=Paper=
{{Paper
|id=Vol-3759/paper24
|storemode=property
|title=Towards Visual Federated SPARQL Queries
|pdfUrl=https://ceur-ws.org/Vol-3759/paper24.pdf
|volume=Vol-3759
|authors=Kārlis Čerāns,Uldis Bojars,Julija Ovcinnikova,Lelde
Lace,Mikus Grasmanis,Arturs Sprogis
|dblpUrl=https://dblp.org/rec/conf/i-semantics/CeransBOLGS24
}}
==Towards Visual Federated SPARQL Queries==
https://ceur-ws.org/Vol-3759/paper24.pdf
Towards Visual Federated SPARQL Queries
Kārlis Čerāns, Uldis Bojārs, Jūlija Ovčiņņikova, Lelde Lāce, Mikus Grasmanis, and
Artūrs Sproģis
Institute of Mathematics and Computer Science, University of Latvia, Riga, Latvia
Abstract
We demonstrate a method for visual creation of schema-backed federated queries that features schema
summary visualizations and context-aware auto-completion of queries, based on schemas of multiple
data sets. The method is implemented in the context of the ViziQuer query tool, based on the collection
of multiple stored data schemas, including schemas for DBpedia and Wikidata. The environment for
schema visualization and for the creation of visual federated queries is available as an online
playground and as an open-source software for local installation.
Keywords
Data schema, Schema visualization, SPARQL, Federated visual queries 1
1. Introduction
Writing a SPARQL query requires knowledge of both the SPARQL query language and the schema
of the data to be queried. Writing a SPARQL query over a federation of endpoints requires
knowledge of the schemas of all endpoints in the federation, which can be even more difficult.
We address this difficulty by providing a visual-centered system for (i) creating a visual
presentation of SPARQL endpoint data schemas in the style of UML class diagrams and (ii)
providing multi-endpoint schema support for creating visual federated SPARQL queries.
The use of the visual paradigm is generally acknowledged to ease the comprehension of the
data model or the data set structure since logically connected model entities can be shown
together. There is a multitude of visual tools for data schema/structure presentation using either
OWL ontology notation (cf. [1], [2]) or RDF data shapes expressed in SHACL or ShEx (cf. [3]).
The visual support for data queries allows to invoke the user’s visual perception capabilities
in the query building process and can be shown to be helpful in query creation at least for a range
of users and data queries (cf. [4]).
The main novel point of this paper is to demonstrate a single visual environment that
supports the visualization of multiple data endpoint schemas and provides a context-aware
visual query auto-completion over data set federations.
Both the data structure visualization and context-aware query auto-completion facilities rely
on the data schemas that need to be made available within the visual tool. Conceptually, the data
schema describes the data classes and properties, as well as their connections, preferably with
the size/frequency characteristics. Ontological domain/range information, cardinalities and
data types can be included, as well. We implement schema extraction directly from a SPARQL
endpoint (cf. [5]) to obtain the schema that exactly matches the data to be queried. The creation
of schemas from data dumps or importing them from (enriched) SHACL shapes can be envisaged,
as well.
The obtained schemas or their fragments can then be visualized in detailed and/or summary
form. These schemas can be used to support the construction of visual query diagrams, both by
visually presenting options for query expansion (available in the context of the visual query built
so far) and by auto-completing textual query fragments.
We develop the federated SPARQL query eco-system in the context of the ViziQuer notation
and tool environment (cf. [6]) that provides the capability for the visual creation of rich data
Proceedings Acronym: Proceedings Name, Month XX–XX, YYYY, City, Country
karlis.cerans@lumii.lv (K. Čerāns); uldis.bojars@lumii.lv (U. Bojārs)
0000-0002-0154-5294 (K. Čerāns); 0000-0001-7444-565X (U. Bojārs)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
queries. We substantially simplify the data schema visualization process, introduced in [7], by
introducing novel web-based in-tool data schema visualizations.
To support the development and visualization of federated queries, the capability for adding
the GRAPH/SERVICE descriptions was integrated into the ViziQuer syntax. The query auto-
completion structures were enriched to enable the use of multiple data schemas, and the SPARQL
query generation and SPARQL query visualization algorithms of [8] were expanded accordingly.
We illustrate the introduced concepts and the user interaction process in visual query
creation on a simple example of the StarWars data set (cf. [9]) connected to Wikidata.
The resources supporting the paper are available online2. The project can also be accessed
from the ViziQuer playground at https://viziquer.app (choose the StarWars example project).
2. Related Work
The visualization of the data structure and the support for visual query creation are based on the
concept of data set schema (cf. [6]) that can be extracted from a SPARQL endpoint. Since the data
set schema is based on the class and property inter-relations, most of its aspects can be encoded
in an RDF data shape language such as SHACL or ShEx. In this work, we use an abstract data set
schema concept whose information is easily enriched, for instance, with the information on the
frequency of entities and their relations, or other custom attributes. The functionality for
import/export between the schemas and ShEx/SHACL data shapes can be created, although
obtaining the additional information (e.g., the entity frequencies) from the data set (or from
some custom attributes, as made available in [10]) would be highly desired.
There is a multitude of tools for diagrammatic presentation of OWL ontologies (cf. [1,2,11] or
[12] for a survey). Some of the known UML style presentations of ShEx / SHACL data shapes are
[3] and [13]. The summarization of data schemas for visual presentation in a more compact form
relates to the landmark work on RDF data summarization [14], where legible query diagrams
can still be created only for rather moderate-size data schemas. The visualization of data
schemas used to support visual queries has been provided for the ViziQuer tool (cf. [7]); however,
previously, the visualization required a platform-specific (MS Windows) external component,
restricting and complicating the data visualization pipeline. In this paper, we demonstrate an
integrated web-based and platform-independent data schema visualization.
