4D-SETL
A Semantic Data Integration Framework
Sergio de Cesare
1
, George Foy
2
and Mark Lycett
2
1
College of Business, Arts and Social Science, Brunel University London, Uxbridge, U.K.
2
College of Engineering, Design and Physical Sciences, Brunel University London, Uxbridge, U.K.
Keywords: Foundational Ontology, Perdurantist, 4D, Semantic Data Integration, Modelling, Graph Databases,
Integration Frameworks.
Abstract: Although successfully employed as the foundation for a number of large-scale government and energy
industry projects, foundational ontologies have not been widely adopted within mainstream Enterprise
Systems (ES) data integration practice. However, as the closed-worlds of ES are opened to Internet scale
data sources, there is an emerging need to better understand the semantics of such data and how they can be
integrated. Foundational ontologies can help establish this understanding and therefore, there is a need to
investigate how such ontologies can be applied to underpin practical ES integration solutions. This paper
describes research undertaken to assess the effectiveness of such an approach through the development and
application of the 4D-Semantic Extract Transform Load (4D-SETL) framework. 4D-SETL was employed to
integrate a number of large scale datasets and to persist the resultant ontology within a prototype warehouse
based on a graph database. The advantages of the approach included the ability to combine foundational,
domain and instance level ontological objects within a single coherent system. Furthermore, the approach
provided a clear means of establishing and maintaining the identity of domain objects as their constituent
spatiotemporal parts unfolded over time, enabling process and static data to be combined within a single
model.
1 INTRODUCTION
An enterprise may acquire data from many sources
in many different forms (Zikopoulos and Eaton,
2011). Key considerations in integrating such data
include dealing with the diversity of representation
and the interpretation of the inherent explicit and
implicit semantics. The latter of these considerations
is particularly important in the context of ES
integration as, if left unrecognised, it can lead to the
things of importance (e.g., domain objects and their
relationships), their nuances and the state of affairs
they represent being misinterpreted (Lycett, 2013).
These considerations are well recognised within
database integration projects (Arsanjani, 2002;
Campbell and Shapiro, 1995; Sheth and Larson,
1990).
Ontology has emerged as a promising way of
dealing with such diversity, however many popular
domain ontologies have no grounding in a consistent
foundational view of reality (Cregan, 2007) and
therefore can add further diversity. A foundational
ontology can be employed to provide this ‘grounded’
view of reality and thus provide an explicit theory and
a common reference through which to interpret,
model and thus integrate data. Foundational ontology
“defines a range of top-level domain-independent
ontological categories, which form a general
foundation for more elaborated domain-specific
ontologies” (Guizzardi et al. 2008). From a
philosophical perspective, foundational ontologies
provide the criteria for ontological commitments –
statements on the things believed to exist within the
context of a particular theory (Bricker, 2014). Several
foundational ontologies currently exist (Gangemi et
al., 2002; Grenon and Smith 2004; Partridge 2005;
Guizzardi, et al., 2008; Herre 2010) which differ in
the ontological commitments they make but,
importantly, there is little existing work that examines
their suitability as an ultimate ‘mediating layer’
within a practical data integration context.
Here, we employ a 4D foundational ontology as
a means of dealing with the diversity of
representation and semantics within acquired data.
We do this in the context of a semantic Extract-
Transform-Load framework (called 4D-SETL from
Cesare, S., Foy, G. and Lycett, M.
4D-SETL - A Semantic Data Integration Framework.
In Proceedings of the 18th International Conference on Enterprise Information Systems (ICEIS 2016) - Volume 1, pages 127-134
ISBN: 978-989-758-187-8
Copyright
c
2016 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
127
this point) that uses a 4D foundational ontology to
harmonise data, then generates a graph database that
accords with the semantic commitments made by the
ontology. We examine the effectiveness of the
framework by applying it to semantically interpret
and integrate a number of large-scale datasets and to
instantiate a data warehouse based on a graph
database to persist the resultant ontology. In doing
this, the paper is structured as follows. Section 1
outlines the problem of variety in terms of the
semantic heterogeneity that exists within systems
modelling and foundational ontologies and also
identifies a number of the weaknesses in current
integration approaches. Section 2 describes the core
categories and foundational patterns of the BORO
foundational ontology. Section 3 introduces the 4D-
SETL framework. Section 4 provides details of the
experimental dataset integrated. Section 5 details the
outcomes and limitations.
