Ontology-Based Data Access (OBDA) Solutions

This assignment delves into the world of ontology-based data access (OBDA), where ontologies are used to manage and integrate data from multiple sources. It discusses the importance of OBDA in enabling seamless data sharing and collaboration, while also highlighting its applications in various industries such as energy exploration and service-based internet platforms. The document examines the technical aspects of OBDA, including query rewriting and materialization, as well as the use of ontology management systems like Optique. Furthermore, it explores the integration of OBDA with relational databases using tools like Ontop.

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COS4840_Assignment1
Question 1
Critically discuss the following definition of ontology: “An explicit specification of a conceptualization” (Gruber,
1993)
The word ontology has gained popularity within the knowledge engineering community. However, its meaning
remains a bit vague, as the term is used in different senses among various disciplines i(Guarino and Giaretta,
1995). Ontologies are explicit specifications of conceptualization ii(Gruber, 1993)); hence it serves as a
fundamental of many Information Systems as computational artefacts representing mainly domain knowledge.
In this context, conceptualization refers to an abstract model of some phenomenon in the world, like business,
that identifies that phenomenon’s relevant concepts, i.e. business concepts. Every knowledge base, knowledge-
based system, or knowledge-level agent is committed to some conceptualization, explicitly or implicitly (Gruber,
1993). Explicit means that the type of concepts used and the constraints on their use are explicitly defined and
formal means that the ontology should be machine understandable. Scientifically, ontology focuses on the formal
model of structures of a system which include identifying the entities and their relationships. The backbone of
ontology consists of specialization hierarchy. The term of conceptualization refers to an abstract model, a
simplified view of a particular domain of concern; hence the conceptualization term becomes explicit by formally
defining the concepts, relations and constraints. Formally, Ontology is defined as a tuple (O) = {C, R, HC, HR, I C,
I R, AO} iii(Yildiz, 2006)
 C: represents a set of concepts.
 R: represents a set of relations that relate concepts to one another. R i ∈ R and Ri → C × C.
 HC: represents a concept hierarchy in form of a relation H C C × C, whereas H C (C1, C2) means that C 1 is
a sub concept of C2.
 HR: represents a relation hierarchy in form of a relation HR _ R×R, whereas H R (R1, R2) means that R1 is
a sub relation of R2.
 I C and I R: represent two disjoint sets of concept and relation instances, respectively.
 AO: represents a set of axioms.
Ontology can be divided in two parts; an extensional and an intentional part. The extensional part is a set of
universe discourse which is represented by D (C, R, H C, H R) and intentional part is represented by R (I C, I R). In
general, ontologies consist of a set of concepts and description of the relationships that hold between these
concepts (Yildiz, 2006). Ontologies are useful for improving accuracy of Web searches. The key role that
ontologies are likely to play in the future of the Web has led to the extension of Web mark-up languages. In the
context of the Semantic Web, an ontology language should:
 be compatible with existing Web standards,
 define terms precisely and formally with adequate expressive power,
 be easy to understand and use,
 provide automated reasoning support,
 provide richer service descriptions which could be interpreted by intelligent agents,
 be sharable across applications.
Ontology languages can be more or less formal. The advantage of formal languages is the reasoning mechanisms
which appear in every phase of conception (satisfiability, sub-sumption, etc.), use (query, instantiation) and
maintenance of ontology. The complexity of underlying algorithms depends on the power and the semantic
richness of the used logics. When querying ontology, a user does generally not have the global knowledge of the
ontology schema. The language should thus allow him to query both the ontology schema and its instances in a
consistent manner
Question 2
Write an essay on the following uses of ontologies: semantic web, semantic interoperability, and ontology-based
data access.
