=Paper=
{{Paper
|id=Vol-2456/paper45
|storemode=property
|title=Monolith: an OBDM and Knowledge Graph Management Platform
|pdfUrl=https://ceur-ws.org/Vol-2456/paper45.pdf
|volume=Vol-2456
|authors=Valerio Santarelli,Lorenzo Lepore,Manuel Namici,Giacomo Ronconi,Marco Ruzzi,Domenico Fabio Savo
|dblpUrl=https://dblp.org/rec/conf/semweb/SantarelliLNRRS19
}}
==Monolith: an OBDM and Knowledge Graph Management Platform==
https://ceur-ws.org/Vol-2456/paper45.pdf
Monolith: an OBDM and Knowledge Graph
Management Platform
Lorenzo Leporea,b , Manuel Namicia,b , Giacomo Ronconia,b , Marco Ruzzia,b , Valerio
Santarellia,b , Domenico Fabio Savoc,b
(a) Sapienza Università di Roma
hlastnamei@diag.uniroma1.it
(b) OBDA Systems
hlastnamei@obdasystems.com
(c) Università degli Studi di Bergamo
domenicofabio.savo@unibg.it
Abstract. In this demo we present M ONOLITH, a novel application that com-
bines Ontology-based Data Management (OBDM) capabilities with a Knowl-
edge Graph platform. The idea of the system is to provide an integrated envi-
ronment offering OBDM services such as ontology inspection, query answering,
data quality checking, and to couple these services with a Knowledge Graph
IDE. Users can access and explore enterprise data through the ontology and
build Knowledge Graphs from them, by exploiting the underlying OBDM sys-
tem. M ONOLITH allows to query the Knowledge Graphs through a SPARQL end-
point, to integrate external resources such as Linked Open Datasets, and export
the Knowledge Graphs for third-party analytics.1
1 Introduction
Ontology Based Data Management (OBDM) [10] is a paradigm for data integration
and governance, which enables accessing existing enterprise data sources by means
of a comprehensible and semantically rich representation of the application domain,
expressed by an ontology. OBDM relies on a three-tier architecture: the ontology, the
data sources, and the mappings, which declaratively link the ontology predicates to the
data in the sources. In recent years, the enterprise and industry world have shown a
constantly growing interest in OBDM and its potential deep impacts on ITC scenarios
are widely recognized [1, 6, 8]. Another rising trend in enterprise data management are
Knowledge Graphs (KGs) [7]. A KG is a map of the data that is available across an
organization, that uses a graph structure (nodes and edges) to highlight the relationships
that exist between the data. These links can be defined in terms of the domain ontology,
providing semantics and meaning to the relationships. The graph model is extremely
flexible, as it can be applied easily to a wide range of use cases, and expandable, making
it easy to integrate new data sources. Also, it abstracts from the underlying application
data stores, meaning that it provides a means to abandon the dreaded data silo approach.
1
Copyright c 2019 for this paper by its authors. Use permitted under Creative Commons Li-
cense Attribution 4.0 International (CC BY 4.0).
Fig. 1. The M ONOLITH system.
In this demo we present M ONOLITH, a novel application developed by OBDA Sys-
tems, a spinoff of Sapienza University of Rome, that combines OBDM capabilities with
a semantic KG platform. The idea is to provide a system which offers OBDM services
such as ontology and mapping inspection, query answering, data quality controls with
respect to the ontology rules, and couples them with a KG IDE. Through semantic data
access provided by the M ASTRO OBDM reasoner [3], M ONOLITH allows to construct
RDF datasets and build KGs from them. Then, it allows to interrogate the KGs through
a SPARQL endpoint, integrate external resources such as Linked Open Data from the
semantic web, and export the KGs for third-party analytics.
2 The Monolith System: architecture and features
In this section we highlight the architecture and main features of M ONOLITH. As
shown in Figure 1, the functionalities provided by M ONOLITH can be logically split
into two different macro-areas: one dedicated to managing OWL ontologies and pro-
viding OBDM services through M ASTRO, exploiting the mappings between ontology
and database; the other to managing KGs and providing services over them. These two
sections are indeed linked together, allowing to build the KGs through semantic data
access, from the results of the ontology queries computed by M ASTRO.
In the Ontology section of the system, users can upload ontologies in OWL 2 and
G RAPHOL [9] format. G RAPHOL is a visual ontology language for OWL 2, whose
distinguishing features are that it allows for drawing ontologies completely diagram-
matically, and that it is equivalent to OWL 2. These ontologies can be navigated by in-
specting the documentation for each entity, in both a wiki-like textual form or through
a custom G RAPHOL viewer, both of which show the entity metadata, and the OWL
axioms they are involved in. Furthermore, users can create, inspect and edit the map-
pings, providing for each mapping a datasource connection, and run queries over the
ontologies through an OBDM SPARQL endpoint, producing tabular or RDF datasets.
A Query Catalog allows to store the more significant ontology queries for future re-use.
In Figure 2 we show a screenshot of M ONOLITH’s OBDM SPARQL endpoint.
