=Paper=
{{Paper
|id=Vol-1286/paper4
|storemode=property
|title=An implementation of multi-level modelling in F-logic
|pdfUrl=https://ceur-ws.org/Vol-1286/p4.pdf
|volume=Vol-1286
|dblpUrl=https://dblp.org/rec/conf/models/IgamberdievGS14
}}
==An implementation of multi-level modelling in F-logic==
https://ceur-ws.org/Vol-1286/p4.pdf
An Implementation of Multi-Level
Modelling in F-Logic
Muzaffar Igamberdiev, Georg Grossmann, and Markus Stumptner
Advanced Computing Research Centre
School of IT and Mathematical Sciences
University of South Australia, Mawson Lakes, SA 5095, Australia
{firstname.lastname}@unisa.edu.au
Abstract. Multi-level modelling is currently regaining attention in the
database and software engineering community with different emerging
proposals and implementations. One driver behind this trend is to reduce
model complexity, a crucial aspect in a time of big data research in which
more and more data from different sources are required to be integrated.
From our experience, multi-level modelling also improves understand-
ing of complex specifications, simplify their management and evolution,
and facilitate interoperability between them. This paper focuses on the
requirement of reasoning for interoperability. Although there exist for-
malisation approaches for multi-level modelling, only few have the im-
plementation for three fundamental aspects: formalisation, querying and
validation of multi-level models. We propose an F-Logic framework to
implement these aspects. In addition, we believe this approach is more
likely to be adapted in real-world use cases because of its simple object-
oriented declarative nature.
Keywords: Multi-level modelling, interoperability, multi-level model rea-
soning, F-Logic, multi-level model querying, multi-level model validation
1 Introduction
Multi-level modelling (also called deep meta-modelling/instantiation) is cur-
rently regaining attention in the database and software engineering commu-
nity with different emerging proposals and implementations. Recently there have
been multiple works published which enrich multi-level modelling with new fea-
tures [2,19], propose a formalisation for multi-level modelling [21] or demonstrate
the practical application of it [8,13]. The most often mentioned arguments for
multi-level modelling are increased expressiveness, by introducing multiple clas-
sifications [5], and reduced complexity [6,20]. This seems to be a contradiction,
because one might expect increasing expressiveness may lead to increased com-
plexity, but multiple classification allows to brake down a complex specification
into smaller and simpler layers. Apart from the above mentioned advantages we
have seen a further three advantages in a use case from the oil and gas indus-
try [13]: (1) Simplification of the standards’ specifications by classifying elements
33
into ontological and linguistic elements, (2) simpler management and evolution
of standards by structuring them into multiple ontological instance-of levels,
and most importantly (3) checking specifications for consistency according to
software engineering modelling principles.
The third advantage facilitates the interoperability between software systems.
During the development life cycle of an interoperability solution, the matching,
transformation, and synchronization of models and data rely on querying source
and target specifications and checking for consistency to ensure a correct end
result (i.e. validation). It only becomes possible with a formal specification that
can be executed.
For multi-level modelling there exist some formalisation approaches. Neu-
mayr et al. [19] proposed ConceptBase as the underlying formalisation frame-
work. ConceptBase is a metamodeling system based on Datalog and the Telos
data model [18]. Rossini et al. [21] implemented the semantics using the Diagram
Predicate Framework (DPF) and Golra et al. [11] used a graph algebra.
We propose a novel approach, namely Multi-level Modelling in F-Logic (MiF),
for the implementation, querying and validation of the multi-level models. We
propose to use F-Logic as an alternative implementation for the following rea-
sons: in comparison to DPF and graph-based approaches, F-Logic is object-
oriented and represents an integrated framework which allows the specification
of the semantics and ontological models as well as perform reasoning. Concept-
Base also aims at an integrated approach but is not as widely accepted as F-
Logic. In particular in the Ontology and Semantic Web community, e.g., there
exist commercial and open-source implementations such as OntoBroker 1 and
Flora 2 , and it has been used for interoperability, such as the Rule Interchange
Format [14]3 and model transformation [15].
In the next section we describe our motivating use case from the oil and gas in-
dustry, followed by a description of the implementation of multi-level modelling
and related work.
2 Oil & Gas Interoperability Pilot
A large-scale standard-based interoperability is one of the main challenges in
the oil and gas industry. Some comparable figures of how much an inadequate
interoperability costs came from the US Capital Facilities Industry and the con-
struction and engineering domain with an estimate of about $15.8 billion per
year [9,10].
