=Paper=
{{Paper
|id=Vol-2187/paper7
|storemode=property
|title=Dataset Dashboard - a SPARQL Endpoint Explorer
|pdfUrl=https://ceur-ws.org/Vol-2187/paper7.pdf
|volume=Vol-2187
|authors=Petr Křemen,Lama Saeeda,Miroslav Blaško,Michal Med
|dblpUrl=https://dblp.org/rec/conf/semweb/KremenSBM18
}}
==Dataset Dashboard - a SPARQL Endpoint Explorer==
https://ceur-ws.org/Vol-2187/paper7.pdf
Dataset Dashboard – a SPARQL Endpoint Explorer
Petr Křemen, Lama Saeeda, Miroslav Blaško, and Michal Med
Czech Technical University in Prague, Praha, Department of Cybernetics, Knowledge-based
Software Systems Group, Czech Republic
http://kbss.felk.cvut.cz
{petr.kremen,saeedla1,blaskmir,medmicha}@fel.cvut.cz
Abstract. We present the Dataset Dashboard, a SPARQL endpoint exploration
tool that helps to understand the dataset structure, as well as relationships to other
datasets. The tool is based on the notion of a dataset descriptor which describes
some characteristic of a dataset. Currently, the tool offers descriptors for basic
class/property statistics, spatial information, temporal information, as well as ad-
vanced dataset summarization. The tool currently registers over 200 SPARQL
endpoints and named graphs inside the SPARQL endpoints.
Keywords: Linked Data · SPARQL · Dataset Descriptor
1 Introduction
When trying to understand unknown RDF datasets1 , linked data consumers needs to
judge suitability of the dataset for his/her scenario in an efficient way. However, the
dataset exploration itself is a non-trivial and time-consuming task for many reasons.
First problem lies in the data – although many RDF datasets are cataloged, the metadata
gathered about these datasets are typically not put in sync with the actual dataset content
on the regular basis, or describe provenance metadata and do not reflect the dataset
content at all. Second important problem lies in the accessibility of the data, which
is often limited by (un)availability/latency of SPARQL endpoints, or efficiency of the
underlying hardware.
In this paper we aim at the problem of dataset exploration by users with technical
background who need to become familiar with unknown datasets (e.g. linked data de-
signers who link to external sources, data journalists who explore a new linked open
data source, etc.). As an example, let’s consider a linked data designer who needs to
find out, whether a new and unknown dataset is suitable for integration with another
dataset (s)he is designing. For this purpose, a bunch of exploratory SPARQL queries
need to be posed by the designer to become familiar with the vocabulary used in the
dataset, mutual interlinks between classes, their significance, as well as the spatial (e.g.
which spatial area it covers) or temporal (which temporal interval it spans) scope of the
dataset.
The introduced Dataset Dashboard tool aims at making the process of understand-
ing an unknown RDF dataset more efficient. For this purpose, the tool shows several
1
In this paper, we only consider RDF datasets available as subsets of actual snapshots of
SPARQL endpoints content.
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Dataset Dashboard - a SPARQL Endpoint Explorer
interactive visualizations of general content-level characteristics of the dataset, includ-
ing a filterable dataset summary, basic class and property statistics, as well as spatial
and temporal range of the dataset.
2 Dataset Dashboard Overview
Please refer to the RDF(S) specifications [6],[5] for related notions. We consider an
RDF graph of the form g = {hsi , pi , oi i}, where for each triple hsi , pi , oi i, si (resp. pi ,
oi ) is its subject (resp. predicate, object). A dataset descriptor is an RDF graph δ(g)
constructed from g which is easier to interpret and visualize than g itself. Function
δ : 2g → 2g , is the actual dataset descriptor function. The main purpose of the dataset
dashboard is to compute and visualize descriptors of various types.
Let’s introduce basic features of the Dataset dashboard2 , which are later presented
in Section 3 on an example.