Such OBDM query answering services are provided by the M ASTRO ontology rea-
soner, which supports data access through ontologies specified in logics of the DL-
Lite family [4]. In M ASTRO, the data source layer is seen as a relational database, and
Fig. 2. The ontology SPARQL endpoint in the M ONOLITH system.
in those cases in which several, possibly non-relational, sources need to be accessed,
M ASTRO can be coupled with a relational data federation tool, which wraps sources
and represents them as a single (virtual) federated relational database. Finally, the map-
ping layer is constituted by a set views over the database and mapping assertions [10]
which associate ontology elements with such views. By virtue of these design choices,
OBDM services, such as query answering, are realized in M ASTRO through a very
efficient technique that reduces them, via query rewriting [5], to standard SQL query
evaluation. In essence, the user query is reformulated with respect to the ontology and
to the mappings, in such a way that a new query, which encodes this reasoning and that
can be directly executed on the relational data sources, is produced.
In the Knowledge Graph section, users can create, import data into, and inspect
their semantic KGs. The RDF datasets that can be imported into a KG can be either the
results of M ASTRO SPARQL queries over the ontology, as previously mentioned, or
external datasets, thus allowing the possibility to integrate the data from the enterprise
data sources with Linked Open Data from other organizations or from the Semantic
Web community. For each dataset imported into the KG, the user can choose the named
graph, or context, in the KG into which the RDF triples will be imported. Exploration
of the data in each KG is provided by both a SPARQL endpoint over the KG, or by
a catalog of all the classes in each KG. For each instance of such classes, the system
provides a description, the list of RDF triples it is involved in, and the possibility to
download such triples in RDF format, choosing the desired RDF syntax.
From a technical standpoint, M ONOLITH is developed through the ReactJS
Javascript library, and communicates with the M ASTRO Java server for all OBDM ser-
vices through a RESTful web service interface. Management of the KGs is delegated
to the Apache Jena framework for Semantic Web and Linked Data applications, and,
specifically, RDF file storage and query is done through the TDB component of Jena.
3 Application scenarios and Demo Session Overview
We demonstrate M ONOLITH through two different specifications. Firstly, the ACI on-
tology and RDF datasets, developed in a joint project by Sapienza University of Rome,
the Automobile Club d’Italia (ACI), OBDA Systems, and OKKAM, a spinoff of the
University of Trento. The project goals were the definition of an ontology of ACI’s
Public Vehicle Register (PRA) and car tax domains, the development of an OBDM sys-
tem to access the data through such an ontology, and the creation of a web portal2 for
the publication of ACI’s car parc data in Linked Open format. The second specifica-
tion is the Movie Ontology [2], which provides a vocabulary to semantically describe
movie related concepts. For this ontology, we have developed a set of mappings for the
M ASTRO system to allow for semantic data access through OBDM.
During the demo, attendees will interact with the M ONOLITH system in the above
scenarios. They will exploit the OBDM services to inspect the ontology and mapping
specifications and to define queries through the ontology, and will be able to build and
then interrogate semantic KGs from the data extracted from such queries.
Acknowledgments. Work supported by Sapienza under the project “PRE-O-PRE” and
by MIUR, under the SIR project “MODEUS” - grant n. RBSI14TQHQ.
References
1. N. Antonioli, F. Castanò, C. Civili, S. Coletta, S. Grossi, D. Lembo, M. Lenzerini, A. Poggi,
D. F. Savo, and E. Virardi. Ontology-based data access: The experience at the italian depart-
ment of treasury. In Proc. of CAISE 2013, volume 1017 of CEUR Workshop Proceedings,
pages 9–16, 2013.
2. A. Bouza. Mo - the movie ontology, 2010. [Online; 26. Jan. 2010].
3. D. Calvanese, G. De Giacomo, D. Lembo, M. Lenzerini, A. Poggi, M. Rodriguez-Muro,
R. Rosati, M. Ruzzi, and D. F. Savo. The Mastro system for ontology-based data access.
Semantic Web J., 2(1):43–53, 2011.
4. D. Calvanese, G. De Giacomo, D. Lembo, M. Lenzerini, and R. Rosati. Tractable reasoning
and efficient query answering in description logics: The DL-Lite family. J. Autom. Reason-
ing, 39(3):385–429, 2007.
5. F. Di Pinto, D. Lembo, M. Lenzerini, R. Mancini, A. Poggi, R. Rosati, M. Ruzzi, and D. F.
Savo. Optimizing query rewriting in ontology-based data access. In Proc. of EDBT 2013,
pages 561–572. ACM Press, 2013.
6. M. Giese, A. Soylu, G. Vega-Gorgojo, A. Waaler, P. Haase, E. Jiménez-Ruiz, D. Lanti,
M. Rezk, G. Xiao, Ö. L. Özçep, and R. Rosati. Optique: Zooming in on big data. IEEE
Computer, 48(3):60–67, 2015.
7. J. M. Gómez-Pérez, J. Z. Pan, G. Vetere, and H. Wu. Enterprise knowledge graph: An
introduction. In Exploiting Linked Data and Knowledge Graphs in Large Organisations,
pages 1–14. Springer, 2017.
8. E. Kharlamov, D. Hovland, M. G. Skjæveland, D. Bilidas, E. Jiménez-Ruiz, G. Xiao,
A. Soylu, D. Lanti, M. Rezk, D. Zheleznyakov, M. Giese, H. Lie, Y. E. Ioannidis, Y. Ko-
tidis, M. Koubarakis, and A. Waaler. Ontology based data access in statoil. J. Web Semant.,
44:3–36, 2017.
9. D. Lembo, D. Pantaleone, V. Santarelli, and D. F. Savo. Easy OWL drawing with the graphol
visual ontology language. In Proc. of KR, pages 573–576. AAAI Press, 2016.
10. M. Lenzerini. Managing data through the lens of an ontology. AI Magazine, 39(2):65–74,
2018.
2
http://lod.aci.it