=Paper=
{{Paper
|id=Vol-2211/paper-42
|storemode=property
|title=Conceptual Schema Transformation in Ontology-based Data Access (Extended Abstract)
|pdfUrl=https://ceur-ws.org/Vol-2211/paper-42.pdf
|volume=Vol-2211
|authors=Diego Calvanese,Tahir Emre Kalaycı,Marco Montali,Ario Santoso,Wil van der Aalst
|dblpUrl=https://dblp.org/rec/conf/dlog/CalvaneseKMSA18
}}
==Conceptual Schema Transformation in Ontology-based Data Access (Extended Abstract)==
https://ceur-ws.org/Vol-2211/paper-42.pdf
Conceptual Schema Transformation in Ontology-based
Data Access (Extended Abstract)
Diego Calvanese1 , Tahir Emre Kalayci2,1 , Marco Montali1 , Ario Santoso3,1 , and
Wil van der Aalst4
1
KRDB Research Centre for Knowledge and Data, Free University of Bozen-Bolzano (Italy)
2
Virtual Vehicle Research Center, Graz (Austria)
3
Department of Computer Science, University of Innsbruck (Austria)
4
Process and Data Science (PADS), RWTH Aachen University (Germany)
During the last two decades, (structural) conceptual schemas have been increasingly
adopted not only to understand and document the relevant aspects of an application
domain at a high level of abstraction, but also as live, computational artifacts. In particular,
the paradigm of Ontology-Based Data Access (OBDA) exploits conceptual schemas
(also called ontologies) as an intermediate layer for accessing and querying data stored
inside legacy information systems [10]. In the context of OBDA, the conceptual schema
provides end-users with a vocabulary they are familiar with, at the same time masking
how data are concretely stored, and enriching those (incomplete) data with domain
knowledge. In this light, we call such a conceptual schema domain schema.
OBDA has been subject of extensive research, and its advantages have been con-
cretely shown in a variety of application domains (see, e.g., [4,6,7]). Surprisingly, though,
no research has been carried out on how to suitably extend the OBDA approach to handle
the common situation where a higher-level conceptual schema (which we call upper
schema) is needed to further abstract the knowledge captured by the domain schema.
This happens when there is the need of viewing the domain schema and, in turn, the
underlying data, according to a predefined structure, described by a reference model or
an interchange format. In addition, different users may need to generate different views
on the data, possibly using multiple upper schemas.
We illustrate the need for such a multi-level approach to data access on the common
situation where certain types of users adopt reference models as an upper schema to
understand the business relationships existing between an organization and its external
stakeholders. For example, the managers of an e-commerce company need to reconstruct
the state of contractual relationships and mutual commitments with customers, on top of
the domain concepts of orders, payments, and deliveries. At the same time, managers
employ the commitment-based core reference ontology for services (UFO-S) [8] as an
upper schema, to understand and monitor the state of commitments that contractually
relate the company and its customers. When managers need to inspect which commit-
ments currently exist, and in which state they are, they cannot directly formulate queries
of this form on top of the legacy data, due to a vocabulary mismatch.
A possible solution would be to create a dedicated OBDA specification that directly
connects the legacy data to the UFO-S upper schema. However, this is unrealistic from
the conceptual modeling point of view, for two reason. First and foremost, linking
data directly to concepts and relations in the upper schema requires to first understand
the data in terms of domain notions, and only then to establish suitable connections
between the domain and the upper level schemas. In addition, an OBDA specification
connecting data to the domain schema could be in place independently on these UFO-S
related needs. It is well-known that creating an OBDA specification, especially for
what concerns the understanding of the legacy data structures and the construction
of corresponding mappings, is a labor-intensive and challenging task [3], similarly to
alternative approaches to data access and integration. If such a specification is already in
place, it would be beneficial to build on it so as to gracefully integrate the upper schema
into the picture, instead of creating another OBDA specification from scratch. A second
issue is related to the fact that reference models and upper ontologies are typically meant
to capture a plethora of concepts and relations spanning over a wide range of application
domains, with the purpose of resolving ambiguities and misunderstandings [8,9,5]. In a
specific application domain, only a small portion of the whole reference model is needed
to capture the commitments of interest.
To attack these issues, we propose to adopt a standard OBDA approach to make sense
of the legacy data in terms of the domain schema. Once this OBDA specification is in
place, domain experts can forget about the schema of the legacy data, and work directly
at the level of the domain schema. In addition, a declarative specification is provided
that declares how the domain schema, e.g., of orders, can be transformed into (a portion
of) the UFO-S upper schema. Once the mapping and transformation rules are specified,
managers can express queries over UFO-S, with the aim to obtain answers that are
computed over the legacy data. This 2-level approach favors modularity and separation
of concerns, since the mapping and the transformation rules can vary independently from
each other. In particular, if the underlying data storage changes, only the mapping to the
domain schema needs to be updated, without touching the definition of commitments.
If instead the contract is updated, the domain-to-upper schema transformation needs to
change accordingly, without touching the OBDA specification.