Semantic Web
Semantic Web is an evolution of the World Wide Web Consortium (W3C), an extension of the current web in
which information is well defined, enabling computers and human beings to interact seamlessly iv(Berners-Lee,
2003). The major goal for Semantic Web is to trigger the evolution of the existing web to enable users to search,
discover and share information with less effort. The Semantic web can be considered a vision for the future in
which data could be instantly structured and interpreted by machines, allowing them to process numerous tedious

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tasks related to discovering, blending and taking action on the information available on the web. Semantic web
uses graph database to store data and allows data to be structured in a way that computers can easily process
and analyse the data.
The idea behind the Semantic web is to weave a Web that not only links documents to each other but also
recognise the meaning of the information in those documentsv(Frauenfelder; 2001), in other words, to transform
the current web from a series of interconnected but ultimately semantically isolated data island into one gigantic,
personal information storage, manipulation and retrieval database.
Most of the web contents are designed for humans to read, not for computer programs to manipulate
meaningfully vi(Berners-Lee et al; 2001). Computers can adeptly parse web pages for layout and routine
processing e.g. the header, a link to another page but in general computers have no reliable way to process the
semantics or the meaning of the page content.
There are two building blocks for semantic web, which are Resource Description Framework (RDF) and Ontology.
RDF is a foundation for processing metadata, which provides interoperability between applications that exchange
machine-readable information on the semantic web. Whereas ontology plays a vital role in the semantic web and
is a source of precisely defined terms and properties in the domain, which can be used in annotations for
communication.
The Semantic Web can be divided into various layers of metadata, each level providing different degrees of
expressivity, as shown on the figure 1 vii(Berners-Lee, 1998). In the following section, Semantic Web formalisms
will be described, starting from the bottom of the stack.
Figure 1. Semantic Web Stack (Berners-Lee, 1998)
The Semantic Web structure might be arranged in such way:
 Each resource is identified with Unicode and Unified Resource Identifier (URI),
 Each resource is described with a formal language (for example with RDF),
 The relations among resources are described with the help of ontology so resources are linked to each
other,
 Construct is a powerful logical language to make semantic Web expressive enough to help us in a wide
range of situations
 Contain proof checking mechanisms and trust dependent on context.

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XML is a first level of semantics which allow users to structure data with regard to their content rather than their
presentation viii(Yergeau et al., 2004). XML tags may represent the meaning of data whereas HTML tags indicate
the way data should be displayed. Namespaces allow the unambiguous use of several vocabularies within a single
document, by indicating explicitly which set a term belongs to. The Resource Description Framework (RDF)
(W3C, 1999) syntax was designed to represent information about resources in the World Wide Web. RDF provides
a common framework for expressing semantic information about data so that it can be exchanged between
applications without loss of meaning. RDF identifies things with Web identifiers called URIs and describes
resources in terms of properties and property values.
A layered architecture was proposed for the Semantic Web languages (Berners-Lee, 1998), among which XML,
XML Schema, RDF and RDFS. RDFS defines classes and properties, range and domain constraints on properties,
subclass and sub-property as sub-sumption relations.
Advantages of Semantic Web are as follows: -
 Search
 Agents
 Knowledge management (KM)
 Integration
 Composition of complex systems
 Multimedia collection
 Information filtering
 Machine dialogue across the domains
 Virtual community
 Online advertising
 Serendipity (unexpected benefits)
 Vocabulary flexibility & standardization
Semantic Interoperability
The term interoperability has been used in the context of electronic government research to define the ability of
independent units to work together, seamlessly and effective in the manner that has been agreed properly in
advance. It has been used in computing; with similar interpretation for example the IEEE has defined it as the
ability of two or more systems or components to exchange information and to use the information which has
been exchanged ix(Mcllraith et al., 2001). The notion of semantic interoperability is characterised by the use of
higher level description language or semantic web technologies. Semantic interoperability relies on the definition
of set of rules and mapping between different contexts, hence integrating resources developed using different
vocabularies and perspectives on data. Semantic interoperability transmits data and exchange information while
allowing each system to process information independently and allows two independent programs to derive same
conclusions for same data x(Alumbaugh et al., 2003).
