=Paper=
{{Paper
|id=Vol-1449/saoa2015-7
|storemode=property
|title= Extension Rules for Ontology Evolution within a Conceptual Modelling Tool.
|pdfUrl=https://ceur-ws.org/Vol-1449/saoa2015-7.pdf
|volume=Vol-1449
|dblpUrl=https://dblp.org/rec/conf/jaiio/BraunC15
}}
== Extension Rules for Ontology Evolution within a Conceptual Modelling Tool. ==
https://ceur-ws.org/Vol-1449/saoa2015-7.pdf
Extension Rules for Ontology Evolution within a
Conceptual Modelling Tool
Germán Braun1,2 and Laura Cecchi1
1
Grupo de Investigación en Lenguajes e Inteligencia Artificial
Departamento de Teorı́a de la Computación - Facultad de Informática
Universidad Nacional del Comahue
2
Consejo Nacional de Investigaciones Cientı́ficas y Técnicas (CONICET)
Abstract Ontology development and maintenance are complex tasks,
so automatic tools are essential for a successful integration between the
modeller’s intention and the formal semantics in an ontology. Never-
theless, tools need to provide a way to capture the intuitive structures
inherent to the conceptual modelling and to focus on ontology elements
currently being refactored by abstracting the user from the whole ontol-
ogy without losing consistency. This can be done by means of a set of
extension rules that identify elements from an ontology and suggest pos-
sible consistent evolutions. Rules guide the development of ontologies
by taking source elements and refactoring. In this paper, we present a
small catalogue of extension rules to cover these identified requirements
and thus to be integrated into a tool for ontological modelling as built-
in reasoning services. Each rule is defined and analysed by considering
different theories of design patterns.
1 Introduction and Motivation
Ontology development and maintenance are complex tasks, so automatic tools
are essential for a successful integration between the modeller’s intention and
the formal semantics in an ontology. Domain experts capture the knowledge in
the universe of discourse but they have a limited understanding of the semantics
of ontology representation languages. Moreover, a good comprehension of the
implicit knowledge in a middle-size ontology formalisation is difficult even for IT
experts. Thus, automatic tools are essential for a successful integration between
the modeller’s intention and the formal semantics in an ontology.
Two of the most important features for any ontology development tool are
support for graphical representation and a consistent integration with a back-
end reasoner for helping in the management of implicit knowledge. Nevertheless,
tools also need to provide a way to capture the modeller’s intentions or the
intuitive structures inherent to the conceptual modelling. The first ones are
sources for abstractions and for exploring, composing and checking the ontology
by making explicit to the user its overall semantic. The last one provides a
way to focus on ontology elements currently being refactored by abstracting
the user from the whole ontology maintaining consistency and giving support
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2 Braun Germán and Cecchi Laura
to the ontological evolution. This can be done by means of a set of extension
rules that identify elements from an ontology and suggest possible consistent
evolutions. Rules guide the development of ontologies by taking source elements
and refactoring together with the underlying reasoning services.
In order to offer rules-based support, some efforts have been undertaken. In
particular, Guizzardi et al. [1] propose an automatic model-checking editor that
takes advantage of a well-behaved and predefined set of design patterns. Unlike
our approach, the modelling rules are derived from this set of patterns and are
implemented by means of an interactive dialogue between the modeller and an
automated tool running these rule sets. Interfaces with reasoning systems have
not been considered in this work.
