Publications

LinkedPipes Visualization is based on our research of easing access to Linked Data visualizations based on the vocabularies which should be used for certain types of data. Here is the list of our publications supporting our tool.

  • lightbulb_outlineEfficient Exploration of Linked Data Cloud
    Abstract
    As the size of semantic data available as Linked Open Data (LOD) increases, the demand for methods for automated exploration of data sets grows as well. A data consumer needs to search for data sets meeting his interest and look into them using suitable visualization techniques to check whether the data sets are useful or not. In the recent years, particular advances have been made in the field, e.g., automated ontology matching techniques or LOD visualization platforms. However, an integrated approach to LOD exploration is still missing. On the scale of the whole web, the current approaches allow a user to discover data sets using keywords or manually through large data catalogs. Existing visualization techniques presume that a data set is of an expected type and structure. The aim of this position paper is to show the need for time and space efficient techniques for discovery of previously unknown LOD data sets on the base of a consumer’s interest and their automated visualization which we address in our ongoing work.
    Authors
    Jakub Klímek, Martin Nečaský, Bogdan Kostov, Miro Blaško, Petr Křemen
    Venue
    DATA 2015, Colmar, France
    Citation
    Klímek J., Nečaský M., Kostov B., Blaško M., Křemen P.: Efficient Exploration of Linked Data Cloud, Proceedings of the DATA 2015 conference (DATA 2015), Colmar, France, July 2015, SCITEPRESS Digital Library, 2015.
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  • lightbulb_outlineUse Cases for Linked Data Visualization Model
    Abstract
    There is a vast amount of Linked Data on the web spread across a large number of datasets. One of the visions behind Linked Data is that the published data is conveniently reusable by others. This, however, depends on many details such as conformance of the data with commonly used vocabularies and adherence to best practices for data modeling. Therefore, when an expert wants to reuse existing datasets, he still needs to analyze them to discover how the data is modeled and what it actually contains. This may include analysis of what entities are there, how are they linked to other entities, which properties from which vocabularies are used, etc. What is missing is a convenient and fast way of seeing what could be usable in the chosen unknown dataset without reading through its RDF serialization. In this paper we describe use cases based on this problem and their realization using our Linked Data Visualization Model (LDVM) and its new implementation. LDVM is a formal base that exploits the Linked Data principles to ensure interoperability and compatibility of compliant analytic and visualization components. We demonstrate the use cases on examples from the Czech Linked Open Data cloud.
    Authors
    Jakub Klímek, Jiří Helmich, Martin Nečaský
    Venue
    LDOW 2015, Florence, Italy
    Citation
    Klímek J., Helmich J., Nečaský M.: Use Cases for Linked Data Visualization Model, Proceedings of the WWW2015 Workshop on Linked Data on the Web (LDOW 2015), Florence, Italy, May 2015, CEUR Workshop Proceedings, 2015.
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  • lightbulb_outlineVocabulary for Linked Data Visualization Model
    Abstract
    There is already a vast amount of Linked Data on the web. What is missing is a convenient way of analyzing and visualizing the data that would benefit from the Linked Data principles. In our previous work we introduced the Linked Data Visualization Model (LDVM). It is a formal base that exploits the principles to ensure interoperability and compatibility of compliant components. In this paper we introduce a vocabulary for description of the components and an analytic and visualization pipeline composed of them. We demonstrate its viability on an example from the Czech Linked Open Data cloud.
    Authors
    Jakub Klímek, Jiří Helmich
    Venue
    DATESO 2015, Nepřívěc u Sobotky, Jičín, Czech Republic
    Citation
    Klímek J., Helmich J.: Vocabulary for Linked Data Visualization Model, in Proceedings of the Dateso 2015 Workshop on DAtabases, TExts, Specifications and Objects (DATESO 2015), Nepřívěc u Sobotky, Jičín, Czech Republic, April 2015, CEUR Workshop Proceedings, ISBN 978-80-7378-285-6, pages 28-39, April 2015.
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  • ondemand_videoVisualizing RDF Data Cubes using the Linked Data Visualization Model
    Abstract
    Data Cube represents one of the basic means for storing, processing and analyzing statistical data. Recently, the RDF Data Cube Vocabulary became a W3C recommendation and at the same time interesting datasets using it started to appear. Along with them appeared the need for compatible visualization tools. The Linked Data Visualisation Model is a formalism focused on this area and is implemented by Payola, a framework for analysis and visualization of Linked Data. In this paper, we present capabilities of LDVM and Payola to visualize RDF Data Cubes as well as other statistical datasets not yet compatible with the Data Cube Vocabulary. We also compare our approach to CubeViz, which is a visualization tool specialized on RDF Data Cube visualizations.
