=Paper=
{{Paper
|id=Vol-3640/paper15
|storemode=property
|title=WikiframeVG: A SPARQL Template-based Framework for Wikidata Graph Exploration and Visualization
|pdfUrl=https://ceur-ws.org/Vol-3640/paper15.pdf
|volume=Vol-3640
|authors=Darnelle Melvin,Andre Hulet,Cory K Lampert
|dblpUrl=https://dblp.org/rec/conf/wikidata/MelvinHL23
}}
==WikiframeVG: A SPARQL Template-based Framework for Wikidata Graph Exploration and Visualization==
https://ceur-ws.org/Vol-3640/paper15.pdf
WikiframeVG: A SPARQL Template-based Framework for
Wikidata Graph Exploration and Visualization
Darnelle Melvin1, *, Andre Hulet1 and Cory Lampert1
1 University Libraries. University of Nevada, Las Vegas, 4505 S Maryland Pkwy, Las Vegas, NV, 89154, USA
Abstract
We present WikiframeVG (Wikiframe Visual Graph), an open-source community initiative and tool
which helps Wikidata editors and users explore knowledge generated from organized Wikidata sprints.
WikiframeVG adopts a community driven, SPARQL template-based approach towards Wikidata graph
exploration. We define the Wikiframe end user and project scope, describe Wikiframe templates, its data
architecture and site architecture, introduce the user interface, present a discussion, and conclusion.
Keywords
Wikidata, Visualization Tools, Content Selection, Data Presentation 1
1. Introduction
In recent years, interest in galleries, libraries, archives, museums, and special collections
(GLAMS) has grown in Wikidata and Wikibase contributions and consumption [1-4]. Over time,
Wikidata [5] has become a large multilingual, crowdsourced, public knowledge graph of high-
quality general facts and a central hub of persistent identifiers [6, 7]. However, there remains a
barrier for those who lack technical or domain expertise, knowledge on how to write SPARQL [8]
for graph query or construction [9-11], let alone visualize or make sense of such a giant cloud of
data [12]. One solution to this problem is a graph-based discovery interface that extracts Wikidata
subsets and presents these nodes and edges into frames of interest. In this initial prototype, the
frame of interest shall focus on the GLAMS community. Here, we propose Wikiframe Visual Graph
(WikiframeVG),2 a tool which helps Wikidata editors and general browsers visualize and explore
knowledge generated from organized Wikidata sprints. WikiframeVG is an ecosystem built upon
open source technologies that provides SPARQL templates, property mappings, and
configuration files allowing users a no-code approach towards Wikidata graph exploration.
In this paper we define the Wikiframe end user and project scope (Section 2). Then describe
Wikiframe templates (Section 3), its data architecture (Section 4), site architecture (Section 5),
and introduce the user interface, including data previews, search techniques and graph
explorations (Section 6). We then present a discussion (Section 7) and end with conclusions and
final thoughts (Section 8).
2. End Users and Project Scope
The WikiframeVG end users are a diverse group of researchers and practitioners consisting of
developers, metadata librarians, digital repository managers, archivists, oral historians, digital
humanists and library technologists. These interdisciplinary participants come primarily from
the library and information science, museum studies, or digital humanities fields and tend to have
Wikidata’23: Wikidata Workshop at ISWC 2023
* Corresponding author.
darnelle.melvin@unlv.edu (D. Melvin); andre.hulet@unlv.edu (A. Hulet); cory.lampert@unlv.edu (C. Lampert)
0000-0002-4614-3504 (D. Melvin); 0009-0006-8772-9735 (A. Hulet); 0000-0002-9467-5214 (C. Lampert)
© 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
2 https://wikiframe.library.unlv.edu/
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
a wide range of technical experience. This segment includes community members interested in
incorporating SPARQL into existing and new workflows [13] and participants in both the
Program for Cooperative Cataloging (PCC) Wikidata Pilot3 and LD4 Wikidata Affinity Group.4 The
PCC Wikidata Pilot program ran from September 2020 through December 2021, where 76 PCC
and non-PCC institutions collaborated to help facilitate the communities move towards identity
management. The pilot was sponsored by the PCC - Task Group on Identity Management in NACO
[14].
