OPN-Ont: Object Petri Nets Ontology Tool

Lynda Dib and Fouad Bousetouane

LASE- LAboratoire des Systèmes Embarqués,

Computer Science Department, Badji Mokhtar University, Annaba, Algeria

Keywords: OPN-Ont System, Ontology, Objects Petri Nets, Requests, Concepts, Relations.

Abstract: Ontologies are being used nowadays in many areas, including software engineering, business, and biology,

to evaluate their suitability for representing and simulating domain processes. To assist users in developing

and maintaining ontologies a number of tools have been developed. The representation of knowledge bases

and conceptual domain models, hierarchical process, the structural components that participate in the

process and the roles that they play in a complex domain, is therefore a major challenge for computer

scientists for this complex domain. Without aiming at exhaustiveness, our study combining ontology and

Petri Nets (PNs) tries to identify some promising tracks in this area, which seems a rather interesting

alternative in the optics of the expressive power of the deductive representations. The context of our work

consists to develop a graphical knowledge model for complex domain. This paper presents the OPN-Ont

(Object Petri Nets Ontology) model. In this system ontology is represented in the PNs format, which allows

verification of formal properties and qualitative and quantitative simulation. It leads to represent and exploit

the different ontological components: concepts, relations and requests. The illustration of our model is made

in biological domain where process supports methods for qualitative and quantitative reasoning.

1 INTRODUCTION

In computer science, ontologies are a technique or

technology used to represent and share knowledge

about a domain by modelling the concepts in that

domain and the relationships between those concepts

(Gruber, 1991). These relationships describe the

properties of those concepts; in essence, what it is to

be one of those concepts in the domain being

modelled. Ontology represents a conceptualization

of reality or simply reality. Ontology often resorts to

various tools of formalization and representation,

which taken independently do not lead to the

anticipated results. Today, the number of tools for

developing ontologies has been increased and

diversified. Increasingly, the construction of

ontologies is an area of ongoing research. Today,

there are a number of models and tools for

developing ontologies. We assessed diverse models

that were developed in the fields of software

engineering, business, and biology, to evaluate their

suitability for representing and simulating domain

processes. Based on this assessment, we propose an

OPN-Ont model that should be mathematically

based to allow verification of properties that are

desirable in biological system, and simulation of

system behaviour. So, we have combined the best

aspects of two models ‘PN and ontology model’ and

we have developed OPN-Ont (Objects Petri Nets

Ontology). This last has an interactive graphical

interface based on Object PN. The illustration of our

model is made in biologic process where PNs can

represent nesting and ordering of biologic processes,

the structural components that participate in the

process and the roles that they play. OPN-Ont not

only represents hierarchical process knowledge in

biology (which is a major challenge for

bioinformatics) and structure components but it

composed queries to discover relationships among

processes and structural components. We used PNs

analysis to answer queries about the dynamic aspects

of the model. OPN-Ont is tested by representing

OntoCell (Dib, 2005), and composed queries to

discover relations among processes and structural

components. We used reachability analysis to

answer queries about the dynamic aspects of the

model.

2 RELATED WORK

There are many formalisms and tools to edit, browse

158

Dib L. and Bousetouane F..

OPN-Ont: Object Petri Nets Ontology Tool.

DOI: 10.5220/0004085301580163

In Proceedings of the 2nd International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH-2012),

pages 158-163

ISBN: 978-989-8565-20-4

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

and map ontologies and a few comparative studies

of ontology tools have been performed. These tools

assist users in developing and maintaining

ontologies as: Workflow used by Peleg to develop

ontology fo biological processes (Peleg, 2002).

Other tools are developed to creat, edit and brows

ontologies such as: Protégé-2000; Ontolingua that

shows the concepts in a two dimensional tree

visualisation (Rice, 1996); Chimaera (Chimaera

Software Description URL, 2004) which is a web-

based ontology system and built on top of the

Ontolingua Distributed Collaborative Ontology

Environment; OilEd which uses the FaCT system

(The FaCT System URL, 2004) a description logic

system for checking the consequences of the

statements in the ontology and which has various

types of tabs where each tab shows information on

the current ontology component (Habbouche, 2002).

Evaluation: A number of other ontology tools

have been developed and used in bioinformatics.

