=Paper=
{{Paper
|id=None
|storemode=property
|title=A Framework for Semantic Checking of Information Systems
|pdfUrl=https://ceur-ws.org/Vol-915/paper_11.pdf
|volume=Vol-915
|dblpUrl=https://dblp.org/rec/conf/invit/AlvesSSJ12
}}
==A Framework for Semantic Checking of Information Systems==
https://ceur-ws.org/Vol-915/paper_11.pdf
A Framework for Semantic Checking of Information
Systems
Gonçalo Alves1, João Sarraipa1, João P. M. da Silva2 and Ricardo Jardim-Gonçalves1
1
CTS, UNINOVA, Dep.º de Eng.ª Electrotécnica, Faculdade de Ciências e Tecnologia, FCT,
Universidade Nova de Lisboa, Caparica, Portugal
g.alves@campus.fct.unl.pt,jfss@uninova.pt,rg@uninova.pt
2
CT2M, Department of Mechanical Engineering, University of Minho,Guimarães, Portugal
jpmas@dem.uminho.pt
Abstract. In this day and age, enterprises often find that their business benefits
greatly if they collaborate with others in order to be more competitive and pro-
ductive. However these collaborations often come with some costs since the
worldwide diversity of communities has led to the development of various
knowledge representation elements, namely ontologies that, in most cases, are
not semantically equivalent. Even after solving once the establishment of a se-
mantic alignment with other systems, they do not keep unchanged. Conse-
quently, they need to check regularly its semantic alignment. Therefore, to aid
in the resolution of this semantic interoperability problem, the authors propose a
framework that intends to provide generic solutions and a mean to validate the
semantic consistency of ontologies in various scenarios, thus maintaining the
interoperability state between the enrolled systems.
Keywords: Semantic Interoperability, Ontology Validation, Consistency
Checking.
1 Introduction
Nowadays, in an increasingly global business environment, several companies have
found that to make themselves more competitive and productive they had to collabo-
rate with others, if they are to compete with the larger organizations [1]. However the
same globalization in the professional field that led to the collaboration between com-
panies, also led to the development of various knowledge representation elements,
such as ontologies, that are not semantically coincident [2]. Thus interoperability
problems appeared when these different systems try to exchange or share information
with one another. After having established seamless communication and semantica-
lignment between systems it was identified the necessity of having “something” that
allows companies to track their semantic evolution to keep the consistency and valid-
ity of their knowledge representation elements. To this effect, an interoperability
framework that provides a set of assumptions, concepts, values and practices (meth-
ods & tools) [3] and that contemplates several scenarios for the semantic checking is a
possible solution to the semantic interoperability maintenance.
2 Semantic Checking Framework
The proposed framework main purpose is to provide generic guidelines for the se-
mantic checking of a knowledge base. It was defined based on the three types of on-
tology consistency suggested by Li et al. in [4] that are single ontology, composite
ontologies and multiple ontologies.It is complemented by the knowledge mapping
types described by Agostinho et al. in [5] that are the structural and conceptual map-
ping types. Agostinho et al. in [5], further propose a 5-tuple mapping expression that
is used to formalize morphisms between model elements and to minimize inconsis-
tencies. However, imperfect mappings can lead to semantic mismatches which can be
lossy, when losses of information are recorded or lossless when no information loss is
recorded. The proposed framework (Table 1)shows the main characteristics that an
ontology based information system should comply to maintain semantic consistency.
