Ontology Based Data Access Ontology Based Data Access (OBDA)  Formally, an OBDA system is a triple O = T , S, M , where: -T is the intensional level of an ontology. We consider ontologies formalized in description logics (DLs), hence T is a DL TBox. -S is a relational database representing the sources. -M is a set of mapping assertions, each one of the form • Φ(x) is a query over S, returning tuples of values for x • Ψ (x) is a query over T whose free variables are from x. The main functionality of OBDA systems is query answering. A schematic description of the query transformation process (usually SPARQL to SQL) performed by a typical OBDA system is provided i

Davide Lanti

Diego Calvanese

Guohui Xiao

Josef Hardi

Mariano Rodríguez-Muro

Martin Rezk

Mindaugas Slusnys

Sarah Komla-Ebri

Timea Bagosi

CiteSeerX

The Ontop Framework for Ontology Based Data
Access
Timea Bagosi1, Diego Calvanese1, Josef Hardi2, Sarah Komla-Ebri1,
Davide Lanti1, Martin Rezk1, Mariano Rodŕıguez-Muro3, Mindaugas Slusnys1,
and Guohui Xiao1(B)
1 Faculty of Computer Science, Free University of Bozen-Bolzano,
Bolzano, Italy
xiao@inf.unibz.it
2 Obidea Technology, Jakarta, Indonesia
3 IBM T.J. Watson Research Center,
Yorktown Heights, NY, USA
1 Ontology Based Data Access
Ontology Based Data Access (OBDA) [4] is a paradigm of accessing data trough
a conceptual layer. Usually, the conceptual layer is expressed in the form of an
RDF(S) [10] or OWL [15] ontology, and the data is stored in relational databases.
The terms in the conceptual layer are mapped to the data layer using mappings
which associate to each element of the conceptual layer a (possibly complex
SQL) query over the data sources. The mappings have been formalized in the
recent R2RML W3C standard [6]. This virtual graph can then be queried using
an RDF query language such as SPARQL [7].
Formally, an OBDA system is a triple O = 〈T ,S,M〉, where:
– T is the intensional level of an ontology. We consider ontologies formalized in
description logics (DLs), hence T is a DL TBox.
– S is a relational database representing the sources.
– M is a set of mapping assertions, each one of the form
Φ(x) ← Ψ(x)
where
• Φ(x) is a query over S, returning tuples of values for x
• Ψ(x) is a query over T whose free variables are from x.
The main functionality of OBDA systems is query answering. A schematic
description of the query transformation process (usually SPARQL to SQL) per-
formed by a typical OBDA system is provided in Fig. 1. In such an architecture,
queries posed over a conceptual layer are translated into a query language that
can be handled by the data layer. The translation is independent of the actual
data in the data layer. In this way, the actual query evaluation can be delegated
to the system managing the data sources.
c© Springer-Verlag Berlin Heidelberg 2014
D. Zhao et al. (Eds.): CSWS 2014, CCIS 480, pp. 67–77, 2014.
DOI: 10.1007/978-3-662-45495-4 6
68 T. Bagosi et al.
SPARQL q
ontology
Datalog q′
mapping M
SQL q′′
data SABox A
+
rewriting
+
unfolding
+
ABox virtualisation
Fig. 1. Query processing in an OBDA system
2 The Ontop Framework
Ontop is an open-source OBDA framework released under the Apache license,
developed at the Free University of Bozen-Bolzano1 and currently acts as the
query transformation module of the EU project Optique2.
As an OBDA system, to the best of our knowledge, Ontop is the first to sup-
port all the following W3C recommendations: OWL, R2RML, SPARQL, SWRL
and SPARQL OWL 2 QL regime. In addition, all the major commercial and
free databases are supported. For each component of the OBDA system, Ontop
supports the widely used standards:
Mapping. Ontop supports two mapping languages: (1) the native Ontop map-
ping language which is easy to learn and use and (2) the RDB2RDF Mapping
Language (R2RML) which is a W3C recommendation.
Ontology. Ontop fully supports OWL 2 QL ontology language [11], which is a
superset of RDFS. OWL 2 QL is based on the DL-Lite family of description
logics [5], which are lightweight ontologies and guarantee queries over the
ontology can be rewritten to equivalent queries over the data source. Recently
Ontop is also extended to support the linear recursive fragment of SWRL