To support the linked open data community, WikiframeVG was built upon open data and
designed for both expert and general use cases. Expert users have domain knowledge and may
know how to write SPARQL statements. Potential use cases for these types of users include
creating multiple options to visualize data (e.g. tables, maps, vertices and edges, or timelines),
providing mechanisms to combine linked open data with other datasets, and integrating new
ways to visualize similar things [15]. However, most potential users fall under the category of lay
users. Helmich [16] describes these users as novice or expert web navigators, but users who do
not possess knowledge of Semantic Web technologies such as RDF, SPARQL, or ontologies. These
users are our target audience and require better tools to present, organize and visualize
structured data.
The project scope for Wikiframe development came to fruition after a series of informal
conversations concluding the 2021 LD4 Conference on Linked Data [17]. Out of these talks,
community members realized a need to create a visualization tool based on a series of core
requirements:
• build it to utilize structured data in the public domain;
• build it using open-source technologies;
• create the ecosystem where no SPARQL experience is needed; and
• build it, govern, and sustain it as a community of practice.
These four requirements act as guiding principles for all current and future Wikiframe designs
and enable the community to build the application using a Wikiframe template approach.
3. Wikiframe Template
The Wikiframe template is a SPARQL statement that defines the scope of data for an element of
an application theme, such as the metadata describing resources and materials for GLAMS
organizations. A template creates a classification scheme that may or may not be directly
supported by the Wikidata ontology and available entities. In other words, whether Wikidata
defines an archival “collection” entity or not, one can write SPARQL that defines what a given
organization means by “collection.” In our case, we used instance of (wdt:P31) object values to
define “collection,” based on the data modeling methods of UNLV staff, (e.g., human (wd:Q5), oral
history (wd:Q558929), photo archive (wd:Q27032363), manuscript collection (wd:Q42939539),
etc.). The template also includes key/value pairs for essential properties statements and their
corresponding qualifiers of the class, such as properties: archival resource key (wdt:P8091),
archives at (wdt:P485), and oral history at (wdt:P9600). Examples of qualifiers include: subject
named as (pq:P1810), inventory number (pq:P217), and described at url (pq:P973). Every time the
template is executed in Wikiframe, it caches all items within the class and their essential
properties, which are then used by the search engine.
4. Data Architecture
3 https://www.wikidata.org/wiki/Wikidata:WikiProject_PCC_Wikidata_Pilot/Participants
4 https://sites.google.com/stanford.edu/ld4-community-site/groups#h.dm8tfdn1yc6h
To improve the speed of retrieval and to moderate the load on the Wikidata Query Service,5 data
returned by templates are cached in the Wikiframe back-end database. MySQL6 was selected for
this purpose, and is one of several database options that could have been used. Each result set is
cached in its own table in a flat, unnormalized structure, and Wikiframe application logic treats
each table as a distinct class of data. It was decided not to store results as semantic triples due to
the time and complexity needed to create a performant and easily updatable application that
relies purely on SPARQL for retrieval. The templated application classes simplify the structure
and possible combinations of result sets, making it relatively easy to re-create semantic
relationships on-the-fly using program iteration techniques.
Figure 1: WikiframeVG data architecture
Figure 1 shows the main components and data flow within Wikiframe. All search and retrieval
logic is written in Python7 and uses the Django web application framework8 to control database
access and perform any needed updates to the database structure. Configuration tables store the
SPARQL statements for execution against the Wikidata Query Service, and Wikidata is retrieved
either by an automated utility or from a utility page within Wikiframe.