In this paper, we present a set of extension rules which work at intentional
level (TBox) of an ontology. An extension rule is a Description Logic (DL) struc-
ture with an antecedent which must be satisfied in the model to be applied, and
a consequent which contains the new knowledge to be asserted. Extension rules
are to be used together with other artefacts as user queries and reasoning sys-
tems, where the former identifies relevant parts of an ontology and the latter
inquires the model to check rules applicability, their possible consequences and
side effects. A rule is applicable iff its antecedent is satisfied and its consequent
maintains the consistency of the model. Our rules-based approach and the ontol-
ogy design patterns [2] can be considered as complementary since both provide
a foundation for modularity to maintain and reduce the complexity of designing
and understanding ontologies. Our alternative offers flexibility and a fine-grained
level where the focus is on a reduced set of graphical elements to analyse so as
to introduce new ones, as opposed to design patterns which are not orientated
towards ontology evolution since they involve more elements in their definitions
than rules. Similar to the design patterns, the rules allow to describe views in
which the evolution suggestions are consistent. As a consequence, they have the
effect of changing only the ontology elements involved in the refactoring, which
can be intra- or inter-ontology elements. In addition, rules present modularity
as a key property so that they could be modified and extended without affecting
the host methodology.
This rule-based approach is to be implemented on the methodology under-
lying to ICOM tool [3, 4, 5] extending the reasoning services provided by the
tool. In this context, we will consider its graphical primitives and its translation
to the underlying DL ALCQI as our initial development framework. ICOM is
an advanced conceptual modelling tool, which allows the user to design mul-
tiple diagrams adopting as neutral language a hybrid of an EER and UML
languages with inter- and intra-schema constraints. Complete logical reasoning
is employed by the tool to verify the specification, infer implicit axioms, i.e. new
intentional knowledge, devise stricter constraints and manifest any inconsist-
ency. The leverage of automated reasoning to support the domain modelling is
enabled by a precise semantic definition of all the elements of the class diagrams.
ICOM graphical language can be represented in ALCQI, although the graph-
ical language cannot express inverse roles. ALCHIQ is necessary for the full
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Extension Rules for Ontology Evolution within a Conceptual Modelling Tool 3
ICOM language, including non-graphical extensions of role hierarchies and the
view language. Moreover, ICOM allows partial graphical evolution of an ontol-
ogy schema (intentional knowledge) by adding axioms which are inferred through
defined reasoning services. Users will see the ontology graphically completed and
evolved with all the deductions and expressed in the graphical language itself.
This work is structured as follows. Section 2 details the set of rules with a
brief explanation and an example of each one of them. Discussions and related
works are presented in section 3. To conclude the paper, section 4 elaborates on
final considerations and directions for future works.
2 Extension Rules
The objective of the extension rules is to reduce the search space of ontology ele-
ments, and thus decrease the complexity of designing and understanding ontol-
ogies. They also focus on identifying relevant parts of models, and facilitate
ontology verification, maintenance and integration. This approach allows to dis-
cover knowledge which is not inferred from a logical point of view but it is
suggested from an intuitive point of view. Rules can be inter-modules in or-
der to reuse, combine and share modules, which are central issues in ontology
engineering branches as modularity.
In comparison with ontology design patterns [2], extension rules work with
elements of ontologies in a greater level of detail than patterns. While Gangemi’s
approach assumes that there exist problems that can be solved by applying com-
mon solutions and define small, task-oriented ontologies with explicit document-
ation, extension rules handle a limited set of ontology elements so as to introduce
new ones by affecting the overall shape of the ontology and maintaining its con-
sistency.
The aim is to integrate this set of rules to a graphical tool so that the graph-
ical ontology and rules are mapped to the ICOM DL. This logical support en-
ables defining when an extension rule can be applied, deducing implicit axioms
to display the implications of the suggestions derived from extension rules and
asserting the new intentional knowledge in the underlying knowledge base.
The general form of the rules is the following:
{A1 , A2 , ..., An } ⇛ {B1 , B2 , ..., Bm }
such that Ai and Bj are TBox formulae from DL of the underlying reasoner,
1 ≤ i ≤ n and 1 ≤ j ≤ m.