A lot of effort has been invested into data standards to overcome the inter-
operability issue in the oil and gas industry. One effort is the joint academic-
industry project Oil & Gas Interoperability (OGI) Pilot hosted by MIMOSA4
and supported by the ISO TC 184 OGI Technical Specification project. The
1
OntoBroker: http://www.semafora-systems.com/
2
Flora-2: http://flora.sourceforge.net/
3
RIF: http://www.w3.org/2001/sw/wiki/RIF
4
MIMOSA: http://www.mimosa.org
34
goal of the OGI Pilot is increased automation in the digital hand-over of de-
sign information of very large physical assets to the operation and maintenance
side. This requires identifying commonalities and differences as well as open
gaps in the specifications of the major standards in the area: ISO 15926 [12]
and MIMOSA’s Open Systems Architecture for Enterprise Application Inte-
gration (OSA-EAI) [17]. Within the OGI Pilot we have identified some chal-
lenges [16] of which we will focus on ISO 15926 standard in this paper. The ISO
standard relies on a 4-dimensional information model specified in STEP/EXPRESS,
RDF, OWL, and first order logic.
Example: Our motivating example is taken from an engineering diagram which
specifies that “The impeller with serial number XXX is part of the Weir Pump
with serial number XYZ ”. Figure 1a displays the flat model of this example
using the instance diagram notation appearing throughout the ISO 15926 doc-
umentation [12].
(a) Instance diagram (b) Multi-level representation
Fig. 1: Two representations of the same example: relating an impeller to a pump.
In ISO 15926 terminology each box represents a class which is part of the
specification and identified by its label. A diamond represents a relationship
where a diamond with a thick line represents a class of relationship. A (class of)
relationship has roles which are displayed by labelled arcs connected to the dia-
mond, e.g., “part” and “whole” are the two roles of “composition of individual”.
A symbol with prefix # is a possible individual with a temporal part, e.g., ”#Im-
peller S/N: XXX” is a possible temporal part with identifier “Impeller S/N:
XXX”. Remaining elements are classes identified by its label, e.g., “Pump Com-
ponent Class”.
Jordan et al. [13] applied rules on the ISO 15926 specification for the transfor-
mation of the flat model into a multi-level model. For example, one of those rules
assigns a model level according to the prefix of a class label, e.g., “class of composition”
is an instance of “class of class of composition” and removes the prefix “class of ”
35
to simplify the notation. Figure 1b shows the result of applying those rules on
the example introduced above.
Some of the advantages of a multi-level model representation over the flat
model are: (1) explicit instance-of relationships, (2) separation of concerns through
multiple levels making it easier for users to focus on particular aspects of the
model, (3) reduced complexity, and (4) clarified terminology which improves
understandability.
In order to verify the multi-level models and perform queries, for example,
to support matching with other standards for model transformation, we require
a formal framework. We propose to use F-Logic because of its object-oriented
semantics and its wide acceptance in the ontology, business rules and model
transformation communities[1].
3 Implementation of Multi-level Modelling in F-Logic
This section introduces the semantics of multi-level modelling, its implementa-
tion, querying and validation in F-Logic.
3.1 Multi-level Modelling Semantics
The semantics of multi-level modelling involves the characterization of concepts
and the definition of relationships between them. Similarly to semantics defini-
tion in two-level modelling, which is designated by its meta-model, the multi-
level modelling semantics have been described by its meta-model that includes
a linguistic meta-model and an ontological stack [3,21].
The fundamental concepts of multi-level modelling are characterized by lin-
guistic and ontological perspectives. The linguistic meta-model deals with syntax
and grammar, whereas the ontological stack addresses structural hierarchies and
classification of an underlying domain. These perspectives make understanding
of multi-level modelling easier and clearer. We also define the semantics in the
light of the two perspectives.
The link between multi-level modelling perspectives is established by a lin-
guistic instance-of relationship. It differs from the relationships within the lin-
guistic meta-model and ontological stack in the sense that it connects the con-
cepts across the perspectives. Every model element and relationship in the on-
tological stack is a linguistic instance-of the concepts from the linguistic meta-
model. An ontological model element may or may not have an ontological type,
but the linguistic type (e.g. clabject) is mandatory.
Meta-model for use case: The multi-level modelling meta-model for our use
case is illustrated in Figure 2. It is organized in linguistic meta-model and onto-
logical stack.