However, only few evaluations of ontology tools

using bio-ontologies have been performed. In

(Lambrix, 2003), Protégé, Chimaera, and OilEd

were evaluated with respect to criteria such as

functionality, data model learning and user interface.

So, they were evaluated as ontology development

tools using GO ontologies as test ontologies. In

(Lambrix, 2004) an extension of this evaluation is

found, where Protégé-2000 with Chimaera were

evaluated against as ontology merging tools. In

(Dragan, 2006) the specific graphical user interface

provides graphical tools for all PN concepts and in

addition, the PN ontology is represented in RDFS,

and concrete PN models are represented in RDF.

However this solution covers only Time PNs, and no

other kinds of PNs. It neither defines PN structuring

mechanisms, nor provides precise constraints.

Finally, it does not enable using other ontology

languages for representing the PN ontology. From

this evaluation, no system is preferred but each

system has its own strengths and weaknesses. Based

on this assessment and on the solution proposed by

Perleg in (Pelegl, 2002), we combined ontology, PN

and a biological concept model and developed an

interactive graphical knowledge model, OPN-Ont,

tested in biological processes that supports methods

for qualitative and quantitative reasoning.

3 OPN-Ont

OPN-Ont model allows ontologies to be created and

explored. It is a computer application for data

organization and analysis. The OPN-Ont tool can

represent nesting and ordering of processes, the

structural components that participate in the

processes, and the roles that they play. It has an

interactive graphical interface based on high-level

PNs an extention of a PNs formalism. So, it maps to

PNs which is a graph-theoretical model that allow

verification of formal properties and qualitative

simulation. OPN-Ont tries to interpret all the

changes and states of the ontology being built or

operating. The user can interact with the system

using menus and graphical representation of

concepts and their relationships (in form of objects

PNs). Ontology in OPN-Ont system is operating by

updates and requests-answers trough queries. The

ontology is primarily an evolutes tool and the

updates must be performed periodically to adapt it to

its ontological function.

4 OPERATING SYSTEM

OPN-Ont

OPN-Ont provides both a net-based and a node-

based view of an ontology, where the latter displays

the selected concept and its entire environment

(definition, parents, children, other concepts linked a

domain relationship). So it is not limited to only

hierarchical link, is-a or part-of, however, the user

can hide links if they choose to. From the main

functions menu, the user can choose to:

Open/Creat/Save/Queries a ontology or Exit the

system. Once Open or New is chosen, the user could

introduce all the information (concepts and relations

that connect thems) collected and required for the

construction of the new ontology or the enrichment

of an existing one. At this level (Open/New), the

updates could be through a menu (Figure 1).

A-Consult: The system allows user to browse or

explore the ontology moving from one concept to

another. The marked place will be displayed with its

name and its entire environment : generic/specific

concepts and concepts that are linked by a domaine

relationship (Figure2).

The marked (current) concept can has equivalent

terms (called not-concepts or synonym concepts)

viewed in a sorted alphabetical list. As the not-

concepts are only linked to the current-concept and

can not referred to other concepts therefore their

representation in the PNs form is not essential and a

list of their names is more than sufficient. Only at

this level changes in the equivalent concepts can be

deleted, renamed or canceled.

b-Create: Through the creation menu the user

must specify if the relationship is equivalent,

specific, generic, or a domaine link. In the case of:

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(a) generic and associative to a domaine links, the

name of the concept that the relation will be

established, should be included in the ontological

base, that means it must have at least one or more

generic links with one or more concepts (Figure 3,

Figure 4). (b) the specific link, the name of the

concept which will be linking may exist or not in the

ontology. If this name does not exist, a control

ensuring its no duplication is activated. At this stage,

the new concept can be added to the ontology

(Figure 5). (c) if the introduced name is a not-

concepts in the ontology, the system return

automatically to the concept which it is equivalent.

(d) the creation of a not-concept, the system control

ensuring the non membership of this term to the list

of concepts or to the list of not-concepts. To add a

new link the system always ensures it has not been

duplicated. Thus, there will be a base of information

that respects the principles of ontology

establishment.

Concept

(Marked place)

Concept Menu

Figure 1: Concept menu: Menu of possible operations

associated to a marked place.