In order to apply a rule, we must check if its antecedent is satisfied in the
logical representation of the graphical ontology Ω, denoted by Θ, and if its con-
sequences are consistent with this representation. Thus, a back-end reasoner
should be inquired about the satisfiability of the following properties of Ω,
{A1 , A2 , ..., An }, and about consistency of every resulting ontology after applying
the rule consequent. Each Bi represents a different possible ontology extension
and it is the user who is required to select which Bi , 1 ≤ i ≤ m will be applied,
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4 Braun Germán and Cecchi Laura
if any. Therefore, the back-end reasoner would be inquired if Θ ∪ Bi is consistent
for any i, 1 ≤ i ≤ m
We present the extension rules catalogue by means of a brief explanation
about each rule with some comparisons with other approaches such as design
patterns or OntoUML [6], which is beyond the scope of the tool. Moreover, rules
are formalised as DL formulae since they work on the ICOM underlying DL and
a graphical description about how they are interpreted is depicted. Examples
about how the rule is used are shown in the context of a domain ontology, where
rule suggestions are depicted in dashed line. According to ICOM’s graphical syn-
tax, the primitives Ci and Ai are ICOM’s classes and associations respectively,
which will be translated as ALCIQ concepts, and ri are ICOM’s roles which
will be also considered ALCIQ roles.
⊑-rule # 1
{A2 ⊑ A1 , A1 ⊑ ∃r1 .C1 , A2 ⊑ ∃r1 .C2 } ⇛ {C2 ⊑ C1 , C2 ≡ C1 }
r1 r1 from
A1 C1 A1 C1 hasSport OlympicSport
≡
r1 r1 from
A2 C2 A2 C2 hasWinterSport WinterSport
(a) (b) (c)
Figure 1: (a)(b) Graphical representation of ⊑-rule # 1. (c) ⊑-rule # 1 applied in
Olympics
The aim of this rule is to identify missing IsA relationships between classes
(and associations) and suggest them. It is intended to capture those scenarios
whose concepts could be related by means of possibly the same role, and thus
suggest a missing relationship as a subsumption axiom obtaining a more pre-
cise model or an equivalence axiom in which case the involved classes could be
factored.
The rule, which is graphically rendered as shown in Fig. 1a and 1b, can be le-
gitimised by analysing the involved classes and their relationships. By definition,
if A2 ⊑ A1 and A1 ⊑ ∃r1 .C1 then A2 ⊑ ∃r1 .C1 . Moreover, if any explicit inequal-
ity exists in the ontology then we can suppose both roles r1 in A1 ⊑ ∃r1 .C1 and
A2 ⊑ ∃r1 .C2 represent the same role and they are identically defined. Consider-
ing these arguments, the rule consequent {C2 ⊑ C1 ,C2 ≡ C1 } could be proposed
as possible extensions. Similar to Gangemi’s design patterns [2], our rule can
be a way to complete some patterns as Classification whose aim is to represent
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Extension Rules for Ontology Evolution within a Conceptual Modelling Tool 5
the relations between concepts and entities and Type of entities, which allows to
identify the type of any element of the knowledge base.
Another way of validating this rule is using the Guizzardi’s definitions in [6]
although in this case we should consider what object types are being represented.
A type is rigid iff for every instance x of that type, x is necessarily an instance
of that type. As these types can be related in a chain of taxonomic relations
and if in the domain under modelling C1 and C2 are defined as rigid types
then the Subkind pattern in [1] is completed by means of the rule suggestion
C2 ⊑ C1 . Also, the suggested subsumption can be justified if C2 is modelled as
a phase type, which are anti-rigid types whose instances can move in and out of
the extension of these types without affecting their identity. Consequently, the
Phase Pattern could be also completed by C2 ⊑ C1 .
Example 1. Let us suppose the following partial and textual ontology Ω:
hasSport ⊑ ∃f rom.OlympicSport
OlympicSport hasSport min ⊑ OlympicSport ⊓ (≥ 1 f rom− .hasSport)
OlympicSport hasSport max ⊑ OlympicSport ⊓ (≤ 1 f rom− .hasSport)
hasW interSport ⊑ ∃f rom.W interSport
W interSport hasW interSport min ⊑ W interSport ⊓ (≥ 1 f rom− .hasW interSport)
W interSport hasW interSport max ⊑ W interSport ⊓ (≤ 1 f rom− .hasW interSport)
hasW interSport ⊑ hasSport
If we match these ontology elements to the ⊑-rule # 1, we can obtain a
consistent ontology Ω ′ , which defines a new type for the OlympicSport by
means of W interSport ⊑ OlympicSport as depicted in Fig. 1c, or a Ω ′′ where
W interSport ≡ OlympicSport.