1.1 Semantic Data Integration
Data integration is problematic on several counts.
Firstly, people perceive and conceptualise reality in
different ways. Even when a set of models is
developed by the same individual, they can make
different (and sometimes arbitrary) choices about the
same reality at different times and in different
contexts (Kent, 1978). Secondly, in the course of
modelling reality, a designer may confuse what is
being represented with the representation itself
(Partridge et al., 2013). Thirdly, different structures
and restrictions are introduced by heterogeneous
modelling methods and languages (e.g., Entity-
Relationship, OWL etc.). Fourthly, it is common
practice to develop a number of models in systems
development – conceptual, logical and physical data
models for example (Codd, 1970). This layering can
have an adverse effect as the original semantic
structures may be distorted or lost completely as the
emphasis of the modelling activity moves from
representing the real world to representing data
structures. Consequently, when integrating data that
originates from different sources, the problem of
semantic heterogeneity arises – resolution is
required regarding differences in meaning,
interpretation or the intended use of related data
which forms a barrier to coherent semantic data
integration (Doan, Noy and Halevy, 2004).
1.2 Heterogeneous Foundational
Ontologies
Ontology provides a way of dealing with semantic
data integration. From a computational standpoint,
an ontology is generally taken as a ‘specification of
a conceptualization’ (Gruber, 1995) – that is, a
description of the concepts and relationships that are
considered legitimate within a particular system of
thought. In terms of the concrete implementation of
software systems, foundational ontologies can be
used to establish the fundamental ‘meta’ objects and
relations used to construct more specific domain
ontologies. If a common foundational theory is
extended and specialised to model a number of
domain ontologies, then objects common to each of
these domains will have the necessary (common)
grounding to enable semantic integration.
Consequently, foundational ontologies are important
as they provide a standpoint that underpins all the
domain models to be integrated – providing a
semantic grounding.
It is the case, however, that several such
standpoints (related to foundational ontologies)
exist, Each provides a criterion for the ontological
commitments made (implicitly or explicitly), which
are principally the things believed to exist within the
context of a particular theory such as four-
dimensionalism (Quine, 1952; Sider, 2003). An
understanding of ontological commitment, however,
means that the computational view of ontology
needs to defer to a philosophical one, which is more
specifically concerned with the nature of being
(metaphysics). As metaphysical theories differ on a
number of dimensions (realism versus idealism,
endurantism versus perdurantism to name but two)
differences thus appear in foundational ontologies.
Furthermore, and perhaps more importantly, the
degree to which foundational ontologies are actually
grounded in metaphysics varies. Clearly, a lack of
consensus at the metaphysical level introduces
obstacles to semantic integration (Campbell and
Shapiro, 1995) that result in weaknesses in
computational applications:
a) Lack of Grounding. Many current models
employed within information systems have no
form of grounding in a more fundamental theory
(Cregan, 2007). Thus the ontological
commitments underlying the model are
unknown. On examination of many Linked Open
Data ontologies, they are often ungrounded.
b) Integrating Elements from Models which are
Founded on Different Theories. There are
many automatic translation techniques for
translating RDBMS schema and data to an
OWL ‘ontology’. However, there is a lack of
recognition that the expressivity of Description
Logics (that underlie OWL) and RDBMS are
ICEIS 2016 - 18th International Conference on Enterprise Information Systems
128
different as are the unique naming and the
open/closed world-view assumptions.
c) Model Strata and Translations. As noted earlier,
the requirement to translate the high-level models
of reality created at the initial design to structures
that are focused on the execution environment can
result in semantic distortion. There is also the
problem of translating between run-time
representations; the often cited OO-RDBMS
impedance mismatch (Ireland et al., 2009).
d) Over Simplification to Fit a Model of Reality
to a Tractable First Order Logic (FOL)
Theory. The simplification of the abstraction of
reality to fit neatly into a FOL theory, thus
ignoring the fact that reality is not so simple and
higher order objects exist (Bailey, 2011).
e) Dividing Models into Static and Dynamic
Types. The separation of static and dynamic
aspects of reality into different structural and
process models leads to the development of
incompatible abstractions together with ‘exotic’
relations that are employed to bridge these static
and dynamic worlds.
f) Naming and Meaning Confusion. There is
often confusion between an entity’s naming and
meaning (Bailey and Partridge, 2009). An
object’s place in reality (and within ontology)
should define its meaning.
g) Establishing Identify. Many modelling and
information systems use ephemeral means of
establishing an entity’s identity which do not
function well over time.
h) Employing Techniques that do Not Scale.
