=Paper=
{{Paper
|id=Vol-1420/wois-paper1
|storemode=property
|title=Translating XML Models into OWL Ontologies for Interoperability of Simulation Systems
|pdfUrl=https://ceur-ws.org/Vol-1420/wois-paper1.pdf
|volume=Vol-1420
|dblpUrl=https://dblp.org/rec/conf/bis/TanBT15
}}
==Translating XML Models into OWL Ontologies for Interoperability of Simulation Systems==
https://ceur-ws.org/Vol-1420/wois-paper1.pdf
Translating XML Models into OWL Ontologies
for Interoperability of Simulation Systems
He Tan, George Barakat, and Vladimir Tarasov
Tekniska Högskolan, Högskolan i Jönköping, Sweden
{he.tan, Vladimir.Tarasov}@jth.hj.se, sbarakat.george@gmail.com
Abstract. Today XML is a common format supporting interoperability
and information exchange between systems in the modeling and simula-
tion field. Although XML enables systems to agree on a common syntax
and understand the exchanged information, systems can misinterpret
them due to their di↵erent conceptualizations of the domain of inter-
est. In this paper, we present a framework for automatic translation of
XML simulation models which follow the High Level Architecture (HLA)
object model template specification, into OWL ontologies. In OWL on-
tologies the semantics of information is formally defined. It provides the
basis for interoperability and information exchange between simulation
systems on semantic level.
Key words: Semantic Interoperability, Ontology, Ontology Language,
OWL, XML
1 Introduction
One of the major problems in the modeling and simulation (M&S) field is to
improve interoperability and reusability of systems, especially when systems are
distributed, autonomous and heterogeneous [1]. The reason for this is that many
military and civil organizations need not just a single system that meets an
exactly specified set of requirements, but need solutions to meet diverse and
changing needs of users. WISE (Widely Integrated Systems Environment) de-
veloped by SAAB is a generic integration platform that allows connection of
simulation systems into a common environment and supports information ex-
change between the individual systems, regardless of individual architecture,
communication standards and protocols [2].
In WISE the information to be exchanged is described in a XML-based com-
mon representation which follows the High Level Architecture (HLA) Object
Model Template (OMT) specification [3]. Although the XML-based information
representation enables the systems to agree on a common syntax and under-
stand the exchanged information, the systems can misinterpret the information
due to their di↵erent conceptualizations of the subject of a domain. This may
result in a mismatch between the intended and actual e↵ect of the information.
The correctness of interoperability and integration depends completely on the
knowledge of human engineer about low-level data structures and his implemen-
Copyright © 2015 by the authors. Copying permitted for private and academic purposes.
This volume is published and copyrighted by its editors.
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tation of the semantically correct integration. In this paper we present our e↵ort
to extend WISE to support interoperability and information exchange between
simulation systems on semantic level. In particular, we propose a strategy for
how OWL ontologies can be generated automatically out of the XML models
of simulation systems. When the semantics of the exchanged information is for-
mally defined in OWL ontologies, it is possible to provide automated support in
defining integration and/or formal automatic verification of manual integration
using the ontologies.
The rest of the paper is organised as follows. Section 2 presents the back-
ground and the related work. In section 3 we describe our strategy for translating
the XML models of simulation systems into OWL ontologies. We evaluate and
discuss the quality of the ontologies generated from XML models in section 4.
In the end we present our conclusion and the future work.
2 Background and Related Work
2.1 Interoperability of Systems in the Modeling and Simulation Field
The M&S community has focused on technical layer for interoperability between
systems [4]. The development of the standards, such as HLA, has simplified
the integration tasks. The HLA defines a standard for the interoperation of
simulation systems through the communication of the models of objects and
events of interest. HLA OMT standardizes the common syntax specification for
the models. The semantics of the models is defined simulation objective specific
in the federation development and execution process and documented in the
federation object model (FOM) lexicon as well as in the federation agreements.