To tackle such challenging but common scenarios, we propose to suitably extend
the OBDA framework so as to take into account multiple conceptual layers at once. We
focus on the case where two conceptual layers are present, accounting for the domain
and upper schemas, and call the resulting setting 2-level OBDA (2OBDA for short).
Specifically, our contribution is threefold. (i) We introduce the 2OBDA model as
an elegant extension of OBDA. The core part of the framework is the conceptual trans-
formation of concepts and relations in the domain schema into corresponding concepts
and relations in the upper schema. This is specified in a declarative way, similarly to
OBDA mappings but in this case accounting for ontology-to-ontology correspondences.
(ii) We show how a 2OBDA specification can be automatically compiled into a classical
OBDA specification that directly connects the legacy data to the upper schema, fully
transparently to the end-users. Consequently, these can query the legacy data through the
upper schema, by resorting to standard OBDA systems like ontop [1]. (iii) We realize
the approach in a tool-chain that supports end-users in modeling the domain and upper
schemas, and in specifying the corresponding transformations as annotations of the
domain schema, whose types and features are derived from the concepts in the upper
schema. Notably, the tool-chain fully implements the above compilation technique.
The full paper has been published in the proceedings of the 21st International
Conference on Knowledge Engineering and Knowledge Management (EKAW 2018) [2].
References
1. Calvanese, D., Cogrel, B., Komla-Ebri, S., Kontchakov, R., Lanti, D., Rezk, M., Rodriguez-
Muro, M., Xiao, G.: Ontop: Answering SPARQL queries over relational databases. Semantic
Web J. 8(3), 471–487 (2017). doi:10.3233/SW-160217
2. Calvanese, D., Kalayci, T.E., Montali, M., Santoso, A., van der Aalst, W.: Conceptual schema
transformation in ontology-based data access. In: Proc. of the 21st Int. Conf. on Knowledge
Engineering and Knowledge Management (EKAW). Lecture Notes in Computer Science,
Springer (2018)
3. Calvanese, D., Kalayci, T.E., Montali, M., Tinella, S.: Ontology-based data access for ex-
tracting event logs from legacy data: The onprom tool and methodology. In: Proc. of the 20th
Int. Conf. on Business Information Systems (BIS). Lecture Notes in Business Information
Processing, vol. 288, pp. 220–236. Springer (2017). doi:10.1007/978-3-319-59336-4 16
4. Daraio, C., Lenzerini, M., Leporelli, C., Naggar, P., Bonaccorsi, A., Bartolucci, A.: The
advantages of an ontology-based data management approach: Openness, interoperability and
data quality. Scientometrics 108(1), 441–455 (2016). doi:10.1007/s11192-016-1913-6
5. Guizzardi, G.: On Ontology, ontologies, conceptualizations, modeling languages, and
(meta)models. In: Databases and Information Systems IV – Selected Papers from the 7th
Int. Baltic Conference (DB&IS 2006). Frontiers in Artificial Intelligence and Applications,
vol. 155, pp. 18–39. IOS Press (2006), http://www.booksonline.iospress.nl/Content/
View.aspx?piid=5421
6. Kharlamov, E., Hovland, D., Skjæveland, M.G., Bilidas, D., Jiménez-Ruiz, E., Xiao, G.,
Soylu, A., Lanti, D., Rezk, M., Zheleznyakov, D., Giese, M., Lie, H., Ioannidis, Y.E., Kotidis,
Y., Koubarakis, M., Waaler, A.: Ontology based data access in Statoil. J. of Web Semantics
44, 3–36 (2017). doi:10.1016/j.websem.2017.05.005
7. Mehdi, G., Kharlamov, E., Savkovic, O., Xiao, G., Kalayci, E.G., Brandt, S., Horrocks, I.,
Roshchin, M., Runkler, T.A.: Semantic rule-based equipment diagnostics. In: Proc. of the
16th Int. Semantic Web Conf. (ISWC). Lecture Notes in Computer Science, vol. 10588, pp.
314–333. Springer (2017). doi:10.1007/978-3-319-68204-4_29
8. Nardi, J.C., de Almeida Falbo, R., Almeida, J.P.A., Guizzardi, G., Pires, L.F., van Sinderen,
M.J., Guarino, N., Fonseca, C.M.: A commitment-based reference ontology for services.
Information Systems 54, 263 – 288 (2015). doi:10.1016/j.is.2015.01.012
9. Scherp, A., Saathoff, C., Franz, T., Staab, S.: Designing core ontologies. Applied Ontology
6(3), 177–221 (2011). doi:10.3233/AO-2011-0096
10. Xiao, G., Calvanese, D., Kontchakov, R., Lembo, D., Poggi, A., Rosati, R., Zakharyaschev,
M.: Ontology-based data access: A survey. In: Proc. of the 27th Int. Joint Conf. on Artificial
Intelligence (IJCAI). pp. 5511–5519. Int. Joint Conf. on Artificial Intelligence Organization
(2018). doi:10.24963/ijcai.2018/777