Software tools such as OWL tableau calculus-
based reasoners are constrained by memory and
cannot be easily scaled to inference over
ontologies containing large instance populations
(Bock et al., 2008). The alternative is to use
simplified semantics and rule based reasoning -
that could in many cases employ standard
RDBMS techniques.
i) Semantic Integration Mismatches. For a more
extensive discussion on the types of semantic
integration mismatches see Visser et al., (1997)
who provide an extensive list of e semantic
mismatches that can occur when integrating
disparate datasets.
2 BORO FOUNDATIONAL
ONTOLOGY
Having examined several foundational ontologies
from a philosophical perspective, the research
described here adopts the Business Object Reference
Ontology (BORO) (Partridge, 2005) to semantically
interpret the original datasets and models. We adopt
BORO on the grounds that the ontology can
overcome the dichotomy that exists between
dynamic and static modelling paradigms and its
metaphysical thoroughness. Hence, the same model
can represent processes and things that are not
traditionally considered as processes (e.g., people,
products, machines, etc.). BORO represents all
individual elements (e.g. the activity, the person
assuming a role and the resource consumed) in
exactly the same way (i.e. as spatiotemporal
extents). BORO is based on a philosophical (rather
than computational) definition of ontology because
it requires more clarity on “the set of things whose
existence is acknowledged by a particular theory or
system of thought” (Lowe, 1998, p.634.). Key to
overcoming the dichotomy noted is the fact that
BORO is perdurantist (and thus extensionalist) in its
nature. In perdurantism (or 4D) an individual object
is never wholly present at one point is time, but only
partly present (a temporal part). For example, John
is not fully present in any given phase of his life
(e.g., childhood), he is fully present from his birth to
his death only – therefore, John’s childhood is a
temporal part of John. Identity is thus defined by an
individual object’s spatiotemporal extension (or
extent). Figure 1 represents the key part of the
foundational ontology relevant for the purposes of
this paper.
Figure 1: BORO Foundational Ontology (top level).
More in-depth discussions are provided in
Partridge, 2002; 2005; Bailey and Partridge 2009;
Bailey, 2011; Partridge et al., 2013). At the top level
4D-SETL - A Semantic Data Integration Framework
129
the BORO foundational ontology represents:
Elements, which are individual objects or objects
with a spatiotemporal extent. For example, the
person John.
Types, which are sets or objects that can have
instances. The identity of a type is also extensional
but, in this case, it is defined as the set of its
instances (i.e. members). For example, the extension
of the type Persons is the set of all people.
Tuples, which are relationships between objects.
The identity of a tuple is defined by the places in the
tuple. An example is (Persons, John) in which the
type Persons and the element John occupy places 1
and 2 in the tuple respectively. This specific tuple is
an instance of the tuple type typeInstances in BORO.
In turn, Elements is subtyped by:
Events: An event is an element that does not
persist through time (i.e. an event has zero
‘thickness’ along the time dimension). Events
represent temporal boundaries that either create
(CreationEvents) or dissolve (DissolutionEvents)
elements (e.g., a person or a person’s childhood
state).
States: A state is an element that persists through
time. States (and elements in general) are bounded
by events. A state (like all elements) can have
further temporal parts (i.e. states and events).
Specific TupleTypes (or types whose instance are
tuples) relevant here are:
temporalPartOf: This tuple type relates an
individual with its temporal parts (states and/or
events).
happensTo: This tuple type relates an event with
one or more elements affected by the event.
happensTo has two subtypes:
- creates: Relates a creation event with the
element(s) whose creation is triggered by the event.
- dissolves: Relates a dissolution event with the
element(s) whose dissolution is triggered by the
event.
happensIn: This tuple type relates an event with a
time instant or interval (TimeInstantsOrIntervals)
and it indicates the time in which an event takes
place.