Some of the concepts are inspired by the work on multi-level modelling and
the formalisation of deep meta-modelling [4,21]: The root element in the lin-
guistic meta-model is called instantiable element, meaning all model elements
are instantiable in the ontological stack. Depending on the representation of the
36
Fig. 2: Multi-level modelling meta-model with the motivational example
instantiated model element across ontological levels, instantiable elements are
categorized as multi- and single-potency elements in Figure 2. While general-
ization and classification relationships cannot be instantiated across ontological
levels, association and composition relationships can. The former ones are the
examples for single-potency and latter ones for multi-potency elements. While
the single potency elements can be instantiated in any ontological level and
will not have further instances, the multi-potency elements can have further
instances depending on the value of their potency[21]. The composition relation-
ship is illustrated as a multi-potency relationship between impeller and pump
elements in the example. The same multi-potency semantics is valid for clabject
and association relationship.
Generalization relationship and attribute model elements are sub-class of
a single-potency element. The difference between attribute and feature is that
while the former one is considered as a single-potency, the latter one has multi-
potency characteristics. Attribute is a property of the model element that can
be instantiated in any ontological level and will not be instantiated in the next
levels (single-potency). Alternatively, feature (field and method) is multi-potency
element and can be instantiated across ontological levels. Clabject is sub-classed
into domain entity and domain connection. Domain entities are the clabjects
that characterize the domain concepts and domain connections address domain
specific relationship, e.g., association and composition are domain connections.
37
The generalization is demonstrated with a relationship between Weir MC
Series Centrifugal Pump and more general concept Centrifugal Pump in the
ontological stack. The association is illustrated with a relationship between Ro-
tating Mechanical Equipment and Designation model elements, that represents
that rotating mechanical equipment has a designation.
3.2 Implementation in Flora-2
In this section, we first introduce F-Logic briefly, then discuss built-in features of
F-Logic which directly support part of the meta-model and finally describe how
we add new semantics to fully support the meta-model. Due to lack of space, we
provide the excerpts of the semantics implementation.
F-Logic stands for Frame Logic, frame-based, object-oriented knowledge rep-
resentation and reasoning language. It has a declarative, compact, simple and
expressive syntax with well-defined semantics. These characteristics makes it
attractive to apply on integration of information, semantic search, intelligent
agents, semantic web and other areas.
In this paper, we use one of F-Logic implementations: Flora-2 [22]. Flora-2
is a dialect of F-Logic with numerous extensions and it supports extensibility,
flexibility and modularity through dynamic modules. It is more suitable for a
knowledge representation and reasoning in a way that multi-model semantics can
be compactly expressed and the constrains can be checked, validation rules can
be applied and more importantly the multi-model concepts and relationships can
be easily queried. The source code presented in this paper is based on Flora-2
syntax. We now continue with the overlapping and distinct features of F-Logic
and multi-level modelling.
Direct support: Some of the multi-level modelling concepts can be mapped di-
rectly to F-Logic. Concepts which are supported directly are represented with in
grey in Figure 2. The mapping between modelling concepts and F-Logic elements
are illustrated in Table 1.
MLM Concept F-Logic Concept Flora-2 Example
Generalization Subclass A::B. subclass::class.
Classification ”IS A” relationship A:B. object:class.
Attribute If M in O[M->V] is a con- Pump[component->’impeller’].
stant it is dealt as an at-
tribute
Table 1: Multi-level modelling concepts and equivalent representation in F-Logic
and its implementation in Flora-2.
Multi-level modelling aware extensions: The linguistic meta-model ele-
ments with white background in Figure 2 represent model elements and relation-
ships that have not been addressed by F-Logic yet. Due to space limitation we
38
provide its the implementation only for the linguistic and ontological instance-of
relationship:
Linguistic instance-of relationship: It is similar to the IS-A(instance-of)
relationship implemented by the colon (:) operator in F-Logic. We introduced a
new operator, (<:), for the linguistic instance-of relationship with the following
short excerpt of validation:
1. :- _op(400,xfx,<:).
2. linguistic_instance_of_validation(?X, ?Y) :-
3. ?X <:: ?Y,
4. ?Y \= clabject,
5. writeln(?X, ’ can only be an instance of CLABJECT’) @ _plg.
The operator is defined by _op(400,xfx,<:) statement. The first argument
(400) defines precedence order to follow when statement contains other ele-
ments. We define precedence as the same as F-logic’s instance-of relationship
precedence.
The Lines 2-5 represent a rule in F-Logic to validate the linguistic instance-of
relationship. If the model element in ontological stack is not a linguistic instance
of clabject (?Y\=clabject), then it prints a validation fail message on the screen
(Line 5).
Ontological instance-of relationship: We introduced new operator ’<::’,
which specifies the semantics of the ontological instance-of relationship. The
rule (Lines 2-5) checks the potency before ontologically instantiating the model
element.