Summary Schema Widget The basic summarization descriptor provided by the Sum-
mary Schema Widget is the SPO Summary, schematically described as
δSP O (g) = {hc1 , p, c2 i|{hx, rdf : type, c1 i, hy, rdf : type, c2 i, hx, p, yi} ⊆ g}
Additionally, frequencies of the summary triples are computed and visualized by the
weight (thickness) of the corresponding graph edge. The widget provides also an en-
hanced version of the SPO Summary descriptor which shows instance-level links to
other datasets. The technique to compute enhanced descriptors is currently under re-
view at the Journal of Web Semantics.
To explore large summaries, two filtering options are offered – content-based fil-
tering allows to exclude concrete classes/properties from the summary graph, while
weight-based filtering allows to exclude all edges with weight below a threshold, i.e.
filtering out triple patterns which are not frequent enough in the dataset.
Spatial Widget Spatial part of the data is represented by geographical objects, using
GeoSPARQL3 . Spatial Widget extracts GeoSPARQL geometries and displays them in
a map, schematically
δGeoSP ARQL (g) = {ho, p, gi|ho, p, gi ∈ g, for p ∈ {geosparql : asWKT, geosparql : asGML}}
The widget recognizes both Well-Known Text (WKT) geometry and Geography Markup
Language (GML) geometry. All types of features found in the dataset are listed in the
pop-up menu next to the map, where the user may choose which features to show in
the map. Point, line and polygon geometries are supported. Moreover, points are rep-
resented as markers with pop-up bubble containing the link to the RDF resource repre-
senting the feature.
2
Available on-line at https://onto.fel.cvut.cz/dataset-dashboard
3
http://www.opengeospatial.org/standards/geosparql, cit. 1.6.2018
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Dataset Dashboard - a SPARQL Endpoint Explorer
Temporal Widget Temporal widget shows the temporal coverage of the dataset, com-
puted as minimum and maximum time points occurring in the dataset. The computation
itself considers the structured temporal information as well as temporal information ex-
tracted from non-structured texts. For the unstructured temporal information, it retrieves
the textual literals and performs a natural language processing analysis to extract the
time information. To perform this step, the Stanford temporal tagger (SUTime)4 which
is part of the StanfordCoreNLP5 tool is used. The pipeline for computing the temporal
descriptor can be found in [10].
Dataset Dashboard Bookmarking In order to share a particular dataset dashboard
across the community, bookmarkable dataset dashboard URIs are offered.
3 Example
Let’s consider a dataset gexp about parcels, buildings, floors and land use which is a
part of the dataset maintained by the Prague Institute of Planning and Development6 .
Comparing to the original, gexp is limited to the data for Prague Centre, which results
in approx. 1.8 mil. triples.
Basic capabilities of the dataset dashboard are shown in Figure 1. The figure shows
an overview of gexp in terms of the SPO summary provided by the Summary Schema
widget. The full SPO summary contains 13 RDFS classes and 25 RDF properties. The
dataset dashboard allows to filter out some classes/properties from the schema. Addi-
tionally, edges can be filtered according to their weight. Thus, the figure excludes two
properties from the view (rdfs:label, ddo:has-published-dataset-snapshot)
and shows only edges with weight at least 12000 (adjustable by the slider control above
the SPO summary graph).
Spatial and temporal context of the dataset is shown in Figure 2. The GeoSPARQL
descriptor shows geometries attached functional land use (instances of the RDFS class
town-fvu:VyuzitiPloch). Dereferencable link of one of the geometries7 is shown
in a tooltip. The temporal descriptor shows the temporal range of the dataset. This
dataset provides temporal information only inside the properties town-parcely:dat vznik
and town-budovy:dat vznik, town-budovy:dat zmena, which denote the
creation/change dates of the data change record. So in this case, and contrary to the
GeoSPARQL descriptor, the extracted temporal knowledge refers rather to the creation
of the data than to the actual content. The dashboard of this example is shown at the
following URL
https://onto.fel.cvut.cz/dataset-dashboard/#/dashboard/online?