While templates control the application theme’s data scope, a filter tagging system controls the
organizational data scope. The templates must include statements that retrieve only a single
organization’s data from the set of all data available within the theme. In our case, we use a filter
to set the on focus list of Wikimedia project (wdt:P5008) property value to UNLV’s project
identifier, WikiProject PCC Wikidata Pilot/University of Nevada, Las Vegas (wd:Q100202113).
Filters are defined and stored in their own configuration table. They consist of a filter key and a
corresponding property identifier. The filter key is saved in the template SPARQL in place of the
actual identifier using the tag format, ~[filt-key-name]~. At runtime this tag is replaced with
the identifier value stored in the configuration table. This approach allows organizations to reuse
a Wikiframe theme as-is, simply by placing their own identifier (in this case, it would be their
code for (wdt:P5008)) in the configuration table.
The Wikiframe cache is refreshed every 24 hours by an automated utility. The utility submits
the SPARQL template query to the Wikidata query service for each application class: Collections,
Oral Histories, People, Corporate Bodies, and Subjects. Each result is returned as a JSON
document which is parsed and saved to a MySQL table for the given class. Cache tables are then
queried by the Wikiframe search engine using Django’s Object Relational Mapping framework.
5 https://query.wikidata.org/
6 https://www.mysql.com/
7 https://www.python.org/
8 https://www.djangoproject.com/
5. Site Architecture
Figure 2: WikiframeVG site architecture
As shown in Figure 2, Wikiframe application logic is organized into four main components. Some
data, namely for charts, images, and all item details are retrieved live from Wikidata. These are
small record sets that don’t need to be cached and, in the case of item details, would unnecessarily
complicate caching. The search engine accepts keywords and phrases and graph node identifiers,
then runs appropriate queries, reconstitutes semantic relationships based on data mappings
stored in the source code, and passes that data to the graphing engine, Vis.js. 9 For instance, the
relationship type mapping for the Person class allows relationships according to occupation, field
of work, place of birth, and place of death, while the Corporate Body class mapping shows
relationships for “instance of” and the main subject. Users can then turn available relationships
between individual results on and off in the graph. Relationship and query mappings can be
updated via a configuration table and the mappings file.
The search execution process also generates graph rendering data, which Django passes to
the web page, where it is consumed by application JavaScript functions that render and
annotate the graph. This enables simultaneous search results lists and their accompanying
graph, which is a key aspect of our system use case: users should receive both authoritative
results and see the semantic relationships between them that drive further insight into the
contents of GLAMS information systems.
Table 1
WikiframeVG: Application Statistics
Measure Value
User Unique browser session 42.5
per week, avg.
Server Utilization
(6 cores, 16 GB RAM)
Weekly CPU utilization, 3
avg. pct.
CPU utilization, lifetime 73.17
max. pct.
RAM use, lifetime avg. pct. 8.73
RAM use, lifetime max. pct. 9.73
Wikidata Cache
Cache size, MB 2.8
Total rows 5017
Search Engine
Performance
Average response time for .24
Collections cache, seconds
9 https://visjs.org/
Table 1 shows performance statistics to date for WikiframeVG. As this application is still in
the proof of concept stage and has not yet been widely shared or publicized, the number of
connections has been low, and the small size of the UNLV data cache has enabled fast response
times and low server resource utilization with a minimal server specification. Due to the small
size of the cache, no index tuning has yet been performed. CPU load spikes when the caching
utility refreshes the cache, so caching will be optimized to support higher load scenarios.
6. User Interface: Data Preview, Search, and Graph Exploration
Figure 3: WikiframeVG data preview view
The current version of Wikiframe not only provides a mechanism to preview information about
the Wikidata within frame, but also provides a visual way to search and explore a predefined
subset of the knowledge graph. When loading the homepage, a live SPARQL query sends a request
to the Wikidata Query Service, and the results are dynamically visualized on the right-side pane.
