=Paper=
{{Paper
|id=Vol-2187/paper5
|storemode=property
|title=Combining Faceted Search with Data-analytic Visualizations on Top of a SPARQL Endpoint
|pdfUrl=https://ceur-ws.org/Vol-2187/paper5.pdf
|volume=Vol-2187
|authors=Petri Leskinen,Goki Miyakita,Mikko Koho,Eero Hyvönen
|dblpUrl=https://dblp.org/rec/conf/semweb/LeskinenMKH18
}}
==Combining Faceted Search with Data-analytic Visualizations on Top of a SPARQL Endpoint==
<pdf width="1500px">https://ceur-ws.org/Vol-2187/paper5.pdf</pdf>
<pre>
        Combining Faceted Search with Data-analytic
        Visualizations on Top of a SPARQL Endpoint

        Petri Leskinen1 , Goki Miyakita2 , Mikko Koho1 , and Eero Hyvönen1,3
          1
          Semantic Computing Research Group (SeCo), Aalto University, Finland
                  2
                     KMD Research Institute, Keio University, Japan
     3
       HELDIG – Helsinki Centre for Digital Humanities, University of Helsinki, Finland
                          http://seco.cs.aalto.fi, http://heldig.fi



       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 proposog-
       raphy 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.

       Keywords: Linked Data, Visualization, Biography, Prosopography, Knowledge
       Discovery


1   Client-side Faceted Search on a SPARQL Endpoint

Faceted search and browsing [5,21], known also as view-based search [19] and dynamic
hierarchies [20], has become a norm in web applications. The idea here is to index data
items along orthogonal category hierarchies, i.e., facets 4 (e.g., places, times, document
types etc.) and use them for searching and browsing: the user selects categories on facets
in free order, and the data items included in the selected categories are considered search
results. After each selection, a count is computed for each category showing the number
of results, if the user next makes that selection. In this way, search is guided by avoiding
annoying ”no hits” results. Moreover, hit distributions on facets provide the end-user
with data-analytic views on what kind of items there are in the underlying database.
Faceted search is especially useful on the Semantic Web where hierarchical ontologies
used for data annotation provide a natural basis for facets, and reasoning can be used
for mapping heterogeneous data to facets [8]. The idea of combining faceted search and
visualizations has been applied, e.g., in ePistolarium5 . However, this application is not
based on Linked Data unlike ours [10,11,16,18].
     4
       The idea of facets dates back to the Colon Classification system of S. R. Ranganathan in
library science, published in 1933.
     5
       http://ckcc.huygens.knaw.nl/epistolarium/



                                             53
Faceted Search and Data-analytic Visualizations on Top of a SPARQL Endpoint

      Faceted search can be implemented with server-side solutions, such as Solr6 , Sphinx7 ,
  and ElasticSearch8 , and higher level tools, such as vuFind9 . However there is a lack of
  light-weight client-side faceted search tools or components that are able to search large
  datasets directly from a SPARQL endpoint. Such a tool is useful, because it can be used
  easily on virtually any open SPARQL endpoint on the web without any need for server
  side programming and access rights. This paper presents such a tool, SPARQL Faceter,
  a web component for implementing faceted search applications efficiently in a browser,
  based only on a standard SPARQL API. We extend our earlier short paper of the tool
  [14] by 1) showing detail about how the tool is used and works, by 2) explaining novel-
  ties in its latest version, and 3) especially show how the tool and faceted search can be
  extended with different kind of data-analytic visualizations.
      As a proof of concept, four use case studies of data visualization are discussed
  from a SPARQL Faceter perspective: 1) WarSampo, using cultural heritage materials
  of World War II in Finland [10]. 2) Norssit, on top of a Finnish high school alumni
  registry data [11]. 3) Semantic National Biography of Finland, based on the National
  biography of the Finnish literature society [16]. 4) U.S. Congress Prosopographer, uti-
  lizing biographical records of U.S. Congress legislators [18]. In these cases, the fol-
  lowing two-step prosopographical research method [22, p. 47] is supported where the
  goal is to find out some kind of commonness or average in selected target groups of
  people. First, a target group of people is selected that share desired characteristics for
  solving the research question at hand. Second, the target group is analyzed, and possibly
  compared with other groups, in order to solve the research question. For finding target
  groups, faceted search is used, and then visualizations are created in order to analyze
  their characteristics.
      The rest of the paper is organized as follows. First, characteristics of SPARQL
  Faceter are explained with a focus on showing how it is used in practice in applica-
  tions. After this, extending the tool with visualizations is in focus. In conclusion, lessons
  learned and directions for further research are discussed.