The assistants for visual SPARQL query creation include RDF Explorer [15], GRUFF [16], as
well as the schema-based OptiqueVQs [4], LinDA [17] and ViziQuer [6]. These tools, in their
existing versions, support queries over stand-alone SPARQL endpoints. This paper extends the
concept of schema-backed visual queries to be available over data endpoint federations, as well.
3. Data Schema Visualizations
In the context of (federated) SPARQL query creation, a visually presented data schema allows
the user to visually comprehend the data set entities and their connections to understand what
data are available for querying and to identify the entities and relations to be used in the query.
The visual schema compacting methods (cf. [7]) allow to increase the size of schemas that can be
legibly presented visually. Still, for larger and heterogeneous data sets such as DBpedia and
Wikidata only certain meaningful schema fragments can be expected to be reasonably visualized.
Figure 1 contains a condensed visualization of the StarWars data schema [9] used further in
this paper for demonstrating visual federated queries. The visualization employs class grouping
(e.g., 39 classes are grouped together in the Character et al. node) and the shortening of node
class lists (in the visual tool, the lists can be seen in full; the StarWars schema contains 51
classes). A finer-grained version of the schema (also using the concept of abstract superclass) is
available in the StarWars example project in the ViziQuer playground and the project support
page.
2 https://github.com/LUMII-Syslab/viziquer/tree/development/doc/demo/fed_queries
Figure 1: StarWars Data Schema Visualization
4. Federated Visual Queries
The visual query concept in ViziQuer is based on an (extended) tree of data nodes and control
nodes. A data node corresponds to a variable in the query pattern and can be optionally assigned
a class name from the schema class vocabulary (or a class name variable) and an instance name
(to be translated into a SPARQL variable name). A control node (a unit node or a union node) can
be used for further query structuring. There can be attribute expressions at the nodes building
up the query selection list, as well as the conditions. The data edges correspond to property-
based links among the node variables (property paths are allowed, as well), or they can be
marked by edge property variables. There can also be structural edges, labelled by ‘++’ (no data
connection specified by the edge) or ‘==’ (both edge ends correspond to the same instance).
The reader may consult [18] for further explanation and examples of the ViziQuer notation.
The root node in the query tree is depicted as an orange rounded rectangle (see Figure 2) that
determines the scoping of fragment-based query constructs such as OPTIONAL and subquery, as
well as the newly introduced GRAPH and SERVICE labels.
A federated query (as any other query) is created in the context of a certain data schema that
serves the class and property vocabulary (including the entity labels that can be used in the
visual query) and provides the resources for query auto-completion. To create a federated query,
a SERVICE specification can be introduced either at the node level or at the edge level to attribute
the node/edge and the entire query fragment behind it to another data schema and to include its
SPARQL code in a SERVICE block that is to be executed over the specified SPARQL endpoint.
Each running ViziQuer tool instance provides a list of available data schemas. These schemas
can be extracted from SPARQL endpoints by the OBIS Schema Extractor tool3 and they are stored
in the tool instance database by its administrator. The schemas for DBpedia and Wikidata,
created by custom extraction processes, are available, as well. Should a query involve a SPARQL
endpoint without the schema available in the visual query tool, the visual queries over it can still
be created, relying on the common and/or explicitly defined namespace prefix declarations.
Figure 2 contains examples of visual federated queries over an instance of the StarWars data
set [9], federated with remote data from Wikidata, and their translation to SPARQL.
3 https://github.com/LUMII-Syslab/OBIS-SchemaExtractor
Figure 2: Example federated queries and their translations to SPARQL (standard prefix
declarations omitted for presentation purposes)
Both queries in Figure 2 are initiated in the context of the StarWars data schema. They use
the :wikidataLink property (still within the StarWars data set) to find the stored Wikidata URIs
corresponding to the selected StarWars resources. These URIs are then used in the context of the
Wikidata schema and SPARQL endpoint to find related information – either the list of performers
for the StarWars characters or the count of students for each of them. In the second example, a
subquery within the query service fragment is created. We note that the auto-completion of
Wikidata properties wdt:[performer (P175)] and wdt:[student (P802)] was available for name
auto-completion within the query link-building dialogue.
Although the benefits of the visual notation are most apparent for simpler queries, the
ViziQuer tool allows users to create queries with a more complex structure, as well. These
features also apply to federated queries. The ViziQuer tool also supports the visualization of
existing textual SPARQL queries, with a rich set of full SPARQL constructs supported (cf. [8]).
This functionality has been extended to include the federated query scenarios.
5. Conclusions and Future Work
In this work we have demonstrated how the ViziQuer visual query environment can be used for
the visual creation of federated SPARQL queries backed by the data schemas of the SPARQL
endpoints involved in these queries and offering context-aware query element auto-completion
from the data structure described in multiple data schemas.
The visual presentation of the data schemas can help the user to comprehend the structure of
the data sets to be queried and to identify the entities to be used in the query.
To further enhance support for creating federated queries, the auto-completion mechanism
of the visual tool can be extended to include additional information about the possible cross-
schema class and property connections (e.g., by comparing the namespace parts of class instance
URIs). We plan to explore the options for this kind of functionality in the future.
Although the data schemas can be added to the query environment, an important future work
would be to expand the library of schemas (cf. [7]) ready to be used for federated query support.
Acknowledgements
This work has been partially supported by a Latvian Science Council Grant lzp-2021/1-0389
“Visual Queries in Distributed Knowledge Graphs”.
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