As shown in Figure 3, the top ring chart provides a count of the number of instance of (wdt:P31)
object values while the lower chart counts occurrences of the main subject (wdt:P921) object
values. These two charts provide a clear overview of what the local Wikiframe instance contains
and what it does not.
The Wikiframe user interface incorporates exploratory search techniques [18], where a user
starts with a basic general search criterion, then from the initial results traverses the graph to
nodes of interest for additional information. The interface also incorporates faceted browsing [19]
views where the instance of (wdt:P31) object values are represented as dedicated classes in
search pages (e.g. Collections, Oral Histories, People, Corporate Bodies) or used within a page as
a facet filter in which one or more class values can be selected for search.
6.1. Collections
From the collections page, users can explore Wikidata pertaining to manuscript and
photographic collections. The index powering the search box is optimized to explore archival
collections by title, material type, or other descriptive details. For example, by using keywords
like "correspondence", or "mining" or "papers", the results return a graph and list of collections
where the collection nodes are clustered by subject. Another strategy is to use partial or full
names of known collections (e.g. type "Clinton" for the Clinton Wright Photographs). On the
collections page, Wikiframe users can also use a facet filter to construct a query by selecting the
checkbox next to the itemized list of subject headings.
Figure 4: WikiframeVG collections page: Result list and graph rendering from keyword
search
Figure 5: WikiframeVG collections page: Segment of the graph rendering in expanded view
6.2. Oral Histories
On the oral histories page, users can use the search box or facet filter to discover oral histories
of interest, by selecting one or more subjects from the facet filter tool. The search box index is
optimized to explore oral histories by title, a person’s name, or some descriptive detail. To find
an oral history based on an individual, a user can type all or part of a name, like "Hurtado" which
will return multiple oral histories from Mary and Alfred Hurtado. It is also possible to combine
keywords search and facet filter settings. For example, by typing the phrase "African American",
then selecting one or more subjects, will return oral histories related to African Americans. It is
worth noting that some combinations of keywords and facet filter selections can at times return
zero results.
6.3. People
Figure 6: WikiframeVG people page: Query construction using occupation facet filter
On the people page, users can also use the search box or facet filter to explore people of
interest, by selecting one or more occupations from the facet filter tool. The search index is
optimized to explore people by occupation or by name. You also can combine keywords and filter
settings. For example, by typing the word "Vegas", then selecting one or more occupations, will
result in occupations related to Las Vegas.
Figure 7: WikiframeVG people page: Graph rendering from query construction in expanded
view
6.4. Corporate Bodies
On the corporate bodies page, users can use the search box and filter tools to look for corporate
entities. The search index is optimized to explore corporate bodies by name or by type of business
(i.e. instance of (wdt:P31)). The user also can combine keywords and facet filter settings for
corporate bodies.
7. Discussion
WikiframeVG is available at: https://wikiframe.library.unlv.edu and currently retrieves only data
from UNLV’s Special Collections dataset. Moreno-Vega and Hogan [20] describe the challenge of
retrieving results across the entire Wikidata graph from a content management and performance
perspective, and they note that “investigating incremental indexing schemes would be an
important practical contribution” (315). In fact, Wikiframe’s frame-of-interest approach
significantly curtails the need to extensively index Wikidata content by simplifying the problem
of which data to index. The template construct enables asking complex questions about the
Wikidata graph, but sets those key questions as constraints on the availability of data within the
context they create.
The goal to visualize linked data and their relationships between special collections and
archives materials has been essential to the Wikiframe project team moving this work forward.
However, there are several areas of research that warrants further investigation, including
limitations and opportunities, future enhancements, and governance as a community of practice.
7.1. Limitations and opportunities
Using the template structure has limitations and opportunities. The template concept raises a
problem of omission: does the template in fact model all the available data for an application
class? If a subset of properties and identifiers defines the class at a given time, can the application
discover new properties or identifiers that might be relevant to the application scope? For
instance, if a set of collections are described as newspapers (wd:Q11032) but newer collections
are described as publications (wd:Q732577), how does Wikiframe recover? In our application
development process, we will investigate how the interplay of filters and new forms of
description can drive a discovery process that tags in-scope Wikidata entries needing review.