It is a requirement to enable machine computable logic, inferencing, knowledge discover and data federations
between information systems. Syntactic interoperability is a pre-requisite for semantic interoperability and
syntactic interoperability refers to the packaging and transmission mechanism of dataxi(Petcu, D; 2011).
Semantic interoperability is achieved when interacting systems attribute the same meaning to an exchanged
piece of data, ensuring consistency of the data across systems regardless of individual data format. This
consistency of meaning can be derived from pre-existing standards or agreements on the format and meaning
of data.
Ontology Based Data Access.
Ontology Based Data Access (OBDA) is a prominent approach to query databases which uses ontology to expose
data in a conceptually clear manner by abstracting away from the technical schema-level details of the underlying
data xii(Kharlamov et al., 2015). The ontology is connected to the data via mappings that allow to automatically
translate queries posed over the ontology into data-level queries that can be executed by the underlying database
management system. OBDA system rewrites such queries and ontologies into the vocabulary of the data sources
and then delegates the actual query evaluation to a suitable query answering system such as a relational
database management system.
OBDA gives a high-level conceptual view of the data, provides the user with a convenient vocabulary for queries,
it allows the system to enrich incomplete data with background knowledge and support queries to multiple
heterogeneous data sources. There are three types of OBDA which are: OBDA with database which allow a
reduction of conjunctive queries over ontologies to first order queries over standard relational databases; the
second one is OBDA with datalog engine which support a datalog reduction and can be used with datalog

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engines.; the third one is OBDA with expressive Description Logic (DL) which require some special techniques
for answering conjunctive queries xiii(Sequeda J and Arenams M, 2014).
An OBDA specification J determines the intensional level of the system and is expressed as a triple (O, S, M),
where O is an ontology, S is the schema of the data source and M is the mapping between S and O xiv(Haase P,
Horrocks I et al., 2013). Specifically M consists of a set of mapping assertions, each one relating a query over
the source schema to a query over the ontology. An OBDA system (J, D) is obtained by adding to J an extensional
level, which is given in terms of database D, representing the data at the source, structured according to the
schema S. In OBDA, the main service to be provided by the system is query answering.
An OBDA framework is characterised by three formalisms; (1) the language used to express the ontology, (2)
the language used for queries and (3) the language used to specify the mapping. The axioms of the ontology
allow one to enrich the information coming from the source with domain knowledge and hence to infer additional
answers to queries. OBDA has an architecture known as Ontop which was implemented at Free University of
Bozen-Bolzano and is available as a plugin for the ontology editor Protégé 4 a SPARQL endpoint xv(Calvanese D,
Cogrel B, et al., 2017).
Question 3
Compare the two ontology languages RDFS and OWL.
RDF Schema does not provide actual application-specific classes and properties. Instead RDF Schema provides
the framework to describe application specific classes and properties. Classes in RDF Schema are much like
classes in object-oriented programming language. This allows resources to be defined as instances of classes
and subclasses of classes. RDFS are written in XML language. RDFS form the lowest two layers of the semantic
web. It provides a standard mechanism for declaring classes and properties as well as defining relationships
between classes and properties, using RDF syntax. As a schema layer language, RDFS is responsible to define
basic metamodeling architecture for Web ontology languages xvi(Pan JZ and Horrocks I, 2001).
RDFS, however has a non-standard and non-fixed layer metamodeling architecture, which makes some elements
in the model appear to have multiple roles, multiple modelling primitives seem to be implicitly represented by a
single RDFS primitive. Therefore, it makes the RDFS specification itself kind of confusing and difficulty to
understand for the modellers. One of the consequences is that when DAML+OIL is layering on top of RDFS, it
uses the syntax of RDFS only, but defines its own semantics for the ontological primitives of RDFS xvii(Decker S,
Melnik S et al., 2000). In order to clear up any confusion, a sub-language for RDFS was proposed which is known
as RDFS (FA), and it provides a fixed layer metamodeling Architecture for RDFS. The implicitly represented
modelling primitives in RDFS are explicitly stratified into different layers of RDFS (FA) (Pan JZ and Horrocks I,
2001). In this way RDFS (FA) has clear semantics and there are no dual roles in RDFS (FA). Subsequently RDF
Model Theory (RDF MT) gave an official semantics for RDF and RDFS, justifying the dual roles by treating both
classes and properties as objects in the universe. So, RDF MT is another approach to clear up the kinds of
confusion that can arise in RDFS.