endpointUrl=http://onto.fel.cvut.cz:7200/repositories/ipr_
datasets&graphIri=http://onto.fel.cvut.cz/ipr-datasets/resource/
town-plan
4
https://nlp.stanford.edu/software/sutime.html
5
https://stanfordnlp.github.io/CoreNLP/
6
http://en.iprpraha.cz, cit. 1.6.2018
7
http://onto.fel.cvut.cz/ontologies/town-plan/pvp_fvu_p/
geometry/70/2018-01-29T14:36:24.178617
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Dataset Dashboard - a SPARQL Endpoint Explorer
Fig. 1: An example SPO summary and basic statistics widget.
(a) (b)
Fig. 2: Example temporal/spatial descriptors of the dataset.
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Dataset Dashboard - a SPARQL Endpoint Explorer
4 Technical Background
Dataset dashboard is a client-server application8 , as shown in Fig. 3a. The client side
serves mainly as a container for various widgets. Each widget is a separate React com-
ponent, which is able to visualize a dataset descriptor of one or more types (see Sec-
tion 4.1). The server takes care of proper creation and consumption of Dataset De-
scriptor Ontology metadata (see Section 4.1). In order to implement a new visual-
ization, one needs to implement a visual widget component and just insert it in the
DatasetDashboardController, along with the other widgets. Upon request, the
widget gets from the Server the result of one of the predefined SPARQL queries. For
more complicated scenarios, one needs to also implement an SPipes (see Section 4.2)
pipeline that computes the particular descriptor.
(a) Dataset Dashboard Architecture (b) Dataset Description Ontology Core
4.1 Dataset Descriptor Ontology
Dataset Descriptor Ontology (DDO) [4] is a domain ontology9 for describing the pro-
cess of dataset description, backed by the Unified Foundational Ontology (UFO) [8].
Simplified view on the ontology core is depicted in Figure 3b. The fundamental notion
of the ontology is the notion of a ddo:dataset, being an endurant entity which can
have different ddo:dataset-snapshots in time. Each dataset is available through some
8
https://kbss.felk.cvut.cz/gitblit/summary/?r=dataset-
dashboard.git, cit. 8.6.2018
9
http://onto.fel.cvut.cz/ontologies/ddo, cit. 4.6.2018
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Dataset Dashboard - a SPARQL Endpoint Explorer
ddo:dataset-source to which particular ddo:dataset-snapshots can be deployed by
an event of ddo:dataset-publication. Upon an event of describing a dataset source
(a ddo:description), a new ddo:dataset-descriptor is created as a description of a
ddo:described-dataset-snapshot. An example of a ddo:dataset-snapshot is a par-
ticular content of a SPARQL endpoint.
4.2 SPipes
SPipes is a custom implementation of the SPARQLMotion engine10 . It allows for cre-
ation RDF processing pipelines, consisting of modules (nodes) and their edges depen-
dencies (edges). A dependency says that first the source module is executed and its
resulting RDF graph together with global variable bindings is passed to the target mod-
ule. Then the target module is executed. The language is very well integrated with
SPARQL[13]. It allows reusing variables from SPARQL expressions in modules and
vice versa. It can be accessed through REST interface that is generated from definitions
of pipelines. Currently, SPipes are used to compute the SPO summaries and temporal
descriptors.
5 Related Work
An overview of exploration tools can be found in [3] and a more recent one in [9]. Let’s
discuss some of the tools in detail.
LODeX [2] is a tool (no on-line demo available any more) offering visualization
similar to the SPO summaries. Comparing to Dataset Dashboard, information about
frequent triple patterns of the dataset is not graphically visualized (and thus cannot be
used for filtering the summary).
LODSight [7] visualizes SPO summaries, in a similar way the Dataset Dashboard
does. Comparing to Dataset Dashboard, LODSight does not allow to filter the SPO
summary by the classes occurring in the data.