Outcomes might include modifications to underlying templates or adjustments to metadata
descriptive practices. While metadata quality was not a primary driver in the application
development, Wikiframe offers a compelling opportunity to view data in a new way and identify
areas where metadata creators can work to improve overall metadata quality, for example, by
observing disjointed nodes in the graph and adding properties to create links between two or
more nodes.
7.2. WikiframeVG future enhancements
Wikiframe can be expanded to include views besides graph visualization and tabular results;
we are particularly interested in exploring the possibilities related to geographic locations. In our
case, Melvin [21] devoted a Wikidata sprint to adding Wikidata that contained information on Las
Vegas-area LGBTQ people, organizations, and events, including organization and event
coordinate locations. Melvin wrote SPARQL queries to showcase these collections and their
relationships, showing gay businesses (gay bars, LGBTQ-friendly restaurants, coffee shops, and
other community businesses) on a map of the city, raising the visibility and awareness of this
community and of UNLV’s collections about it.10 This work has become the basis for a new
Wikiframe geographic visualization feature (Figure 8) that will be released in a future version.
Figure 8: WikiframeVG map prototype of corporate bodies plotted on layers organized
by instance of (wdt:P31)
7.3. WikiframeVG as a community of practice
Wikiframe is intended to be developed collaboratively by the Wikidata community. This
commitment to sharing the code for reuse and community contributions is a core principle of
open-source application development. By establishing a core codebase and making the
application available for others to develop additional templates, the application can be easily
customized for other datasets or research communities. The goal of sharing the application
openly is a key part of sustaining the work invested thus far. As more users test and interact with
the tool, it can be continuously improved or modified according to community needs and
priorities. Some of these enhancements may be completed by the original developer because of
community feedback, while others may happen over time as different members of the community
see how the tool can be used to visualize their unique data sets. The templates depend upon new
data being made available on Wikidata and on the writing of new SPARQL statements; two
activities that are robustly pursued within the Wikidata community. Sharing Wikiframe as a tool
that can leverage this work happening in the community has resulted in strong interest and
excitement as other organizations see the value of the work and want to contribute in their own
ways. This distributed approach to future Wikiframe development better ensures that the project
will remain active regardless of any one organization’s funding or resources.
8. Conclusion
There is significant interest in the GLAMS community to harness the power of Wikidata’s
extensive global knowledge graph and centralized source of persistent identifiers. But to realize
this potential, more organizations need tools that can overcome the barriers. These include: lack
of technical expertise, lack of experience and knowledge of Semantic Web technologies, and the
scarcity of available and easy to implement visualization software to fully express the richness of
the data. We developed WikiframeVG with the assumption that making sense of Wikidata or
relevant portions of Wikidata is a strategic project for professional disciplines and organizations,
10
https://www.wikidata.org/wiki/Wikidata:WikiProject_PCC_Wikidata_Pilot/UNLV#Visualize_Las_Vegas_LGBTQ+_Bus
inesses
and we developed this proof of concept to provide a no-code approach to Wikidata graph
exploration. WikiframeVG is a template-based solution that bypasses the common problems of
specialized library development projects that only work for one organization’s unique needs and
which become obsolete quickly as the organization lacks interest in long-term sustainability.
WikiframeVG is customizable, expandable, and can be collaboratively developed through
community cooperation over time as user needs evolve. As more GLAM community members
work with tools such as WikiframeVG and build collaborative tools, the whole community will
benefit and better leverage the power of Wikidata for their researchers and users.
Acknowledgements
This research has been funded by the University of Nevada, Las Vegas. Libraries - Dean’s
Leadership Circle through special projects funding. Source code is made available under the
Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0):
https://github.com/UNLV-Libraries/wikidata-discovery-project.
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