⊑-rule # 2
{C2 ⊔ C3 ⊔ ... ⊔ Cn ⊑ C1 , A1 ⊑ ∃r1 .C2 ,..., A1 ⊑ ∃r1 .Cn } ⇛ {A1 ⊑ ∃r1 .C1 }
This rule, which is graphically represented as depicted in Fig. 2a, intends to
identify redundant relationships between concepts within an IsA relationship.
The same roles involving each subclass of an IsA could be defined by relating it
to the superclass of the hierarchy.
Although it is not caught by the current definition of the rule, if a totality
constraint is defined then the rule suggestion is a strong suggestion since each
instance of the superclass belongs to one of its subclasses. Consequently the
validation can be obtained from a logical point of view: if ∀i, 2 ≤ i ≤ n, Ci ⊑
C1 and A1 ⊑ ∃r1 .Ci then A1 ⊑ ∃r1 .C1 . Nevertheless, if any other constraint
(exclusive or partial) is defined then the rule suggestion is weaker than the
previous one.
Despite being ⊑-rule # 2 a common-sense rule, in our methodological con-
text it enables the user to optimise relationships in possibly big-size ontologies
without removing existing ones in order to show each consistent modelling scen-
arios.
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6 Braun Germán and Cecchi Laura
r1
A1 C1 hasSport
from
OlympicSport
r1 r1 from
from
C2 . . . Cn WinterSport SummerSport
(a) (b)
Figure 2: (a) Graphical representation of ⊑-rule # 2. (b) ⊑-rule # 2 applied in
Olympics ontology
Example 2. Let us consider the ontology shown in Fig. 2b where an olympicSport
can be a winterSport or a summerSport and both related to other classes by
means of the association hasSport and the role f rom. The DL representation
of this ontology is not included here but it is similar to the one shown in the
example associated to ⊑-rule # 1.
The new relationship hasSport ⊑ ∃f rom.OlympicSport will be suggested by
the rule, as depicted in Fig. 2b in dashed line, and thus it will allow to optimise
the ontology and reduce the underlying representation as follows (as long as user
decides to remove the redundant existing relationships).
W interSport ⊔ SummerSport ⊑ OlympicSport
hasSport ⊑ ∃f rom.OlympicSport
OlympicSport hasSport min ⊑ OlympicSport ⊓ (≥ 1 f rom− .hasSport)
OlympicSport hasSport max ⊑ OlympicSport ⊓ (≤ 1 f rom− .hasSport)
c-rule
{A1 ⊑ ∃r1 .C1 , C2 ⊑ C1 , A2 ⊑ A1 , A2 ⊑ ∃r1 .C2 , C1 ⊑ ∃r1− .A1 } ⇛ {C2 ⊑ ∃r1− .A2 }
The c-rule intends to capture those cardinalities that cannot be logically
deduced but they can be considered as graphically intuitive in models such as the
ontology shown in Fig. 3a. At first, nothing can be concluded about the minimum
participation of r1 relating C2 with A2 , since the A2 relationship may not contain
all instances of C2 in the A1 relationship. Nevertheless, from an intuitive and
graphical point of view, we consider that this minimum participation could be
inherited since both C2 and A2 are subclasses of C1 and A1 respectively, then
they have the same properties than their parents. In certain contexts, the c-
rule can be considered as an instance of the Gangemi’s pattern named Role task
where C2 is a role, which is represented in the pattern as a concept that classifies
an object, and A2 is a task. While the pattern does not explicit any cardinality,
we conclude that roles make sense only if they have at least one task associated.
