=Paper=
{{Paper
|id=Vol-1193/paper_88
|storemode=property
|title=OptiqueVQS: Visual Query Formulation for OBDA
|pdfUrl=https://ceur-ws.org/Vol-1193/paper_88.pdf
|volume=Vol-1193
|dblpUrl=https://dblp.org/rec/conf/dlog/SoyluKZJGH14
}}
==OptiqueVQS: Visual Query Formulation for OBDA==
https://ceur-ws.org/Vol-1193/paper_88.pdf
OptiqueVQS: Visual Query Formulation for OBDA
(Abstract)
Ahmet Soylu1 , Evgeny Kharlamov2 , Dmitriy Zheleznyakov2 , Ernesto Jimenez-Ruiz2 ,
Martin Giese1 , and Ian Horrocks2
1
University of Oslo; 2 University of Oxford
Motivation Ontology Based Data Access (OBDA) [16] is a recently proposed promi-
nent approach that aims at providing domain experts with a direct access to available
enterprise data sources without IT-experts being involved. OBDA is an alternative to
centralised approaches, where an IT-expert translates the requirements of domain ex-
perts into Extract-Transform-Load (ETL) processes to first integrate the data and then to
apply predefined analytical reporting tools. Currently, centralised approaches are com-
monly used in enterprises; they, however, can become too heavy-weight and inflexible
in some cases [12], that can be addressed the OBDA approach.
The key idea behind OBDA is to use ontologies to mediate between users and data.
Ontologies describe the domain of interest on a higher level of abstraction in terms that
are clear for domain experts, and introduce modeling concepts such as inheritance and
relationships between classes of objects, thus allowing to describe the intended mean-
ing of the ontological vocabulary. Ontologies have become a common and successful
mechanism to describe application domains in, e.g., biology, medicine, the (Semantic)
Web [13]. This success is partially due to a number of available formal languages for
describing ontologies, including RDF(S) [7] and OWL 2 [5] standardised by W3C.
In OBDA, users formulate their information needs as queries using terms defined
in the ontology, and ontological queries are translated into SQL and executed over the
data automatically, without an IT-expert’s intervention. To this end a set of mappings
is maintained that describe the relationship between the ontological vocabulary and the
elements of the schema of the underlying data.
The standard query language for ontologies is SPARQL [8]. Writing queries using
SPARQL, however, is not easy for domain experts and thus intuitive visual query for-
mulation support is required for OBDA systems. Existing OBDA systems, e.g., [1, 2, 9,
10, 18–21] typically offer limited or no visual query formulation support. Our goal is to
provide a solution for visual query formulation over ontologies that is specifically tai-
lored for OBDA systems. The solution should rely on solid theory, be efficient, support
interactive data exploration, and should follow the best Human-Computer Interaction
practices to guarantee good usability. In the following we give a short overview of our
ideas that were partially implemented in our OptiqueVQS system [22].
OptiqueVQS We first describe functionality of OptiqueVQS’ components and then
give their formal description. OptiqueVQS is a system for visual query formulation
support that allows the user to construct a query over an ontology step by step where at
each step the system provides the user with relevant information to continue the query
construction. OptiqueVQS has a widget-based architecture and exploits multiple repre-
sentation and interaction paradigms, see Fig. 1 for a screenshot where a sample query
SELECT DISTINCT ?c1 ?a1 ?c2 ?c3 WHERE{
?c1 ns1 : type ns2 : Field.
?c2 ns1 : type ns2 : Company.
?c3 ns1 : type ns2 : Wellbore.
?c3 ns1 : type ns2 : ShallowWellbore.
?c1 ns2 : currentFieldOperator ?c2.
?c1 ns2 : discoveryWellbore ?c3.
?c1 ns2 : name ?a1.
?c2 ns2 : name “Statoil Petroleum AS00 .
?c3 ns2 : wellborePurpose “APPRAISAL00 .
}
Fig. 1. Interface of OptiqueVQS
over an ontology for the Oil and Gas domain is composed1 together with its SPARQL
counterpart. The query asks for oil fields, wellbores operated on these fields, and com-
panies currently exploiting the fields. OptiqueVQS has three widgets: W1 employs the
graph metaphor, gives an overview of the constructed query, and allows further manip-
ulation of it, W2 employs the menu-based representation paradigm to visualise sugges-
tions that users can use to extend the query, W3 employs the form-based representation
paradigm to visualise possible constraints (projection and selection) that users can set
on different parts of the queries.