As a note of importance for the example shown
later, names are types in BORO. The instances of the
name of an individual (e.g. John Smith’s Name) are
all utterances (written, spoken, etc.) that name that
individual (e.g., John Smith). Therefore while a
name, is a type its instances are spatiotemporal
extents. To provide clarity within the ontology,
‘names’ as much elements of the ontology as the
things they name. A name object will belong to a
Name Space which holds all names related to a
particular naming authority or domain. As the
ontology adopts a theory of utterances – each
utterance of a name is an individual element and so
has an extent (Strawson, 1964). Therefore, a name is
a Type that has as instances all utterances of the
same name individuals.
3 4D SEMANTIC EXTRACT
TRANSFORM AND LOAD
FRAMEWORK (4D-SETL)
Given an outline understanding of the foundational
ontology, we now describe a Semantic Extract-
Transform-Load framework. Given a variety of data
input, 4D-SETL is designed to output a graph
database in accordance with the BORO foundational
ontology. The framework was designed around a
number of industry standard tools and technologies
(e.g., a UML design tool and a Graph Database),
supplemented where necessary with custom software
implemented in Java. The key technology choices
made for the initial implementation were threefold.
First spreadsheets were employed to document each
dataset. Second, a UML design tool (Enterprise
Architect) was selected as the graphical design tool
for the ontological models and a BORO custom
UML profile created: The advantage is that BORO
UML enables easy manipulation and design of each
of the required domain ontologies. Last, the Neo4J
Graph database was chosen for persistence, for
several reasons: (a) Primarily due to its flexibility in
enabling BORO to be used as the foundational
‘schema’ (both can be seen as graphs); (b)
scalability in order to handle model and instance
data volume appropriately; (c) Neo4J's web-based
interface also provides access to the graph database
for development testing; and (d) Neo4J Cypher
provides an appropriate means of querying and
updating the graph database resident data.
The Semantic Extract Transform Load (ETL)
process is shown in Figure 2, the key stages of the
process are as follows:
a) Semantic Extraction and Transformation.
The input data to a semantic integration process
may be structured in many forms –e.g., as fixed
record or delimited tabular files, RDF, RDFS,
OWL etc. – and may consist of both model
(schema) level and/or instance level data. Thus
the first stage in the semantic integration
ICEIS 2016 - 18th International Conference on Enterprise Information Systems
130
process begins with documenting the dataset
which can be considered a semantic extraction
and transformation process. The BORO
foundation provides a view of reality and the
patterns that can be employed perform this
interpretation and transformation. The
foundational ontology provides the equivalent
of a canonical data model (Saltor et al., 1991)
that can be employed to develop domain models
providing the semantics that are common to all
datasets that will be integrated. Thus the
translation process results in a new schema
(domain ontology) that extends the ontic
categories and patterns of the foundation.
Through this process, schemas are developed to
represent the entities and relationships that are
represented by the data. Finally this schema is
documented using a profile of UML that
conforms to BORO semantics.
b) Ontology Model ETL. Once a domain model
has been created in an ontologically consistent
form the semantic load and integration process
can be undertaken. Firstly the domain
ontological model, which includes such patterns
as type and classification taxonomies, is
translated from the BORO UML model and
loaded to the graph database. The 4D-SETL
framework provides a Java application to
translate the BORO UML and load it to the
graph database.
c) Ontology Data ETL. Next, the instance level
dataset is loaded and integrated. It is through
this process that the integration of individual
elements takes place. Integration can be
considered to take place within vertical and
horizontal planes. Initially the ‘vertical’
relationships between an individual element and
the domain ontology (and hence the foundation
ontology) is asserted, which consists of
establishing the individual relationships (such as
type instance, etc.). Then the ‘horizontal’
relationships that are deemed to hold between
individual domain level objects are established
(such as a company being located at a particular
geographic location). Foundational ontological
patterns can then be applied to simplify this
process. This can be a complex process that
requires both one-to-many and many-to-one
transformations. The 4D-SETL framework
provides a Java application to perform this
process.
Figure 2: 4D-SETL Framework.
4 EXPERIMENTAL DATA
As the foundational ontology is an integral part of
the framework, prior to processing any of the
domain ontological elements (model and datasets)
the foundational ontology is transformed to graph
format and loaded to the database. This is achieved
via the 4D-SETL framework which extracts the
BORO UML as XML (XMI), then transforms it to a
set of nodes and edges that are loaded to the
database. The graph database ontology also includes
the UML model identifiers as indexed node and
edge parameter key-values, these are employed to
enable the reproduction of the design time UML
models within the graph database runtime
environment and to establish the relationships
between the foundation and other subsequent
domain model elements that are loaded. The 4D-
SETL framework was applied to Extract, Transform
and Load (ETL) five datasets of varying scale and
complexity related to corporate data:
Calendar: temporal locations (1856 to present).