According to Guizzardi’s definitions, this rule can be also considered as an
instance of the Role Modeling Design pattern [1], where C2 must be modelled
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Extension Rules for Ontology Evolution within a Conceptual Modelling Tool 7
r1 1..n r1 1..n
A1 C1 origin Call
A2
r1 1..n C mOrigin
r1 1..n MobileCall
2
(a) (b)
Figure 3: (a) Graphical representation of c-rule. (b) c-rule applied in Phone ontology
as a role, i.e. an anti-rigid type similar to phase type but the changes among
subtypes occur due to changes in their relational properties. As a consequence,
there exists a relational dependence between C2 and A2 which requires minimum
cardinality ≥ 1 in order to justify the existence of A2 in the model.
Example 3. Let us suppose the following partial ontology Ω from Phone domain,
which is depicted in Fig. 3b and translated to DL as follows. Ω models calls and
mobile calls which could have different origins.
Call ⊑ (≤ 1 r1− .origin)
Call ⊑ ∃r1− .origin
origin ⊑ ∃r1 .Call
Call origin min ⊑ Call ⊓ (≥ 1 r1− .origin)
Call origin max ⊑ Call ⊓ (≤ 1 r1− .origin)
mOrigin ⊑ ∃r1 .M obileCall
M obileCall mOrigin min ⊑ M obileCall ⊓ (≥ 1 r1− .mOrigin)
M obileCall mOrigin max ⊑ M obileCall ⊓ (≤ 1 r1− .mOrigin)
M obileCall ⊑ Call
mOrigin ⊑ origin
According to rule description, M obileCall ⊑ ∃r1− .mOrigin could be pro-
posed to extend Ω. Notice that intuitively only one origin is possible for any
call so that this could be considered for a mobileCall since it is also a call.
r-rule
{C2 ⊑ C1 , A1 ⊑ ∃r1 .C1 , A2 ⊑ ∃r2 .C2 , A2 ⊑ A1 } ⇛ {r2 ⊑ r1 }
The aim of this rule, depicted in Fig. 4a, is to find recurrent ontological
structures where a hierarchy of roles can be defined and suggested. From a
logical point of view, if A2 ⊑ A1 and A1 ⊑ ∃r1 .C1 then A2 ⊑ ∃r1 .C1 . Moreover,
if C2 ⊑ C1 and A1 ⊑ ∃r1 .C1 then A1 ⊑ ∃r1 .C2 . Consequently, both A2 and C2
concepts are related to C1 and A1 respectively through r1 so that there could
exist unexpected relationships between A2 and C1 or C2 and A1 . This scenario
can be identified as the Relation Specialization anti-pattern in [7] where one of
the proposed solutions matches the consequent of our r-rule.
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8 Braun Germán and Cecchi Laura
r1 r1
A1 C1 origin PhonePoint
r2 r2
A2 C2 mOrigin Cell
(a) (b)
Figure 4: (a) Graphical representation of r-rule. (b) r-rule applied in Phone ontology
Example 4. Fig. 4b shows a model extracted from ontology Phone and how the
rule can be applied to this model. Let us suppose that two instances PhonePoint,
for example phonePoint1 and phonePoint2, are related to origin1 and origin2
respectively. Notice that without asserting r2 ⊑ r1 and if both phone points are
considered as instances of Cell then it could not be guaranteed that these cells
belongs to the same origin.
3 Discussion and Related Works
All the logical systems suppose ideal objects but they do not consider the in-
formation about the reality or intuition. This explains that only those axioms
implied by the current models will be suggested as possible ontology evolutions.
Nevertheless, if we consider the modelling as an approach based on empirical
facts, we need something more than only the formal logic. We need observa-
tions, experiments and pattern analysis to confirm our hypothesis. The benefit
of a rules-based approach is to offer a trade-off between the inherent rigidity to
the logic systems and the intuitive characteristics of the ontological modelling.