Query construction process in OptiqueVQS works as follows [3]. The user starts
with selecting in W2 a ‘starting’ suggestion, i.e., a class, from the list of available ones
and the selected suggestion appears in W1 and becomes ‘active’. Then, the user can
extend the query either by selecting in W2 one of the offered suggestions, i.e., a class
reachable from the active suggestion via some object property, or by setting constraints,
i.e., by restricting in W3 the data properties of the objects belonging to the class of
the active suggestion. W1 displays all selected suggestions and organise them in a tree.
The user can change the active suggestion by clicking on the ones in W1, or by adding
a new one through W2. For each active suggestion OptiqueVQS automatically gener-
ates relevant further suggestions in W2 and constraints in W3. The generation is done
via reasoning (e.g., extraction of classification, inferred domain and ranges) over the
ontology underlying the system and to this end we exploit the HermiT reasoner [17].
Moreover, users have partial control on output variables, can delete fragments of con-
structed queries, access query catalogue, save/load queries, and undo/redo actions.
Queries of OptiqueVQS. The queries follow the following grammar:
query ::= A(x)(∧constr(x))∗ (∧expr(x))∗ , where A is an atomic class,
expr(x) ::= sug(x, y)(∧constr(x))∗ (∧expr(y))∗ ,
constr(x) ::= ∃y R(x, y) | R(x, y) | R(x, c), where R is an atomic data property,
sug(x, y) ::= Q(x, y) ∧ A(y), where A and Q are atomic class and object property,
where variables y in different expressions expr(x) of a structure str are different. An
OptiqueVQS query is constructed using suggestions sug and constraints constr,
1
This ontology was designed for Statoil [4] as a part of the Optique project [15].
that are combined in expressions expr. Such queries are conjunctive and tree shaped:
the graph corresponding to the query where nodes are variables and edges are properties
is a tree. All the variables that occur in classes and object properties are output variables
and some variables occurring in data properties can also be output variables.
When users interacts with OptiqueVQS, then for every sug(x, y) that an expr(x)
starts with, that is, for every active suggestion, the system offers a list of relevant
constr(x) via W3 and relevant sug(y, z) via W2 that can be used to construct fur-
ther expr(y). We explore several notions of relevance, including local where offered
constraints and suggestions depend on sug(x, y) only, and global where they depend
on the entire query. We currently investigate complexity of suggestion generation for
different ontologies and notions of relevance.
Treatment of Data Properties. An important feature of OptiqueVQS is a special treat-
ment of data properties in W3: it automatically generates different end-user oriented
representations of data values, including sliders restricting possible ranges of numerical
values, such as age, depths, etc., and drop boxes with precomputed lists for categorical
data, such as names of companies, geographical locations, etc. Throughout empirical
evaluations we determined that this treatment of data properties is of high importance
for end-users. To support different intuitive representations for data properties, we en-
code relevant information in the ontology underlying the system and generate the rep-
resentations on the fly.
Query Construction vs Rewriting Ontology. We use OptiqueVQS for query formulation
in the Optique OBDA system, and thus we convert queries constructed via OptiqueVQS
in SPARQL and then they are processed by the Optique query processing component
[20] that rewrites them with the system’s ontology and unfolds it with mappings in
SQL. We use OWL 2 QL ontologies for query rewriting, while the query construction
is based on much richer OWL 2 ontologies that, in particular, make use of nominals.
There are both theoretical and practical reasons for having two ontologies: conjunctive
query rewriting for OBDA is well studied for OWL 2 QL ontologies [6], while for
effective and efficient query support of conjunctive query construction the expressive
power of OWL 2 QL ontologies is not sufficient. The query construction ontology that
we use in the system extends the query rewriting ontology.
Feedback in Query Construction. To improve query construction experience and to al-
low data exploration OptiqueVQS provides users with feedback at each step of query
construction: the users can see answers relevant to the constructed query. Since com-
putation of answers in OBDA systems is expensive, we investigate several possibilities
for the feedback: it can be, for example, a set of sample query answers, or a summary
of query answers, or some statistics on query answers. We currently investigate com-
plexity of different types of feedback. Moreover, we investigate influence of different
types of feedback on the usability of the system.
To Sum Up. We developed OptiqueVQS in cooperation with Statoil and did prelimi-
nary user evaluation with Statoil geologists that gave us encouraging results. We also
presented the system at several venues [11, 14, 22, 23]. Currently we investigate theo-
retical properties of our techniques. We also work on improvements of the system in
several directions, e.g., we develop ranking functions for suggestions and constraints.