The problem for the standardized solution is that it is complex and high-cost to
integrate a new system having di↵erent information model. The more systems
integrated, the more complex it becomes to integrate an additional system.
WISE goes beyond the standardized solutions. The focus of system integra-
tion is switched from implementation details to the flow of information. The
integration platform is seen as an information infrastructure. It is responsible
for collecting, translating and delivering information. Each system only needs
communicate with the infrastructure. The engineer who builds a connection of
a system to the information infrastructure, manually defines the mappings be-
tween the model of the information the system needs, to a common model. The
WISE currently includes the common models for command and control informa-
tion in military, real-time simulation, and emergency alerts and public warnings
for the civil domain.
In WISE the information exchange models still follow the HLA OMT syntax
specification, and are implemented in XML. Since the semantics of information
exchange is not formally defined in the XML models, it is not possible to pro-
vide formal automated verification of the manual mapping, and/or automated
support in defining the mapping. The correctness of the connection depends
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completely on the knowledge of the engineer about low-level data structures and
his implementation of the semantically correct integration.
2.2 Ontology for Interoperability of Simulation Systems
Over the last few years the community has found that many challenges in the
area are on higher levels, underlying concepts and models that have to be aligned,
not the implementation questions [4]. Many researchers started to study how to
utilize ontology to promote semantic and pragmatic interoperability in the field.
One direction of the research is toward enhancing the semantic expressiveness
of the base object model (BOM) using ontology (e.g. [5, 6]). The BOM [7] is
a SISO standard to response to the semantics lacking in the HLA simulation
object model (SOM) and FOM, but itself does not contain sufficient information
for defining conceptualizations. While the semantics of SOM and FOM can be
defined in BOM using this kind of methods, it is usually very complex do the
alignment between BOMs.
Another direction is to develop complete ontology-based framework to achieve
semantic interoperability [Ra04]. To build up a simulation among distributed sys-
tems, developers have to develop domain ontologies for the individual systems
and an ontology for certain simulation objective. Although there are many meth-
ods, tools and guidelines for ontology development, building ontologies is still
not a simple task, particularly when engineers have no background knowledge
on ontology engineering techniques and/or they have not much time to invest
in domain conceptualization. In this paper we propose a strategy for how OWL
ontologies can be generated automatically out of the XML models, so that there
is no requirement on engineers to developing ontologies.
2.3 XML to OWL Strategies
Several strategies have been proposed for translating XML to OWL or RDF
meta-data (e.g. [9, 10]). All existing XML to OWL mapping strategies assume
that an XML document always contains instance data, so that the information
from an XML document is always mapped to OWL instances and an XML
schema is translated into an OWL model. Although most of the work deal with
creating an ontology from a single XML source, Janus framework [11] presents a
method for generating an ontology from a large source of XML schemas based on
pattern recognition. The work in [12] also proposes the solutions for generating
a local OWL model from heterogeneous XML data sources.
In the case of WISE project, every XML document represents either a model
for individual simulation system, or a common model for a simulation domain.
No actual data is included in XML documents. All XML documents are vali-
dated against an XML schema to verify the structure of the documents. As a
result, the adoption of any of the existing strategies is not sufficient to produce
the intended OWL ontologies in our project. We aim to propose a method fo-
cusing on translating XML to OWL on conceptual level. Every XML document
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is translated into an ontology representing a more specific simulation domain.
The XML schema is translated into an ontology that can be considered as the
top domain ontology for the M&S field.
3 The Translation
The XML model is based on the labelled tree, where the meaning of the tags-
nestings are interpreted by the program undertake on it. The OWL model is
based on the subject-predicate-object structure from RDF/RDF-S, where ob-
jects, their attributes and relations are naturally represented, and the semantics
is specified [13]. Translation from XML to OWL is to interpret the tree struc-
ture of XML, and to represent the intended model in the subject-predicate-object
structure. In this work we implemented the translation from XML to OWL Full
language [14].
