimension to graph visualizations, showcasing them withresearch networks, allowing to observe changes as time passes.3 Research Network VisualizationFig. 1. The research network of an actual Semantic Web researcher:(a) time span: from 2008 to 2010, (b) precise time: in 2012, and (c) overallOur visualizations suite, lodvizsuite [5], supports two types of research net-work visualizations: (i) weighted graph-based with center displaying theresearch network around a researcher, in the dimension of time or not,and (ii) path-based displaying alternative paths between two particular re-searchers. A live demo of the research networks visualizations is available athttp://ewi.mmlab.be/time/.The first type of research network visualizations relies on the researcher’spublications and co-authors. The network is represented as weighted graph withcenter the researcher whose research network is examined. For instance, theoverall network of collaboration of an actual Semantic Web researcher appearsin (c) of Figure 1 based on accumulated data without taking into considerationthe time dimension. A timeline is introduced to adjust the time dimension. Theresearch networks can then display the network (i) at a precise time, e.g.in 2012, or (ii) in a time span, e.g., between 2008 and 2010, (a) and (b) ofFigure 1 respectively. Relying on different time snapshots, it is easy to observethe collaborations evolution over time. For instance, how the network growsover time, how it is shaped in a certain time-span, which collaborations arereinforced and which disappear. While the first type of visualizations is focusedon a single researcher and eventually established collaborations, the second typeis focused on the network formed between two researchers, displaying alternativeopportunities to establish collaborations. The visualizations display alternativepaths of collaborations between them, considering series of common publicationsand co-authors as intermediate steps. The visualizations, in this case, rely on anoptimized path-finding algorithm, the Everything Is Connected Engine [3].We implemented visualizations of the temporal dimension to our lodvizsuitesuite. We demonstrate it with the rilod dataset.Dataset. The visualizations were applied on top of the Research InformationLinked Open Data (rilod) dataset1 that contains information about 22,006researchers, 2,022 research groups, 24,635 projects and 608,729 publications,from 2008 till 2013, resulting in more than 400,000,000 triples.Vocabularies. The data is annotated with the Common European Research Infor-mation Format (CERIF)2 and the EWI Linked Open Data Ontology (ewilod)3.Implementation. The visualizations are part of the lodvizsuite, a suite of dataand schema-agnostic visualizations, easily applicable and transferable across dif-ferent datasets, offering an interactive interface on top of Linked Data. At clientside, the lodvizsuite consists of a number of JavaScript libraries relying onD3.js4 library, to display different types of visualizations. At server side, a Java-based implementation handles and processes the client’s requests and retrievesin real time the corresponding Linked Data according to sparql queries pub-lished at a dedicated DataTank5 instance. The DataTank publishes data on theWeb by transforming them in an http api, among them sparql templates. Pa-rameters can be passed to the sparql template at request time, which replaceplaceholders and constructs a valid sparql query. The visualizations are easilyapplicable to other datasets, only by adjusting the published sparql queries.1http://goo.gl/zKZbBM2http://www.eurocris.org/ontologies/cerif/3http://ewilod.be/ewilod/lod/0.1/ontology4http://d3js.org/5http://TheDataTank.comUsage. A user can access the visualizations and start typing a name in the searchbox. While typing, an auto-completion menu appears and the user may selectthe entity, in this case a researcher, to obtain the corresponding visualization.Then using the time frame at the bottom, the user may obtain a time snapshotby select either a precise time or a time span. Firstly, the user needs to indicatethe end time by clicking on one of the available dates, appearing under the timeslider. Then, he may select the start date, by clicking on one of the availabledates, appearing on top of the time slider. If the start and end date are the same,a visualization for this precise time is shown. If they are different, a visualizationof the corresponding time span is shown.In this paper, we demo how, taking advantage of the Linked Data temporaldimension captured, we can enable time-dependent visualizations and let usersidentify possible patterns and trends, otherwise not able to be observed.References1. G. A. Atemezing and R. Troncy. Towards a linked-data based visualization wizard.In ISWC 2014, 5th International Workshop on Consuming Linked Data, 2014.2. J. M. Brunetti, S. Auer, and R. Garc´ıa. The linked data visualization model. InInternational Semantic Web Conference (Posters & Demos), 2012.3. L. De Vocht, S. Coppens, R. Verborgh, M. Vander Sande, E. Mannens, andR. Van de Walle. Discovering meaningful connections between resources in theWeb of Data. In Proceedings of the 6th Workshop on Linked Data on the Web.CEUR-WS, 2013.4. L. De Vocht, E. Mannens, R. Van de Walle, S. Softic, and M. Ebner. A search in-terface for researchers to explore affinities in a linked data knowledge base. In Pro-ceedings of the 12th International Semantic Web Conference Posters and Demon-strations Track. CEUR-WS, 2013.5. A. Dimou, L. De Vocht, G. Van Grootel, L. Van Campe, J. Latour, E. Mannens,P. Mechant, and R. Van de Walle. Visualizing the information of a Linked OpenData enabled Research Information System. In Proceedings of the Current ResearchInformation Systems Conference, 2014.6. Y. Hu, K. Janowicz, G. McKenzie, K. Sengupta, and P. Hitzler. A Linked-Data-Driven and Semantically-Enabled Journal Portal for Scientometrics. In Proceedingsof the 12th International Semantic Web Conference. 2013.7. B. Shneiderman. The eyes have it: a task by data type taxonomy for informationvisualizations. In Visual Languages. Proceedings., IEEE Symposium on, 1996.8. M. G. Skjæveland. Sgvizler: A JavaScript Wrapper for Easy Visualization ofSPARQL Result Sets. In 9th Extended Semantic Web Conference (ESWC 2012),workshop and demo proceedings., 2012.9. J. Tang, J. Zhang, L. Yao, J. Li, L. Zhang, and Z. Su. ArnetMiner: Extraction andMining of Academic Social Networks. In Proceedings of the 14th ACM SIGKDDInternational Conference on Knowledge Discovery and Data Mining, 2008.10. M. Voigt, S. Pietschmann, and K. Meißner. A Semantics-Based, End-User-Centered Information Visualization Process for Semantic Web Data. In SemanticModels for Adaptive Interactive Systems. 2013.

Visualizing research networks evolution over time

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Visualizing research networks evolution over time

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