References
1. http://virtuoso.openlinksw.com/
2. http://www.revelytix.com/content/spyder
3. OptiqueVQS. https://www.youtube.com/watch?v=ks5tcPZVHp0
4. Statoil. http://www.statoil.com/en/Pages/default.aspx
5. W3C: OWL 2 Web Ontology Language. http://www.w3.org/TR/owl2-overview/
6. W3C: OWL 2 Web Ontology Language Profiles. http://www.w3.org/TR/owl-profiles/
7. W3C: Resource Description Framework (RDF). http://www.w3.org/RDF/
8. W3C: SPARQL 1.1 Query Language. www.w3.org/TR/sparql11-query/
9. Bizer, C., Seaborne, A.: D2RQ—Treating non-RDF Databases as Virtual RDF Graphs. In:
ISWC (2004)
10. Calvanese, D., De Giacomo, G., Lembo, D., Lenzerini, M., Poggi, A., Rodriguez-Muro, M.,
Rosati, R., Ruzzi, M., Savo, D.F.: The MASTRO System for Ontology-Based Data Access.
Semantic Web 2(1), 43–53 (2011)
11. Cuenca Grau, B., Giese, M., Horrocks, I., Hubauer, T., Jimenez-Ruiz, E., Kharlamov, E.,
Schmidt, M., Soylu, A., Zheleznyakov, D.: Towards Query Formulation, Query-Driven On-
tology Extensions in OBDA Systems. In: OWLED (2013)
12. Doan, A., Halevy, A.Y., Ives, Z.G.: Principles of Data Integration. Morgan Kaufmann (2012)
13. Horrocks, I.: What are Ontologies Good for? In: Evolution of Semantic Systems, pp. 175–
188. Springer (2013), download/2013/Horr13a.pdf
14. Kharlamov, E., et al.: Optique 1.0: Semantic Access to Big Data: The Case of
Norwegian Petroleum Directorate’s FactPages. In: ISWC (Posters & Demos) (2013),
https://www.youtube.com/watch?v=PToyue4BFXA
15. Kharlamov, E., Jiménez-Ruiz, E., Zheleznyakov, D., Bilidas, D., Giese, M., Haase, P., Hor-
rocks, I., Kllapi, H., Koubarakis, M., Özçep, Ö.L., Rodriguez-Muro, M., Rosati, R., Schmidt,
M., Schlatte, R., Soylu, A., Waaler, A.: Optique: Towards OBDA Systems for Industry. In:
ESWC (SE). pp. 125–140 (2013)
16. Kogalovsky, M.R.: Ontology-Based Data Access Systems. Programming and Computer
Software 38(4), 167–182 (2012)
17. Motik, B., Shearer, R., Horrocks, I.: Hypertableau Reasoning for Description Logics. Journal
of Artificial Intelligence Research 36, 165–228 (2009)
18. Munir, K., Odeh, M., McClatchey, R.: Ontology-Driven Relational Query Formulation Using
the Semantic and Assertional Capabilities of OWL-DL. Knowledge-Based Systems 35, 144–
159 (2012)
19. Priyatna, F., Corcho, O., Sequeda, J.: Formalisation and Experiences of R2RML-Based
SPARQL to SQL Query Translation Using Morph. In: WWW (2014)
20. Rodriguez-Muro, M., Kontchakov, R., Zakharyaschev, M.: Ontology-Based Data Access:
Ontop of Databases. In: ISWC. pp. 558–573 (2013)
21. Sequeda, J.F., Miranker, D.P.: Ultrawrap: SPARQL Execution on Relational Data.
Journal of Web Semantics 22(0), 19 – 39 (2013), http://www.sciencedirect.com/sci-
ence/article/pii/S1570826813000383
22. Soylu, A., Giese, M., Jimenez-Ruiz, E., Kharlamov, E., Zheleznyakov, D., Horrocks, I.: Op-
tiqueVQS: Towards an Ontology-Based Visual Query System for Big Data. In: MEDES
(2013)
23. Soylu, A., Skjæveland, M., Giese, M., Horrocks, I., Jimenez-Ruiz, E., Kharlamov, E.,
Zheleznyakov, D.: A Preliminary Approach on Ontology-Based Visual Query Formulation
for Big Data. In: MTSR. pp. 201–212 (2013)