3.1 Mapping Rules
Two kinds of entities present in WISE XML models: objects and events. An
object is an entity which persists over time (e.g. ”device”, and ”system”) while
an event is an entity which only exists momentarily (e.g. ”act”, and ”interac-
tion”). Every entity, either an object or an event, is translated into a OWL
class. Each entity always has a set of properties. Many of the properties are
datatype properties, describing relations between individuals and data values.
All the datatypes in WISE are user-defined types that always restrict the value
of a property to a subset of an existing type, not the build-in ones from XML
Schema. They are important in capturing the intended meaning of the elements
in the models. For example, the ”HLAfloat32LE” is a HLA standard datatype:
.
They are translated to RDF datatype classes.
Either a model for individual simulation system, or a common model for
a simulation domain, is described in a XML document. All XML documents
are validated against a XML schema to verify the structure of the documents.
The XML schema indeed describes very general concepts that are the same
across all XML object models. It defines what object is, what event is, their
general properties, datatypes specific in simulations, etc. It supports semantic
interoperability between the XML models of more specific simulation domains.
Therefore, the general model descried in the XML schema can be considered as
a top domain ontology for the M&S field. Table 1 gives the principle mapping
rules for the translation of the XML schema to the OWL top domain ontology.
3.2 The Translation Process
Figure 1 shows the process for the translation. First, a set of manually defined
mapping rules are used to generate an XSLT stylesheet to translate the XML
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Table 1. The principle mapping rules for the correspondences between XML schema
model and OWL ontology
XML Schema OWL Full
named xsd:elements (not within the element Class
”datatypes”)
ObjectProperty, DatatypeProperty
or AnnotationProperty (deter-
mined by the value of the type).
named ObjectProperty, DatatypeProperty
or AnnotationProperty (deter-
mined by the value of the type).
named xsd:complexType on the top level rdfs:Datatype
named xs:element within the element ”datatypes” rdfs:Datatype
xsd:minOccurs, xsd:maxOccurs owl:minCardinality,
owl:maxCardinality
XML XSLT OWL
generate transform
Schema Stylesheet Top Domain
Ontology
mapping
rules
XSLT XPath
Stylesheet validate configure
generate
transform
OWL
XML
Domain
Document
Ontology
Fig. 1. The Translation Process
schema into the top domain ontology. This is a one time translation when all
XML documents share the same schema. A produced XSLT stylesheets can be
used by any XSLT processor to automatically generate the desired ontology. The
second step is to translate the source XML document to an domain ontology.
Not all schema constructs defined in the XML schema appear in every XML doc-
ument. Since each XML document contains a di↵erent set of schema constructs,
XSLT stylesheet for translating each XML document is di↵erent. A configura-
tion file maintains the XPath expressions for each kind of OWL expressions
in the top domain ontology, including classes, properties and datatypes. If an
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XPath expression is evaluated as a valid XPath in the source XML document, a
corresponding XSLT stylesheet is generated based on the manually defined map-
ping rules. In the end, the produced XSLT stylesheet is used to automatically
generate the domain ontology from the source XML document.
4 Evaluation and Discussion
The goal of the work is to capture the semantics of exchanged information be-
tween simulation systems in XML models and represent them formally in OWL
ontologies, so the focus of our evaluation is to verify and validate the struc-
ture and semantics of the generated ontologies against XML data sources. One
proper method to perform this evaluation is relying on domain expert assessment
against a set of criteria [15]. In the evaluation we considered five ontology qual-
ity criteria: accuracy, consistency, completeness, clarity and adaptability [16].
Table II shows the criteria, assessment questionnaire and the assessment results
according to a 1-to-5 scale, where 1 corresponds to ’totally disagree’ and 5 to
’fully agree’. Three resulted ontologies are assessed by an ontology expert. The
ontology expert is also acquainted with the relevant XML data sources. One of
the three ontologies is generated from the XML schema, and the other two are
generated from XML models of two specific simulation domains.