    Authors
    Jiří Helmich, Jakub Klímek, Martin Nečaský
    Venue
    ESWC 2014 Demo track, Anissaras, Crete, Greece
    Citation
    Helmich J., Klímek J., Nečaský M., Visualizing RDF Data Cubes using the Linked Data Visualization Model, The Semantic Web: ESWC Satellite Events 2014 (ESWC 2014), Anissaras/Hersonissou, Crete, Greece, May 2014, Lecture Notes in Computer Science, volume 8798, Springer, ISBN: 978-3-319-11954-0, ISSN:0302-9743, pages 368-373, 2014.
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  • lightbulb_outlineApplication of the Linked Data Visualization Model on Real World Data from the Czech LOD Cloud
    Abstract
    In the recent years the Linked Open Data phenomenon has gained a substantial traction. This has lead to a vast amount of data being available on the Web in what is known as the LOD cloud. While the potential of this linked data space is huge, it fails to reach the non-expert users so far. At the same time there is even larger amount of data that is so far not open yet, often because its owners are not convinced of its usefulness. In this paper we refine our Linked Data Visualization Model (LDVM) and show its application via its implementation Payola. On a real-world scenario built on real-world Linked Open Data created from Czech open data sources we show how end-user friendly visualizations can be easily achieved. Our first goal is to show that using Payola, existing Linked Open Data can be easily mashed up and visualized using an extensible library of analyzers, transformers and visualizers. Our second goal is to give potential publishers of (Linked) Open Data a proof that simply by publishing their data in a right way can bring them powerful visualizations at virtually no additional cost.
    Authors
    Jakub Klímek, Jiří Helmich, Martin Nečaský
    Venue
    LDOW 2014, Seoul, Korea
    Citation
    Klímek J., Helmich J., Nečaský M., Application of the Linked Data Visualization Model on Real World Data from the Czech LOD Cloud, Proceedings of the WWW2014 Workshop on Linked Data on the Web (LDOW 2014), Seoul, Korea, April 2014, CEUR Workshop Proceedings, 2014.
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  • whatshotFormal Linked Data Visualization Model
    Best paper award
    Abstract
    Recently, the amount of semantic data available in the Web has increased dramatically. The potential of this vast amount of data is enormous but in most cases it is difficult for users to explore and use this data, especially for those without experience with Semantic Web technologies. Applying information visualization techniques to the Semantic Web helps users to easily explore large amounts of data and interact with them. In this article we devise a formal Linked Data Visualization Model (LDVM), which allows to dynamically connect data with visualizations. We report about our implementation of the LDVM comprising a library of generic visualizations that enable both users and data analysts to get an overview on, visualize and explore the Data Web and perform detailed analyzes on Linked Data.
    Authors
    Joseph Maria Brunetti, Sören Auer, Roberto García, Jakub Klímek, Martin Nečaský
    Venue
    IIWAS'13, Vienna, Austria
    Citation
    Brunetti J. M., Auer S., García R., Klímek J., Nečaský M.: Formal Linked Data Visualization Model in IIWAS '13: Proceedings of the 15th International Conference on Information Integration and Web-based Applications & Services, Vienna, Austria, December 2013, ACM New York, ISBN 978-1-4503-2113-6, pages 309-318, 2013.
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  • ondemand_videoPayola: Collaborative Linked Data Analysis and Visualization Framework
    Abstract
    Payola is a framework for Linked Data analysis and visualization. The goal of the project is to provide end users with a tool enabling them to analyze Linked Data in a user-friendly way and without knowledge of SPARQL query language. This goal can be achieved by populating the framework with variety of domain-specific analysis and visualization plugins. The plugins can be shared and reused among the users as well as the created analyses. The analyses can be executed using the tool and the results can be visualized using a variety of visualization plugins. The visualizations can be further customized according to ontologies used in the resulting data. The framework is highly extensible and uses modern technologies such as HTML5 and Scala. In this paper we show two use cases, one general and one from the domain of public procurement.
    Authors
    Jakub Klímek, Jiří Helmich, Martin Nečaský
    Venue
    ESWC 2013 Demo track, Montpellier, France
    Citation
    Klímek J., Helmich J., Nečaský M.: Payola: Collaborative Linked Data Analysis and Visualization Framework, in The Semantic Web: ESWC 2013 Satellite Events (ESWC 2013), Montpellier, France, May 2013, Lecture Notes in Computer Science, volume 7955, Springer, ISBN: 978-3-642-41241-7, ISSN:0302-9743, pages 147-151, 2013.
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