Similar to our approach, Guizzardi in [1] proposes to use rules in pattern-
based design. However, the proposed inductive rules are extracted from a subset
of the design patterns underlying to OntoUML and its application requires of an
intensive interaction between users and the tool so as to specify each possible ele-
ment and its relationships in the ontology under development. As a consequence,
reasoning services are not invoked during this modelling process. Respecting to
ontology evolution, it is partially supported by reducing the possible choices
of modelling primitives to be adopted and by offering a step-by-step modell-
ing activity. Finally, this feature has not been developed in the last OntoUML
version.
In order to analyse the current graphical tools, we consider the following
key factors: the graphical, automatic reasoning and evolution support and their
integration in the tool. OntoUML [6] is a graphical tool but the reasoning and
evolution support is limited. In spite of offering an insufficient graphical inter-
face, Protégé [8] and TopBraid Composer [9] allow this integration. The infer-
ences are shown in the graphical editor, but these are only restricted to IsAs
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Extension Rules for Ontology Evolution within a Conceptual Modelling Tool 9
hierarchies. Their evolution support is also partial: manual, ontology differences
(Protégé) and versioning and collaboration (TopBraid Composer). Finally, NeOn
toolkit [10], Kaon2 [11] and SWOOP [12] provide access to reasoners but they
are not graphical-based tools. Respecting to evolution, only NeOn incorporates
a specific framework to support it from external sources. Kaon2 and SWOOP
simply offer some operations as redo and undo (Kaon2) or imports and versioning
(SWOOP). Other tools as GrOWL [13], OWLGrEd [14], Graphol [15], “model
outline” framework [16] and VOWL [17], which is a Protégé plugin, are also
graphical-centered tool. They define a graphical syntax and semantics providing
users with a visual representation of their models and thus avoid any complex
textual syntax. Nevertheless, the reasoning support is not provided in any of
these tools as ICOM does and the ontology evolution is not properly supported.
The intention behind rules is to reduce the complexity of the evolution process
and they are defined to be implemented within a graphical tool. Nevertheless,
they cannot be isolated from other complementary artefacts such as user queries
which allow to identify relevant parts of an ontology and back-end reasoning
systems which allow to inquire the model to check rules applicability and their
possible consequences. In any case, it is the user who decides which of the sug-
gestions (rule consequences) are appropriate, if any, according to his intended
model.
4 Conclusions and Future Works
This work introduces a small catalogue of extension rules which intends to cap-
ture the intuitive characteristics inherent to the ontology modelling. Each rule
has been defined and analysed by considering different theories of design patterns
and by showing the intended meaning through illustrations where the intuition
is presented. The aim is to place emphasis on a reduced search space of possible
ontology extensions, and thus also reduce the complexity when trying with large
ontologies. This alternative offers flexibility and a fine-grained level where the
focus is on a subset of ontology elements to analyse in order to introduce new
ones, in contrast to design patterns which are not orientated towards ontology
evolution since its smallest unit of work is an ontology when in rules it is an
ontological element. Then they have the effect of changing only the ontology
elements involved in the refactoring, which can be intra- or inter-ontology ones.
Rules must be used together with other artefacts such as user queries and back-
end reasoning systems to identify relevant parts of an ontology and check the
consequences of application on the whole model. Furthermore, the modularity is
a key property in this approach so that some changes on the set of rules are in-
dependent of the underlying methodology. An example about the usage of these
rules in a methodology has been published in [18].
As future works, we propose to identify new extension rules and define a
logical formalism as an application framework for these rules. The rules will
be also evaluated by means of two techniques such as a behaviour-based one,
which will allow us to register the number of suggestions accepted by user, and
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10 Braun Germán and Cecchi Laura
an opinion-based one, which will enable us to elicit users opinions about the
use of them [19]. We plan to provide support to user-defined rules and to rules
involving instances so that we could supply evolution at extensional level (ABox)
and possibly use it to extend the intentional knowledge.
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