1. :- _op(400,xfx,<::).
2. ?X <:: ?Y :-
3. ?Y[potency -> ?_P], ?_P == 0,
4. \+(?X[potency -> ?_P - 1]),
5. writeln(’cannot instantiate! It should be P > 0.’) @ _plg.
The second argument of the _op() operator, xfx is used to define the type
where f stands for the operator, and x and y stand for the arguments. The
negation operator is denoted by \+ symbol in Flora-2.
3.3 Querying and Validation
An essential feature of F-logic is reasoning. This paper focuses on the querying
and validation aspects of reasoning. A knowledge base is built based on the facts
(e.g. like the ones in the previous subsection) and can be easily queried. The
following facts illustrate ontological instance-of relationships on the motivational
example.
39
weir_mc_series_centrifugal_pump<::rotating_mechanical_equipment.
weir_pump_sn_123<::weir_mc_series_centrifugal_pump.
Validation rules can be introduced to check certain properties of the concepts
and relationships. For example, the following predicate, validation rule checks
for the condition that a potency of a property should be equal or less than the
potency of an object.
validate_property_potency(?Prop):-
?X[property->?Prop],
?X[potency->?XPot],
?Prop[potency(?X)->?PropPot],
?PropPot=?Potency].
?- writeln(’Give me all instances of the pump’) @ _plg,
instances(?X, weir_mc_series_centrifugal_pump).
4 Related work
Even though multi-level modelling was introduced more than ten years ago, the
formalisation of its semantics has only recently been addressed. Research on
multi-level modelling started to get momentum, and some formalization or im-
plementation attempts were made. In this section we compare related work using
six comparison criteria: (1) Linguistic extension and open semantics: support to
extend the linguistic meta-model with new concepts and relationships. E.g. speci-
fying semantics of ”membership” relationship, (2) object-oriented semantics: the
framework is based or supports object-oriented modelling principles, (3) inte-
grated framework : a single framework for formalization, querying and validation,
(4) relationship across levels: a support for the relationships across levels in the
ontological stack, (5) mediation of relationship: a need of intermediate relation-
ship to instantiate the relationship in not-immediate ontological/instantiation
level, and (6) single and multi-potency semantics: a support of single and multi-
potency concepts in the ontological stack (see Figure 2).
The evaluation of some multi-level modelling approaches is illustrated in
Table 2.
The linguistic and semantic extension criteria are covered by almost all of
approaches. The object-oriented criterion is supported by most of approaches
including this paper. We benefit from object-orientation in two ways: (1) It
already covers part of multi-level modelling and (2) it is built-in paradigm of
F-Logic. In the context of the integrated framework criterion, ConceptBase
[19] (based on Datalog and Telos) addresses formalism and validation, and
METADEPTH [21] deals with all components of the integrated framework:
40
Criteria/Approaches [4] [11] [19] [21] [7] MiF
Linguistic extension and open semantics − X X X X X
Object-oriented X − X − X X
Integrated framework X X − − X X
Relationship across levels − − X X − −
Mediation of relationship − − X X − X
Single and multi-potency semantics X − X X X X
Table 2: Evaluation of multi-level modelling approaches.
formalization(METADEPTH), querying(Epsilon Object Language (EOL)) and
validation (Epsilon Validation Language (EVL)). The Multi-level modelling in
F-Logic (MiF) approach uses F-Logic / Flora-2 to implement the formalism,
querying and validation. The relationship across levels is used between models
with different ontological structures and in different domains/spaces [19,21]. MiF
approach could also support relationship across levels as well, however we did
not come across a use case in the OGI Pilot so far. All DPF[21], DDI[19] and
MiF support mediation of relationship, additionally MiF supports mediation of
a composition relationship as well. Almost all approaches support the criterion
of single and multi-potency semantics. F-OML [7] approach behaves as the same
as MiF approach, except in mediation of relationship criterion.
5 Conclusion
In this paper we introduced an alternative implementation of multi-level mod-
elling using F-Logic and Flora-2. We have applied the implementation on a
subset of the OGI Pilot use case, which dealt with the specification of a pump
using the ISO 15926 standard. The main benefit behind this proposal is the
integrated approach of F-Logic which allows the specification of the semantics,
ontological stack and reasoning capabilities (i.e. querying and validation) in a
single framework, its object-oriented semantics and its wide acceptance in the
ontology modelling community. In the future we plan to implement the semantics
of another standard used in the OGI Pilot, the MIMOSA standard and use the
reasoning capabilities of F-Logic to automate the matching and transformation
between ISO 15926 and MIMOSA.
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