Location: spatial locations (~2.5M locations).
Standard Industrial Classification (taxonomy).
UK Companies (~3.5M)
UK corporate officers (~12M)
4D-SETL - A Semantic Data Integration Framework
131
5 OUTCOMES AND
LIMITATIONS
Having applied the framework, our experience is
that BORO provides a coherent lens through which
to view and model the world together with the
foundational ontological elements and patterns
through which the domain ontologies can be
developed to represent the datasets to be
semantically integrated (Partridge, 2002). In terms
of domain ontology development, this work concurs
with the work of Keet (2011), who stated that
employing foundational ontologies provides
advantages in terms of the quality and
interoperability of domain ontologies. Developing
such domain ontologies provided the means of
semantically integrating data conforming to different
models and theories – a necessary evil in dealing
with variety in big data.
Employing a graph database provided the means
of importing and restructuring data in a manner that
directly reflects the ontological model patterns
without the normal translation to tabular RDBMS or
Object Oriented form and not introducing the
‘impedance mismatch’ problem (Ireland et al.,
2009). Dispensing with RDBMS storage in favour of
a property graph data model removed the
partitioning of the storage structures between data
and schema and allows both ‘schema’ ontological
model objects and instance level objects to be
updated at run time. This supports the work related
to graph databases by Webber (2012). Related to this
finding, it was also demonstrated in this study that
patterns could be established within the warehouse
that directly reflected the physical or socially
constructed patterns of reality such as taxonomies
and taxonomic ranks, the latter of which employed
the powertype pattern (equivalent to the set theoretic
powerset) to more accurately reflect the nature of
such classification systems. These aspects of 4D
ontologies (along with others) provide a greater level
of flexibility and reusability when evolving the
warehouse system and therefore concur and take
forward the initial findings of Partridge (2002).
In practical terms, we propose that the data
structures resulting from the 4D-SETL process are
more suitable for discovering relationships within
data rather than for example processing aggregate
data (Vicknair et al., 2010). It is relatively easy, for
example, to discover all relationships that exist
between two elements using a standard algorithm
from the Neo4J library (designed to find all
available paths or the shortest path between two
nodes). Further, the Cypher graph database query
facilities provide the means of discovering more
complex patterns of relationships between the
people, company officers, company activities, events
and physical location. Finally, it was found through
the evaluation and empirical experiment on the
prototype warehouse (graph database) that data load
and information retrieval response times that the
prototype could be developed into a practical
information system. This was confirmed by
performing test data query (graph traversal)
experiments that for example, performed graph
traversals to retrieve all companies within a postcode
location (61 milliseconds) and all officers for a
specific business organisation (37 milliseconds) thus
the prototype produced indicative response times
within bounds that would support interactive
applications (Bhatti et al., 2000). Testing also
confirmed the graph database performance
evaluation undertaken by Vicknair et al. (2010).
Thus using a graph database and the parameter
graph model to store the ontology, alongside query
information via graph traversal, circumvents the
issues that limit the ability of systems built using
triple stores and tableau calculus-based reasoner
technology to deal with ontologies that are both
expressive and have with very large instance level
elements (arguably exactly what one would want
from big data). Neo4J is highly scalable and
provides capacities for Nodes/Edges of ~34 billion
and properties at least 68 billion respectively.
With the issue of disparate data sources in mind,
the work here has: (a) Examined the potential
contribution of foundational ontology; and (b)
described an implementation of a Semantic Extract-
Transform-Load framework (4D-SETL) based on
BORO, a 4D foundational ontology. Foundational
ontologies provide a ‘grounding’ for our view of
reality and thus provide a common reference through
which to model and integrate heterogeneous data.
The 4D-SETL framework uses the BORO
foundational ontology to harmonise data and then
generates a graph database that accords with the
semantic commitments made by that ontology. The
effectiveness of the framework was examined
applying it to large-scale open datasets related to
company information to semantically interpret and
integrate the datasets and to instantiate a prototype
graph database warehouse to persist the resultant
ontology. Our implementation is a prototype at this
stage and the use of foundational ontologies is not
without challenge (e.g., automation in the context of
real-time data streams). Accepting such limitations,
however, the potential utility of the 4D-SETL
framework can be seen in its ability to model and
ICEIS 2016 - 18th International Conference on Enterprise Information Systems
132
instantiate a number of complex ontological
structures, such as higher order taxonomic ranks.