  2       Using and Extending SPARQL Faceter

  SPARQL Faceter uses AngularJS10 as the implementation framework. The GitHub
  page11 gives instructions how to install it, and how to define the application with facets
  of desired type in the source code. The page provides demo examples with queries
  to DBpedia and WarSampo databases. The developer can adopt it to any Linked Data
  publication by configuring the endpoint, property paths for facets, and queries. The
  SPARQL Faceter is documented in detail12 .
      6
         http://lucene.apache.org/solr/
      7
         http://sphinxsearch.com/blog/2013/06/21/faceted-search-with-sphinx/
       8
         https://www.elastic.co/
       9
         https://vufind.org/
      10
         https://angularjs.org/
      11
         https://github.com/SemanticComputing/angular-semantic-faceted-search
      12
         http://semanticcomputing.github.io/angular-semantic-faceted-search/#/api



                                               54
Faceted Search and Data-analytic Visualizations on Top of a SPARQL Endpoint


  PREFIX xsd:      <http://www.w3.org/2001/XMLSchema#>
  PREFIX schema:   <http://schema.org/>
  PREFIX skos:     <http://www.w3.org/2004/02/skos/core#>
  PREFIX skosxl:   <http://www.w3.org/2008/05/skos-xl#>
  PREFIX nbf:      <http://ldf.fi/nbf/>
  PREFIX crm:      <http://www.cidoc-crm.org/cidoc-crm/>
  PREFIX foaf:     <http://xmlns.com/foaf/0.1/>
  PREFIX gvp:      <http://vocab.getty.edu/ontology#>

  SELECT DISTINCT (?id AS ?id__uri) ?id__name ?value WHERE {
    # Restraints set in Faceter
    { ?id a nbf:PersonConcept ;
          foaf:focus/ˆcrm:P98_brought_into_life/
          nbf:time/gvp:estStart ?slider_2 .
      FILTER (1800<=year(?slider_2) && year(?slider_2)<=2018)
      }

    # Query person’s age
    ?id foaf:focus/ˆcrm:P100_was_death_of/nbf:time
        [ gvp:estStart ?time ; gvp:estEnd ?time2 ] ;
        foaf:focus/ˆcrm:P98_brought_into_life/nbf:time
        [ gvp:estStart ?birth ; gvp:estEnd ?birth2 ] .
    BIND (xsd:integer(0.5*
        (year(?time)+year(?time2)-year(?birth)-year(?birth2)))
      AS ?value)
    # Filter out erroneous cases
    FILTER (-1<?value && ?value<120)

    # Query for person’s name
    ?id skosxl:prefLabel ?id__label .
    OPTIONAL { ?id__label schema:familyName ?id__fname }
    OPTIONAL { ?id__label schema:givenName ?id__gname }
    BIND (CONCAT(
        COALESCE(?id__gname, ""),
        " ",
        COALESCE(?id__fname, ""))
      AS ?id__name)

  } ORDER BY ?value ?id__fname ?id__gname


                     Fig. 1. A SPARQL example for querying people’s ages




       Figure 1 depicts a SPARQL query for fetching the data visualized in Fig. 2. The
  first code block in the query pattern defines the restricted target group of the Faceter
  application, in this case we are interested in people who were born on or after the
  year 1800, a choice that has been made with the timespan slider. The example follows
  the data model of the National Biography of Finland [16], so to query for the desired
  resource in the data property paths are utilized. In the next block related events of birth
  are searched, and the age of a person is calculated. Errors in the data are filtered out
  by accepting only values in the range of 0 to 120 years. In the third block person’s
  proper name is constructed. Some of the fields are optional because we cannot assume
  all person entries to have both a first and a family name. The query returns a JSON
  formatted array consisting of objects containing the URI of the resource, the person
  name, and age. Notice that for linkage purposes, we also need the person information,
  not as histogram data with ages as bins with corresponding counts. In the application


                                             55
Faceted Search and Data-analytic Visualizations on Top of a SPARQL Endpoint

  the data is converted to a JavaScript array suitable for Google Chart tools13 . The output
  in this example case, (Fig. 2) is a column chart with age on the horizontal, and the
  amount of people on the vertical axis. A mouse click on any of the columns opens a
  modal list of all people having that age, from which the user can get to the detailed
  page of a person.




                            Fig. 2. Lifespan of people lived in 1800–2018.