OWL stands for Web Ontology Language and is built on top RDF. OWL is for processing information on the Web
and was designed to be interpreted by computers. OWL and RDF are much of the same thing, but OWL is stronger
language with greater machine interpretability than RDF. OWL comes with a larger vocabulary and stronger
syntax than RDF xviii(Horst H.J, 2005). Owl information can easily be exchanged between different types of
computers using different types of operating system and application languages and it is written in XML. OWL can
declare classes and organise these classes in a subclass hierarchy as can RDF Schema. OWL classes can be
specified as logical combinations of other classes going beyond the capabilities of RDFS. OWL can also declare
properties, organise these properties into a sub property hierarchy and provide domains and ranges for these
properties again as in RDFS. The domains of OWL properties are OWL classes and ranges can be either OWL
classes or externally-defined datatypes such as string or integer. OWL can state that a property is transitive,
symmetric, and functional of the other property, here again extending RDFS xix(Welty C, Fikes R and Makarios S,
2006).
The major extension over RDFS is the ability in OWL to provide restrictions on how properties behave that are
local to a class. OWL can define classes where a particular property is restricted so that all the values for the
property in instances of the class must belong to a certain class. OWL is quite a sophisticated language. OWL
has both RDF/XML exchange syntax and an abstract frame-like syntax and it has three sublanguages: OWL DL,
OWL full and OWL Lite (Welty C, Fikes R and Makarios S, 2006).
OWL Full: The entire language is called OWL Full and uses all the OWL language primitives. It also allows
combining these primitives in arbitrary ways with RDF and RDFS. The advantage of OWL full is that it is fully
upward compatible with RDF, both syntactically and semantically.
OWL DL (Description logic): is a sublanguage of OWL full which restricts the way in which the constructors for
OWL and RDF can be used. The advantage of OWL DL is that it permits efficient reasoning support.

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OWL Lite: An even further restriction limits OWL DL to a subset of the language constructors. For example OWL
Lite excludes enumerated classes and arbitrary cardinality.
One of the aspects of OWL that distinguishes it from RDFS is that it supports a rich set of inferences which require
reasoning by cases and following chains of properties. OWL uses URI references as names and constructs these
URI references in the same manner as that used by RDF. It is thus common in OWL to use qualified names as
shorthand for URI references. OWL gathers information into ontologies, which are generally stored as Web
documents written in RDF/XML. OWL uses the facilities of RDF datatypes and XML schema datatypes to provide
datatypes and data values.
OWL allows:
 the formalization of a domain by defining classes and properties of those classes,
 the definition of individuals and the assertion of properties about them, and
 the reasoning about these classes and individuals.
Although OWL is more expressive than RDFS, it still has limitations; in particular, it lacks a more powerful
language to better describe properties, in order to provide more inference capabilities. An extension to OWL with
Horn-style rules has been proposed by xx(Horrocks & PatelSchneider, 2004), called ORL: OWL Rules Language.
ORL itself may be further extended if more expressive power is needed.
However, RDFS is insufficient in terms of expressivity. On the other hand, ontologies allow a better specification
of constraints on classes. They also make reasoning possible, as new knowledge may be inferred, e.g. by
transitivity. Ontologies aim at formalizing domain knowledge in a generic way and provide a common agreed
understanding of a domain, which may be used and shared by applications and groups.