The results from the assessment show that the translation process provides
accurate conceptualization during automatic translation of XML object models
into OWL ontologies. The assessment also shows that the resulted ontologies are
consistent. With regard to completeness, the expert ’fully agrees’ that the domain
of interest is appropriately covered in the resulted ontologies and is ’neutral’
about any implicit knowledge in XML object models. The latter can be explained
by the fact that the expert was also ’neutral’ about his level of expertise in the
simulation domain. However, the results still show soundness of the translation
process and the mapping rules. Regarding clarity, the expert found no ambiguity
in the names of classes and properties and that naming conventions were properly
applied. However, the expert was ’neutral’ about the ease of understanding of
the conceptualization of the constructed ontologies. Thus, more e↵ort can be
made to improve the descriptions of classes and properties and their clarity
level. Finally, the expert ’agrees’ that the resulted ontologies can be adapted to
di↵erent usages but comments that it is difficult to judge without being able to
evaluate the use of the constructed ontologies within their applications.
5 Conclusion
In this paper we presented an e↵ort to enable interoperability and integration
in the M&S field on the semantic level. XML is the common format for in-
formation exchange between systems in the field. We proposed the method for
automatically translating XML models in the domain into OWL ontologies. The
translation intends to capture the semantics of exchanged information in the
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Table 2. Ontology Evaluation Criteria and Result
Accuracy: determines if the asserted knowledge in the ontology agrees with Result
the expert’s knowledge about the domain. A higher accuracy comes from cor-
rect definitions and descriptions of classes and properties.
1. Are [rdfs:Datatype]s well structured and do they properly represent the 5
datatypes in XML object model?
2. Are [owl:Class]s well structured and do they properly represent the entities 5
in XML object model?
3. Are [owl:ObjectProperty]s well structured and do they properly represent 5
the possible relations between defined datatypes?
4. Are [owl:DatatypeProperty]s well structured and do they properly represent 5
the attributes of the defined datatypes, and entities?
5. Are [owl:AnnotationProperty]s well structured and do they properly provide 5
descriptions about the defined entities and their attributes?
Consistency: describes if the ontology does not include any contradictions.
Not only asserted knowledge must be logically consistent, but also the formal
and informal descriptions in the ontology should be consistent
6. Is there any logical contradictions inferred in the constructed ontologies. 1
e.g. by running a reasoner?
7. Is there any other contradictions, e.g. the documentation and comments of 1
an entity should be aligned with its definition in ontology?
Completeness: determines if the domain of interest is appropriately covered.
8. Are all the entities and datatypes described in XML object model repre- 5
sented in the ontology?
9. Is implicit knowledge in XML object model captured by the resulted ontol- 3
ogy?
Clarity: measures if the resulting ontology communicates the intended mean-
ing of the defined terms.
10. Is it easy to understand the conceptualization of the resulting ontology? 3
11. Are the names of classes and properties unambiguous? 5
12. Are the descriptions of classes and properties unambiguous? 4
13. Are the naming conventions properly applied to classes and properties? 5
Adaptability: measures how far the ontology can be adapted to anticipated
usages. The resulting ontology should o↵er the conceptual foundation for a
range of anticipated tasks
14. Could I apply the constructed ontologies in anticipated M&S tasks? 4
15. Could I extend or specialize the resulted ontology monotonically, i.e. with- 4
out the need to remove axioms?
XML models and represent them formally in OWL ontologies. The evaluation
has showed that the ontologies generated by the automatic translation success-
fully capture the semantics of the information and correctly represent them. One
direction of our future work is to develop algorithms and methods for automatic
integration support. When the semantics of exchange information is formally de-
fined in OWL ontologies, it is possible to provide automated support for defining
integration and formal automatic verification of manual integration.
122
Acknowledgment
We thank Kurt Sandkuhl for comments on the project. We also acknowledge the
technical support of Saab Training Systems AB.
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