Table 1. - Semantic Checking Framework
Single Ontology Composite Ontologies Multiple Ontologies
3.Automatic reasoning; 5.Ad hoc synchronization;
Structural 1.Automatic reasoning
Automatic synchronization Automatic reasoning
4.Human action plus auto- 6.Human action plus automatic
2.Human action plus
Conceptual matic reasoning; reasoning;
automatic reasoning
Automatic synchronization Ad hoc synchronization
The framework is composed of 6 items. Items 1 and 2 refer to scenarios where only a
single ontology is involved. For item 1, a simple reasoning process suffices to verify
the structural consistency of the ontology. In addition, item 2, also requires human
action. This is because the user needs to create instances of the concepts to test if after
running the reasoner such concepts are well positioned in the ontology. Items 3 and 4
of the framework denote cases where the knowledge base aggregates various ontolo-
gies. On item 3, in addition to an automatic reasoning process, an automatic synchro-
nization mechanism is also required. Since composite ontologies are composed of two
or more ontologies merged together, if a structural change occurs in one of the on-
tologies, then this change needs to be reflected in all the elements. On the other hand,
item 4 additionally requires human interaction to the automatic reasoning and syn-
chronization processes. Here, the user also needs to create instances with the same
objective mentioned for the item 2. Moreover in this case, the concepts need to be
well represented in the merged ontology to avoid repetitions and that is why the syn-
chronization and reasoning are both required. Finally, items 5 and 6 of the framework
are applicable in scenarios where multiple but separate ontologies are involved. In
item 5, besides having an automatic reasoning process, an ad hoc synchronization
process is also required, in order to align the knowledge represented in the various
components. This means that any changes that occur in a certain element of the sys-
tem must also be reflected in all the other components. Since these types of systems
can be very complex, knowing the synchronization process facilitates the further se-
mantic checking. In entry 6 it is also needed human intervention, by the same reasons
as in the other conceptual checking items. It is needed to create instances and then
execute the reasoner to check its conceptual definition. To accomplish the communi-
cation checking between ontologies it is also needed to know its particular synchroni-
zation processto then execute modifications in one side that could be reflected in the
other side.
3 Use Case Demonstration
The use case demonstration refers to item 6 of the framework and features a scenario
between a bolt manufacturer and retailer. To be able to collaborate with one another it
was decided to follow the MENTOR methodology [5] to build a reference ontology to
serve as a mediator to their interactions. Here the semantic interoperability problems
derive from the different definitions of involved concepts in this domain. After identi-
fying these differences both the manufacturer and the retailer need to come to a con-
sensus regarding those terms and definitions by adopting reference ones. Upon reach-
ing the reference terms and definitions, mappings between each term of the entities
with the adopted references ones are established. Based on these elements, a reference
ontology was built, along with the ontologies of the manufacturer and retailer (Fig.1).
Retailer Ontology Manufacturer Ontology Reference Ontology
Fig.1. Used Ontologies
To verify the consistency of the involved concepts, instances were created in the
“Thing” class of the retailer, manufacturer and reference ontologies and a set of rules
that aim to represent the mappings between the concepts were defined.These in-
stances were created there to ensure that by reasoning the system puts them in their
corresponding classes, ensuring the conceptual consistency of the system. Fig.2 (a)
shows the retailer and reference ontologies with the created instances inferred to their
proper classes (both “b” and “b1” concepts were inferred to “Bolt” and “Bolt1”).
Properties Properties
Properties
Properties
a) b)
Fig.2.After Reasoning Example: a) Retailer – Reference; b) Manufacturer Reference
Contrarily to the previous example, in Fig.2 (b) it is possible to observe some loss of
information because although both instances (“b” and “b2”) are represented within the
reference ontology, the same cannot be said regarding the manufacturer’s ontology
since only “b2” is represented. This is because of the “Tolerance” definitions
represented by each of the ontologies. While the reference ontology distinguishes
between “Upper and Lower Tolerances”, the manufacturers only define a single toler-
ance, assuming an equal value for “Upper” and “Lower”. This means that if different
values for the “Upper and Lower Tolerances” are defined in the reference ontology
then a conflict is created. Since the manufacturers’ ontology does not have such dis-
tinction, therefore will not know which value is the correct one, leading to possible
inconsistencies in the knowledge representation.
4 Conclusions and Future Work
The proposed framework was developed with the idea to provide general guidelines
to various contexts and situations, allowing organizations to effectively assess if their
knowledge representation elements still semantic consistent. Following such guide-
lines it was possible to assess the semantic consistency of the involved ontologies on
a small case study scenario that comprises a bolt retailer and a manufacturer. The
authors have also been able to validate items 1 and 2 of the framework, as well as
item 5, where a prototype of an ad-hoc synchronization tool has been developed o this
case between a wiki and an ontology. In conclusion, the proposed framework could
prove to be a valuable asset in helping in the semantic checking of knowledge reposi-
tories. In terms of future work, the authors are working on developing scenarios re-
garding composite ontologies.
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