References
i Giaretta, P. and Guarino, N., 1995. Ontologies and knowledge bases towards a terminological
clarification. Towards very large knowledge bases: knowledge building & knowledge sharing.
ii Gruber, T., 1993. What is an Ontology. WWW Site http://www-ksl. stanford. edu/kst/whatis-an-ontology.
html (accessed on 07-09-2004).
iii Yildiz, B., 2006. Ontology evolution and versioning. Vienna University of Technology, Karlsplatz.
iv Berners-Lee, T., Connolly, D. and Hawke, S., 2003, May. Semantic web tutorial using n3. In Twelfth
International World Wide Web Conference.
v Frauenfelder, M., 2001. A smarter web. TECHNOLOGY REVIEW-MANCHESTER NH-, 104(9), pp.52-59.
vi Berners-Lee, T., Hendler, J. and Lassila, O., 2001. The semantic web. Scientific american, 284(5), pp.34-43.
vii Berners-Lee, T., 1998. Semantic web road map.
viii Yergeau, F., Bray, T., Paoli, J., Sperberg-McQueen, C.M. and Maler, E., 2004. Extensible markup language
(XML) 1.0. W3C Recommendation, 4, p.220.
ix McIlraith, S.A., Son, T.C. and Zeng, H., 2001. Semantic web services. IEEE intelligent systems, 16(2), pp.46-
53.
x Alumbaugh, E., Bohorquez, Y., Bain, M., Reynolds, R., Rasmussen, S. and Lucky, D., COMPASS AL Inc, 2003.
System and method for autonomously generating heterogeneous data source interoperability bridges based on
semantic modeling derived from self-adapting ontology. U.S. Patent Application 10/329,153.
xi Petcu, D., 2011, October. Portability and interoperability between clouds: challenges and case study. In
European Conference on a Service-Based Internet (pp. 62-74). Springer, Berlin, Heidelberg.s
xii Kharlamov, E., Hovland, D., Jiménez-Ruiz, E., Lanti, D., Lie, H., Pinkel, C., Rezk, M., Skjæveland, M.G.,
Thorstensen, E., Xiao, G. and Zheleznyakov, D., 2015, October. Ontology based access to exploration data at
Statoil. In International Semantic Web Conference (pp. 93-112). Springer, Cham.
xiii Sequeda, J.F., Arenas, M. and Miranker, D.P., 2014, October. OBDA: query rewriting or materialization? In
practice, both! In International Semantic Web Conference (pp. 535-551). Springer, Cham.s
xiv Haase, P., Horrocks, I., Hovland, D., Hubauer, T., Jiménez, E., Kharlamov, E., Klüwer, J., Pinkel, C., Rosati,
R., Santarelli, V. and Soylu, A., 2013. Optique system: towards ontology and mapping management in OBDA
solutions. In Workshop on Debugging Ontologies and Ontology Mappings (WoDOOM).
xv Calvanese, D., Cogrel, B., Komla-Ebri, S., Kontchakov, R., Lanti, D., Rezk, M., Rodriguez-Muro, M. and Xiao,
G., 2017. Ontop: Answering SPARQL queries over relational databases. Semantic Web, 8(3), pp.471-487.
xvi Pan, J.Z. and Horrocks, I., 2001, July. Metamodeling Architecture of Web Ontology Languages. In the
Emerging Semantic Web.
xvii Decker, S., Melnik, S., Van Harmelen, F., Fensel, D., Klein, M., Broekstra, J., Erdmann, M. and Horrocks, I.,
2000. The semantic web: The roles of XML and RDF. IEEE Internet computing, 4(5), pp.63-73.
xviii ter Horst, H.J., 2005. Completeness, decidability and complexity of entailment for RDF Schema and a
semantic extension involving the OWL vocabulary. Web Semantics: Science, Services and Agents on the World
Wide Web, 3(2-3), pp.79-115.
xix Welty, C., Fikes, R. and Makarios, S., 2006, May. A reusable ontology for fluents in OWL. In FOIS (Vol. 150,
pp. 226-236).
xx Horrocks, I. and Patel-Schneider, P.F., 2004, May. A proposal for an OWL rules language. In Proceedings of
the 13th international conference on World Wide Web (pp. 723-731). ACM.