(Semantic Web Rule Language) [8,16].
Data Source. Ontop supports all the databases which implement SQL 99.
These include all major relational database systems, e.g., PostgreSQL,
MySQL, H2, DB2, ORACLE, and MS SQL Server.
Query. Ontop essentially supports all the features of SPARQL 1.0 and SPARQL
OWL QL Regime of SPARQL 1.1 [9]. Supporting of other features in
SPARQL 1.1 (e.g., aggregates, property path queries, negations) is ongo-
ing work.
1 http://ontop.inf.unibz.it
2 http://www.optique-project.eu
The Ontop Framework for Ontology Based Data Access 69
The core of the Ontop is the SPARQL engine Quest which supports RDFS
and OWL 2 QL entailment regimes by rewriting the SPARQL queries (over the
virtual RDF graph) to SQL queries (over the relational database).Ontop is able to
generate efficient (and highly optimized [13,14]) SQL queries, that in some cases
are very close to the SQL queries that would be written by a database expert.
The Ontop framework can be used as:
– a plugin for Protege 4 which provides a graphical interface for mapping editing
and SPARQL query execution,
– a Java library which implements both OWL API and Sesame API interfaces,
available as maven dependencies, and
– a SPARQL end-point through Sesame’s Workbench.
3 A Demo of the Movie Scenario
In this section, we describe a complete demo of Ontop using the movie sce-
nario [12]. The datasets and systems are available online3.
3.1 Movie Scenario Dataset
The Movie Ontology. The movie ontology MO aims to provide a controlled
vocabulary to semantically describe movie related concepts (e.g., Movie, Genre,
Director, Actor) and the corresponding individuals (“Ice Age”, “Drama”,“Steven
Spielberg” or “Johnny Depp”) [3]. The ontology contains concept hierarchies for
movie categorization that enables user-friendly presentation of movie descrip-
tions in the appropriate detail. There are several additions to the ontology ter-
minology due to the requirements in the demo, e.g., concepts TVSeries and
Actress.
IMDb Data. IMDB’s data is provided as text files4 which need to be converted
into an SQL file using a third party tool. Our IMDB raw data was downloaded
in 2010 and the SQL script was generated using IMDbPY5. IMDbPY generates
an SQL schema (tables) appropriate for storing IMDB data and then reads
the IMDB plain text data files to generate the SQL INSERT commands that
populate the tables. It can generate PostgreSQL, MySQL and DB2 SQL scripts.
In this demo we use a PostgreSQL compatible script and database takes up
around 6 GB on the disk.
Mappings. The mappings for this scenario are natural mappings that associate
the data in the SQL database to the movie ontology’s vocabulary. They are
“natural” mapping, in the sense that the only purpose of the mappings was
3 https://github.com/ontop/ontop/wiki/Example MovieOntology
4 http://www.imdb.com/interfaces
5 http://imdbpy.sourceforge.net
70 T. Bagosi et al.
to be able to query the data through the ontology. There was no intention to
highlight the benefits of any algorithm or technique used in Ontop. The first
version of the mappings for this scenario were developed by students of Free
University of Bolzano as part of an lab assignment. The current mappings are
the improved version of those create by our development team.
Queries. We included around 40 queries which are in the file movieontology.q
and can be used to explore the data set. The queries have different complexi-
ties, going from very simple to fairly complex. Note that some form of inference
(beyond simple query evaluation) is involved in most of these queries, in partic-
ular, hierarchies are often involved.
3.2 Using Protege Plugin
We demonstrate how to use Ontop as a protege plugin. The steps are:
(1) Start PostgreSQL with IMDb data.
(2) Start Protege with ontop plugin from command line.
Fig. 2. Movie ontology
The Ontop Framework for Ontology Based Data Access 71
Fig. 3. Movie mappings
(3) Open the OWL file movieontology.owl from Protege. The Ontop plugin
will also automatically open the mapping file movieontology.obda and
query file movieontology.q.
(4) Check the ontology and mappings. Two screen shots of the ontology and
mappings are shown in Figs. 2 and 3.
