Combining Faceted Search with Data-analytic Visualizations on Top of a SPARQL Endpoint

Petri Leskinen, Goki Miyakita, Mikko Koho, Eero Hyvönen

Research output: Contribution to journalConference articleScientificpeer-review

Abstract

This paper discusses practical experiences on creating data-analytic visualizations in a browser, on top of a SPARQL endpoint based on the results of faceted search. Four use cases related to Digital Humanities research in proposography are discussed where the SPARQL Faceter tool was used and extended in different ways. The Faceter tool allows the user to select a group of people with shared properties, e.g., people with the same place of birth, gender, profession, or employer. The filtered data can then be visualized, e.g., as column charts, with business graphics, sankey diagrams, or on a map. The use cases examine the potential of visualization as well as automated knowledge discovery in Digital Humanities research.
Original languageEnglish
JournalCEUR Workshop Proceedings
Volume2187
Pages (from-to)53-63
Number of pages11
ISSN1613-0073
Publication statusPublished - Oct 2018
MoE publication typeA4 Article in conference proceedings
EventVOILA 2018: Visualization and Interaction for Ontologies and Linked Data - Montery, United States
Duration: 8 Oct 20188 Oct 2018
http://voila2018.visualdataweb.org/

Fields of Science

  • 113 Computer and information sciences
  • Linked Data
  • visualization
  • 615 History and Archaeology
  • Biography
  • Prosopography

Cite this

Leskinen, Petri ; Miyakita, Goki ; Koho, Mikko ; Hyvönen, Eero. / Combining Faceted Search with Data-analytic Visualizations on Top of a SPARQL Endpoint. In: CEUR Workshop Proceedings. 2018 ; Vol. 2187. pp. 53-63.
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abstract = "This paper discusses practical experiences on creating data-analytic visualizations in a browser, on top of a SPARQL endpoint based on the results of faceted search. Four use cases related to Digital Humanities research in proposography are discussed where the SPARQL Faceter tool was used and extended in different ways. The Faceter tool allows the user to select a group of people with shared properties, e.g., people with the same place of birth, gender, profession, or employer. The filtered data can then be visualized, e.g., as column charts, with business graphics, sankey diagrams, or on a map. The use cases examine the potential of visualization as well as automated knowledge discovery in Digital Humanities research.",
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Combining Faceted Search with Data-analytic Visualizations on Top of a SPARQL Endpoint. / Leskinen, Petri; Miyakita, Goki; Koho, Mikko; Hyvönen, Eero.

In: CEUR Workshop Proceedings, Vol. 2187, 10.2018, p. 53-63.

Research output: Contribution to journalConference articleScientificpeer-review

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T1 - Combining Faceted Search with Data-analytic Visualizations on Top of a SPARQL Endpoint

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N2 - This paper discusses practical experiences on creating data-analytic visualizations in a browser, on top of a SPARQL endpoint based on the results of faceted search. Four use cases related to Digital Humanities research in proposography are discussed where the SPARQL Faceter tool was used and extended in different ways. The Faceter tool allows the user to select a group of people with shared properties, e.g., people with the same place of birth, gender, profession, or employer. The filtered data can then be visualized, e.g., as column charts, with business graphics, sankey diagrams, or on a map. The use cases examine the potential of visualization as well as automated knowledge discovery in Digital Humanities research.

AB - This paper discusses practical experiences on creating data-analytic visualizations in a browser, on top of a SPARQL endpoint based on the results of faceted search. Four use cases related to Digital Humanities research in proposography are discussed where the SPARQL Faceter tool was used and extended in different ways. The Faceter tool allows the user to select a group of people with shared properties, e.g., people with the same place of birth, gender, profession, or employer. The filtered data can then be visualized, e.g., as column charts, with business graphics, sankey diagrams, or on a map. The use cases examine the potential of visualization as well as automated knowledge discovery in Digital Humanities research.

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KW - Biography

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