The patterns specialised from the core foundational
BORO ontology patterns offer a high degree of
flexibility and reusability when evolving the graph-
based warehouse system. We have thus
demonstrated how a 4D (perdurantist) foundational
ontology can be employed to semantically interpret
and structure data, showing that a single coherent
ontology can be developed and loaded to a graph
database without the problems associated with
current approaches – e.g., model distortion, over
simplification or scalability problems.
Understandably, the work here is not without its
limitations, which may be summarised as follows.
First, and at the outset, the interpretation process is
manual. BORO encourages the development of
patterns (for ontological reuse), which allow for
partial automisation but skills in ontological
modelling are necessary throughout. In the context
of dealing with variety in big data automatic
translation of data is of particular importance. As a
consequence, pattern development and the extraction
of the rules associated with that are also of
importance for ongoing research. Second, as
previously noted, BORO is one of several
foundational ontologies and further work is required
to understand their relative comparative advantages
and disadvantages.
The work here was supported by funding from
the Engineering and Physical Sciences Research
Council (Project EP/L021250/1). The experimental
research data and metadata (Ontology) for this
project was sourced from the following
organisations: Companies House (2016), Company
Information; UK Office of National Statistics (2016)
Geographic Location (ONSPD Product); UK Office
of National Statistics (2016), Standard Industrial
Classification; Company Officers: (A commercial
credit reference agency); BORO Engineering
Limited (2016), Foundational Ontology.
The Companies House and ONS Datasets are
UK Open Government Data and can be freely
downloaded. The Company Officers and BORO
Ontology are commercial in-confidence.
REFERENCES
Arsanjani, A. (2002) 'Developing and Integrating
enterprise Components and Services', Communications
of the ACM, 45(10), pp. 30-34.
Bailey, I. (2011) 'Enterprise Ontologies–Better Models of
Business', in Intelligence-based systems engineering.
Springer, pp. 327-342.
Bailey, I. and Partridge, C. (2009) 'Working with
extensional ontology for defence applications',
Ontology in Intelligence Conference.
Bhatti, N., Bouch, A. and Kuchinsky, A. (2000)
'Integrating user-perceived quality into web server
design', Computer Networks, 33(1), pp. 1-16.
Bock, J., Haase, P., Ji, Q. and Volz, R. (2008)
'Benchmarking OWL reasoners', Proc. of the
ARea2008 Workshop, Tenerife, Spain (June 2008).
BORO Engineering Limited (2016) ‘BORO Ontology’.
Available from: < http://www.borosolutions.co.uk/so
lutions/resources/boro-presentations-and papers >. [16
February 2016].
Bricker, P. (2014) 'Ontological Commitment', in Edward
N. Zalta (ed.) The Stanford Encyclopedia of
Philosophy. Winter 2014 edn.
Campbell, A. and Shapiro, S. (1995) 'Ontological
Mediation: An Overview', IJCAI Workshop on Basic
Ontological Issues in Knowledge Sharing. 1995.
AAAI Press, Menlo Park, CA,.
Codd, E. (1970) 'A relational model of data for large
shared data banks', Communications of the ACM,
13(6), pp. 377-387.
Companies House (2016), Free Company Data Product.
Available from: < http://download.companieshouse.g
ov.uk/en_output.html >. [16 February 2016].
Cregan, A. (2007) 'Symbol grounding for the semantic
web', in The Semantic Web: Research and
Applications. Springer, pp. 429-442.
Doan, A., Noy, N.F. and Halevy, A.Y. (2004)
'Introduction to the special issue on semantic
integration', ACM Sigmod Record, 33(4), pp. 11-13.
Gangemi, A., Guarino, N., Masolo, C., Oltramari, A. and
Schneider, L. (2002) 'Sweetening ontologies with
DOLCE', in Knowledge engineering and knowledge
management: Ontologies and the semantic Web.
Springer, pp. 166-181.