(5) Start the Quest reasoner from the menu.
(6) Run sample queries and check the generated SQLs. For example, we can
execute the query “Find names that act as both the director and the actor
at the same time produced in Eastern Asia” as shown in Fig. 4.
3.3 Using Java API
We show how the movie scenario can be implemented using the Ontop java
libraries through OWL API and sesame API. The complete code for the demo
is available online6.
Using OWL API. The OWL API is a Java API and reference implementa-
tion for creating, manipulating and serializing OWL Ontologies [2]. In the first
6 https://github.com/ontop/ontop-examples
72 T. Bagosi et al.
Fig. 4. Example query
example we use OWL API to execute all the 40 SPARQL queries over the movie
ontology, using the mapping in our obda format and a PostgreSQL database
with the IMDb data.
Ontop uses Maven to manage the dependencies. Since the release of version
1.10, Ontop itself has been deployed to the central maven repository. All artifacts
have the same groupId it.unibz.inf.ontop. In this example we use the OWL
API interface of Ontop, so we put the following in the pom.xml:
<dependency>
<groupId>it.unibz.inf.ontop</groupId>
<artifactId>ontop-quest-owlapi3</artifactId>
<version>1.12.0</version>
</dependency>
Moreover we need the dependency for PostgreSQL JDBC driver as shown below.
<dependency>
<groupId>postgresql</groupId>
<artifactId>postgresql</artifactId>
<version>9.0-801.jdbc4</version>
</dependency>
The Ontop Framework for Ontology Based Data Access 73
The files needed to start the Ontop reasoner are the ontology file
movieontology.owl and the obda file movieontology.obda. The obda file con-
tains both mappings and database settings. This allows to access the data in the
PostgreSQL database using the mappings in the OBDA model. First we load the
OWL file and OBDA file:
Next we create a new instance of the reasoner (QuestOWL reasoner), adding
the necessary preferences to prepare its configuration. We prepare the
Ontop supports a file format of multiple SPARQL queries. Here we execute
each query using the file movieontology.q of 40 queries. Within the instance each
SPARQL query is translated in an SQL query, which allows to retrieve the results
from the PostgreSQL database. For simplicity, we only display to the user the
number of results of the query and the time required for the execution.
74 T. Bagosi et al.
At the end of the execution we close all connections and we dispose of the rea-
soner.
Using Sesame API. OpenRDF Sesame is a de-facto standard framework for
processing RDF data and includes parsers, storage solutions (RDF databases a.ka.
triplestores), reasoning and querying, using the SPARQL query language [1].
In the second example we show how to create a repository and execute a single
query using Sesame API. First we need to add the Sesame API module of Ontop
as a dependency to the pom file pom.xml.
<dependency>
<groupId>it.unibz.inf.ontop</groupId>
The Ontop Framework for Ontology Based Data Access 75
<artifactId>ontop-quest-sesame</artifactId>
<version>1.12.0</version>
</dependency>
Then we set up the repository and create a connection. The repositories must
always be initialized first. We get the repository connection that will be used to
execute the query.
We load the SPARQL file q1Movie.rq which contains the same query that we
used for the Protege example.
Now we are ready to execute the query using the created Sesame repository
connection and output the results of the SPARQL from the database.
76 T. Bagosi et al.
Finally we close all the connections and release the resources.
//Close result set to release resources
result.close ();
// Finally close connection to release resources
System.out.println("Closing..,");
conn.close ();
Acknowledgement. This paper is supported by the EU under the large-scale inte-
grating project (IP) Optique (Scalable End-user Access to Big Data), grant agreement
n. FP7-318338.
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The Ontop framework for ontology based data access

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The Ontop framework for ontology based data access

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