  3        Applications

  In this section examples of visualizations on top of the SPARQL Faceter tool in different
  applications are shown and discussed.
      1) WarSampo is the first semantic portal for serving and publishing large hetero-
  geneous sets of linked open data about the World War II (WW2)14 . To create a global
  view of the war, and to attain a deeper understanding about its history, the portal con-
  tains e.g., some 95 000 death records of the Finnish WW2 casualties. This in-use portal
  includes 8 different application perspectives through different datasets, and had 130 000
  users in 2017.
      Fig. 3 shows a screenshot of the faceted search application in the casualties per-
  spective15 . The data is laid out in a table-like view. Facets are presented on the left of
  the interface with string search support. The number of hits on each facet is calculated
  dynamically and shown to the user, and selections leading to an empty result set are
  hidden. In Fig. 3, nine facets and the results are shown, where the user has selected
  “widow” in the marital status facet, limiting the search down to 279 killed widows that
  are presented in the table with links to further information.
      13
         https://developers.google.com/chart/
      14
         https://www.sotasampo.fi/en/
      15
         https://www.sotasampo.fi/en/casualties/



                                                   56
Faceted Search and Data-analytic Visualizations on Top of a SPARQL Endpoint

      The faceted search is used not only for searching but also as a flexible tool for
  researching the underlying data. In Fig. 3, the hit counts immediately show distributions
  of the killed widows along the facet categories. For example, the facet “Number of
  children”, which is not visible in the figure, shows that one of the deceased widows
  had 10 children and most often (in 89 cases) widows had one child. If we next select
  the category “one child” on its facet, we can see that two of the deceased are women
  and 86 are men in the gender facet. In the latest version of SPARQL Faceter, each facet
  component has a push button for visualizing the distributions with Google pie charts.




  Fig. 3. The faceted search interface of death records with one selected facet. The left side contains
  the facets, displaying available categories and the amount of death records for each selection.
  Death records matching the current facet selections are shown as a table.



      In addition to the table-like results view, the casualties perspective allows to visu-
  alize the results using three different visualization types: 1) age distribution (column
  chart), 2) personal life paths (sankey diagram), and 3) distribution of property values
  (bar chart) [13]. The method of results display does not affect the operation of facets, so
  the user can use same facet selections, and have different views of the resulting death
  records. Figure 4 shows a visualization of the life paths of soldiers that were buried in
  the cemetery of the municipality of Kolari. The visualization shows the known munic-


                                                 57
Faceted Search and Data-analytic Visualizations on Top of a SPARQL Endpoint

  ipalities where the individuals have been at different times of their lives, which also
  reveals the fact that perished Finnish soldiers were usually buried in their hometown.




                 Fig. 4. Life paths of 63 soldiers buried in the cemetery of Kolari.


      In addition to the casualties perspective, the WarSampo portal employs SPARQL
  Faceter in two other perspectives, photographs and cemeteries, to provide a user inter-
  face to search, browse and explore their data content.
      2) Norssit dataset consists of a register with over 10 000 alumni of the prominent
  Finnish high school “Norssi” in 1867–1992. The register was transformed into RDF,
  was enriched by data linking, was published as a linked data service, and is provided
  to end users via a faceted search engine and browser for studying their lives and for
  prosopographical research. [11]
      The Norssit portal16 contains two pages for visualizations17,18 . The pages use Google
  Charts showing search results as pie charts, column charts, or sankey diagrams [15]. An
  example of rendering the most common employers on different decades is depicted in
  Fig. 5.

     3) Semantic National Biography of Finland The National Biography of Finland19
  consists of biographies of notable Finnish people throughout history. The biographies
     16
        http://www.norssit.fi/semweb
     17
        http://www.norssit.fi/semweb/#!/visualisointi
     18
        http://www.norssit.fi/semweb/#!/visualisointi2
     19
        http://kansallisbiografia.fi



                                                58
Faceted Search and Data-analytic Visualizations on Top of a SPARQL Endpoint




          Fig. 5. Column chart showing the most common employers and their changes in time.




  describe the lives and achievements of historical figures, containing vast amounts of
  references to notable Finnish and foreign figures, including internal links to other bi-
  ographies. [12]
      To support the prosopographical research, the portal contains pages with faceted
  search where the data is visualized on Google Maps20 , or as column charts [15]. An
  example of rendering the query results on a map is depicted in Fig. 6. The portal also
  has a faceted search page for linguistic analysis of the vocabulary used in biographical
  descriptions.