Grenon, P. and Smith, B. (2004) 'SNAP and SPAN:
Towards dynamic spatial ontology', Spatial cognition
and computation, 4(1), pp. 69-104.
Gruber, T.R. (1995) 'Toward principles for the design of
ontologies used for knowledge sharing?', International
journal of human-computer studies, 43(5), pp. 907-
928.
Guizzardi, G., de Almeida Falbo, R. and Guizzardi, R.
(2008) 'Grounding Software Domain Ontologies in the
Unified Foundational Ontology (UFO): The case of
the ODE Software Process Ontology.', CIbSE. , 127-
140.
Herre, H. (2010) 'General Formal Ontology (GFO): A
foundational ontology for conceptual modelling', in
Theory and applications of ontology: computer
applications. Springer, pp. 297-345.
Ireland, C., Bowers, D., Newton, M. and Waugh, K.
(2009) 'A classification of object-relational impedance
mismatch', Advances in Databases, Knowledge, and
4D-SETL - A Semantic Data Integration Framework
133
Data Applications, 2009. DBKDA'09. First
International Conference on. IEEE, 36-43.
Keet, M. (2011) 'The use of foundational ontologies in
ontology development: an empirical assessment', in
The Semantic Web: Research and Applications.
Springer, pp. 321-335.
Kent, W. (1978) Data and reality : basic assumptions in
data processing reconsidered. Amsterdam ; Oxford:
North-Holland Publishing Co.
Lowe, E.J. (1998) 'Ontology.', in Hondreich, T. (ed.) The
Oxford Companion to Philosophy. New York: Oxford
University Press, pp. 634.
Lycett, M. (2013) ''Datafication': Making sense of (big)
data in a complex world', .
Partridge, C. (2002) 'The role of ontology in integrating
semantically heterogeneous databases', Rapport
technique, 5(02).
Partridge, C., Mitchell, A. and de Cesare, S. (2013)
'Guidelines for developing Ontological Architecures in
Modelling and Simulation', in Tolk, A. (ed.) Ontology,
Epistemology, and Teleology for Modeling and
Simulation. Berlin Heidelberg: Springer, pp. 27-57.
Office of National Statistics (2016), Postcode Data
Product. Available from: < http://www.ons.gov.uk/ons
/guide-method/geography/products/postcode-directorie
s/-nspp-/index.html>. [16 February 2016].
Office of National Statistics (2016), Standard Industrial
Classification System 2007. Available from: <
http://www.ons.gov.uk/ons/guide-method/classificatio
ns/current-standard-classifications/standard-industrial-
classification/index.html>. [16 February 2016].
Partridge, C. (2005) Business objects. 2nd edn. Oxford:
Butterworth Heinemann.
Quine, W.V. (1952) Methods of logic. Routledge and
Kegan Paul.
Saltor, F., Castellanos, M. and Garcia-Solaco, M. (1991)
'Suitability of Data models As Canonical Models for
Federated Databases', SIGMOD Rec., 20(4), pp. 44-48.
Sheth, A.P. and Larson, J.A. (1990) 'Federated Database
Systems for Managing Distributed, Heterogeneous,
and Autonomous Databases', ACM Comput.Surv.,
22(3), pp. 183-236.
Sider, T. (2003) Four-dimensionalism: An ontology of
persistence and time. Oxford.
Strawson, P. F. "Identifying reference and truthvalues",
Theoria, 30(2), 1964 pp. 96-118.
Vicknair, C., Macias, M., Zhao, Z., Nan, X., Chen, Y. and
Wilkins, D. (2010) 'A comparison of a graph database
and a relational database: a data provenance
perspective', Proceedings of the 48th annual Southeast
regional conference. ACM, 42.
Visser, P.R., Jones, D.M., Bench-Capon, T. and Shave, M.
(1997) 'An analysis of ontology mismatches;
heterogeneity versus interoperability', AAAI 1997
Spring Symposium on Ontological Engineering,
Stanford CA., USA. , 164-172.
Webber, J. (2012) 'A programmatic introduction to Neo4j',
Proceedings of the 3rd annual conference on Systems,
programming, and applications: software for
humanity.
ACM, 217-218.
Zikopoulos, P. and Eaton, C. (2011) Understanding big
data: Analytics for enterprise class hadoop and
streaming data. McGraw-Hill Osborne Media.
ICEIS 2016 - 18th International Conference on Enterprise Information Systems
134