Linked Data Visualization Wizard [1] is an on-line tool11 detecting presence of sev-
eral types of information inside a SPARQL endpoint, including temporal information,
spatial information, statistical data or SKOS. in addition to detecting presence of the
information, the tool also shows examples of data for each type of information and the
query used to generate them. Comparing to Dataset Dashboard, graphical visualization
of the spatio-temporal characteristics and dataset summaries is missing.
Linked Geo Data Browser [11] allows to dynamically generate a faceted search
based on linked data geographically located inside a region selected by user in a map.
Comparing to Dataset Dashboard, it does not use the GeoSPARQL vocabulary and is
bound to the LinkedGeoData dataset12 .
Facete [12] is a tool providing faceted search over a SPARQL endpoint, present-
ing the results in a map.Map4rdf13 is a similar tool for GeoSPARQL-compliant data.
Comparing to Dataset Dashboard, both tools seem to support only point geometries.
10
http://sparqlmotion.org/, cit. 3.6.2018
11
http://semantics.eurecom.fr/datalift/rdfViz/apps, cit. 11.7.2018
12
http://linkedgeodata.org, cit. 11.7.2018
13
http://oegdev.dia.fi.upm.es/map4rdf/, cit. 1.6.2018
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Dataset Dashboard - a SPARQL Endpoint Explorer
Furthemore, comparing to the all mentioned tools, Dataset Dashboard provides a
comprehensive view of the dataset under exploration by combining different descriptors
into a single dashboard. Also, it stores the computed dataset descriptors14 and provide
persistent identifiers, for efficient sharing of the view over the dataset.
6 Evaluation
We conducted a preliminary survey about usefulness of the tool among three IT experts
(a PhD student in the semantic web field, a linked data expert and a semantic web de-
veloper) not involved in the system design and development. As a part of the survey
they had to explore three datasets (a SKOS vocabulary about labor law15 ), a complex
dataset about EU television content16 ), a dataset with geospatial and temporal infor-
mation about urban development in Prague17 ) not known to them before and judge the
benefits of using tool for their use-case. Although the experts were not provided with
any information about the tool beyond its SPARQL endpoint URL, all of them were
successful in describing what the topics of all three datasets are (mainly using the Sum-
mary Schema Widget). Two of them see the main advantage of the tool in support for
subsequent SPARQL query formulation to the particular SPARQL endpoint. The third
one sees its advantage in providing visualization revealing the complexity of the dataset.
Two of the experts find dataset dashboard persistent links useful for sharing information
about the datasets. On the other hand, for the purpose of dataset exploration, the experts
miss visualization of data samples and more advanced data statistics.
7 Conclusions
We presented the Dataset dashboard – a tool for dataset exploration using different
dataset descriptors. The tool currently registers over 200 SPARQL endpoints and named
graphs inside the SPARQL endpoints and is currently used in two national research
projects. Initial feedback by IT experts is motivating, revealing that the tool is useful
for dataset exploration, as well as providing suggestions for future work.
In future, we aim at providing history tracking for computed descriptors, as well
as introducing new descriptor types (e.g. data samples, as suggested by the survey) for
spatial and temporal widgets.
Acknowledgements This work was partially supported by grants No. GA 16-09713S
Efficient Exploration of Linked Data Cloud of the Grant Agency of the Czech Republic
and No.SGS16/229/OHK3/3T/13 Supporting ontological data quality in information
systems of the Czech Technical University in Prague.
14
Currently only for SPO summary and temporal descriptors.
15
http://vocabulary.wolterskluwer.de/PoolParty/sparql/
arbeitsrecht, cit. 10.7.2018
16
http://lod.euscreen.eu/sparql, cit. 10.7.2018
17
The dataset presented in Section 3
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Dataset Dashboard - a SPARQL Endpoint Explorer
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