                     Fig. 6. Places of birth and death of 17th century Finnish clergy




     20
          https://cloud.google.com/maps-platform/



                                                  59
Faceted Search and Data-analytic Visualizations on Top of a SPARQL Endpoint

      4) U.S. Congress Prosopographer This interface21 contains biographical records
  of 11 987 people who served in the U.S. Congresses from the 1st (1789) to the 115th
  (2018) one—converted and extracted from open-source data22,23 . The interface con-
  tains four integrated tools and demonstrates how historical patterns correspond to bi-
  ographical information and further intertwine with politics, economics, and historical
  knowledge alongside the American history.
      Being adapted from the previous studies above, novelty of this interface are the
  comparing visualizations. As shown in Fig. 7, a different set of target groups—in this
  case, the two major parties, Democrats and Republicans—can be analyzed and com-
  pared with each-other. The end user is able to find and execute new insights through the
  independent variables, as well as the latent biographical relationship of U.S. Congress
  legislators through selecting, filtering, and comparing two different accounts of histo-
  ries.




          Fig. 7. Examples of Comparing Visualizations: Democratic (left) / Republican (right)




     21
        https://semanticcomputing.github.io/congress-legislators/
     22
        https://github.com/unitedstates/congress-legislators
     23
        http://k7moa.com



                                                 60
Faceted Search and Data-analytic Visualizations on Top of a SPARQL Endpoint

  4   Related Work and Discussion

  The conference proceedings [2] include several papers on bringing biographical data
  online, on analyzing biographies with computational methods, on group portraits and
  networks, and on visualizations. Applying Linked Data principles to cultural heritage
  data [9] and historical research [17] has been a promising approach to solve the prob-
  lems of isolated and semantically heterogeneous data sources. Also a number of previ-
  ous research exists in faceted search [6,7] and Linked Data visualization [1,3,4].
      Based on the applications discussed, faceted search and browsing can be combined
  in a useful way with various means and tools for visualization: facet selections are a
  very flexible way to filter out result sets, and we have demonstrated that this can be
  done in real time using SPARQL queries in endpoints containing tens of millions of
  triples. Based on the query results, wrappers for data visualization tools, such as Google
  Charts for statistics or network analysis tools can be integrated and reused easily. By
  making the data analysis on the client side, computational burden can be distributed
  to end-user browsers, and Rich Internet Applications can be created without server-
  side programming. Moreover, the resulting visualizations open up ways of exploring
  new types of questions, and further evoke a knowledge discovery process in conducting
  digital humanities research.
      A key challenge in this approach is how to deal with large result sets. It is usu-
  ally not feasible to transfer very large result sets, say tens of thousands of casualty
  records in the WarSampo case, from the server to the browser. If the data is not avail-
  able in the browser, it cannot of course be analyzed there. This problem is solved in
  SPARQL Faceter by paginating the results; the results are uploaded in pages and only
  when needed. The end-user should be aware about the limitation that the visualizations
  are based on only the data that has been uploaded. The size of the page therefore sets a
  limit on how large datasets can be visualized, even though very large result datasets can
  be queried on the server side.
      The use case study WarSampo was implemented by the original SPARQL Faceter
  [14] while the other use cases discussed are based on its new versions with the following
  enhancements: 1) Every facet is now able to make its own SPARQL query (or many),
  which leads to better efficiency. 2) Hierarchical facets up to any number of levels are
  supported and more efficiently implemented. 3) Text search facet is included as a new
  facet type. 4) A slider facet for selecting a range of numerical values interactively can
  be used. 5) Facet hit distributions can be visualized using pie charts in addition to hit
  counts. There are also some enhancements made for visualizations. The U.S. Congress
  Prosopographer and Semantic National Biography allow, for example, visual compar-
  ison of two groups. The different Faceter versions and extensions need to be amalga-
  mated together in next versions of the tool and applications. Also the technical solutions
  for showing new types of visualization, such as social networks of people, should be
  studied more—we are about to implement a network application to the Semantic Na-
  tional Biography. Still another direction for further work are the aesthetic qualities. The
  visualizations are generated using standardized templates, e.g., web frameworks such
  as Google Charts, and balancing between usability and design aesthetics needs to be
  studied.


                                             61
Faceted Search and Data-analytic Visualizations on Top of a SPARQL Endpoint

  Acknowledgements Thanks to Erkki Heino for implementational help regarding ex-
  tending the Faceter SPARQL tool for our case studies, and to Jouni Tuominen for dis-
  cussions related to the data models and data services underlying our applications. Goki
  Miyakita was supported by a mobility scholarship at Aalto University in the frame of
  the Erasmus Mundus Action 2 Project TEAM Technologies for Information and Com-
  munication Technologies, funded by the European Commission. Our research was also
  supported by the CSC computing services and the Severi project24 funded mainly by
  Business Finland.

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</pre>