Figure 2: Consultation of the current-concept.

Figure 3: Creation of a generic relationship between

‘Molecule’ and ‘Factor’ concepts in OntoCell ontology.

Figure 4: Creation of a domain relationship: Cell ‘secret’

Factor.

C-Renaming Concept or Relationship

Through the ‘Rename Menu’ the user can rename a

concept or a not-concept but it is necessary to avoid

redundancy. He can also renaming a domain

relationship among concepts.

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Figure 5: Creation of a specific relationship: Creation of

‘Cell & Molecule’ concepts in OntoCell ontology.

D-Delete: Through the ‘Delete Menu’ the user can

delete a concept previously introduced (error during

the introduction of the concept, ...). After

confirmation, the system removes all links that relate

the concept on cause to other ones Figure 6.

If further, one of specific concepts (concept2) to

the removed concept (concept1) was not attached to

any other concept, the elimination of concept1 will

cause automatically the deletion of concept2 if the

user wants this, otherwise he will relate concept2 to

another existing concept in the ontology to avoid

any loss of information. This principle will be

applied to specific concept of concept2 and so on

until to the last specific one. We note that deleting

only a relation (with a specific (Figure 7a), generic

(Figure 7b), or concept linked by a domain

relationship (Figure 7c), between concept2 and

concept1 and not a related concept, is possible by

selecting the relation in cause and choosing the

Delete option. The same verification process

described above will be followed if concept2 is not

connected to any other concept that concept1.

Figure 6: The result after deletion of a concept.

3 - Requests: Once the ontology is built, queries

about its contents may be made. Once the user

describes this request by entering the operators (OR,

AND, EXCEPT) and their inputs, the system uses

the appropriate processing with the possibility of

combining them. The user structures his query as a

PN form starting with the most general operator of

the query to the more specific one.

a: Delete a specific link.

b: Delete a generic link.

c: Delete a domain link.

Figure 7: Examples of relationship delation.

5 RESULTS AND DISCUSSION

Results: A biological OPN-Ont Example

The example represent OntoCell ontology (Dib,

2005). Due to lack of space we only show some

concepts of OntoCell diagrams. In the first part of

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this example we represent OntoCell Concepts.

Initially, we represent two concepts (Cell and

Molecule) of the first level of OntoCell (Figure 8 a).

(a)

(b)

(c)

(d)

Figure 8: Some concepts in OntoCell ontology.

(e)

Figure 8: Some concepts in OntoCell ontology(cont.).

After, we represent the second level, where we

represent for example the specific (black transition)

and associative to a domain (red transtion) links of

Cell concept (Figure 8 b,c) and specific Molecule

concept (Figure 8 d). At last, in the third level, we

represent for example the specific concepts of the

concept Factor (Figure 8 e). Factor is linked to

Molecule by a specific relation and to Cell by the

role ‘Secret’.

In the second part we identify queries. The

results of the three first queries are given by Figure

9: Case a, shows the result generated by the query

‘Cell AND Molecule’. Case b, shows the result

generated by the query ‘Cell OR Molecule’. Case c,

shows the result generated by the query ‘Cell

EXCEPT Molecule’.

Discussion

OPN-Ont allows Ontologies to be created and

explored. Places represent concepts, and transitions

represent relationships (hierarchical links or

activities). There are many benefits to use PNs: they

have a firm mathematical foundation and they

explicitly represent states, which allows for the

modeling of milestones and implicit choices.

Another benefit is that Hierarchical PNs can control

the complexity of the representation of biological

systems. And last, Colored PNs can define states and

transitions and dynamical behaviours of the systems

are indicated by distributions of tokens changed

progressively along individual fulfilments of

conditions at places and succeeding firing the events

at transitions.

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Response of query: 'Cell AND Molecule'.

Response of query: 'Cell OR Molecule'.

Response of query: ’Cell EXCEPT Molecule'.

Figure 9: OntoCell in OPN-Ont: functional role, biological

reactions, biological process.

6 CONCLUSIONS

The aim of OPN-Ont tool is to allow experts, each in

its area, to construct and operate their ontologies.

The graphics offers the user ease of handling and

understanding the behaviour of the system by the

various commands he sends. The search for

information thus becomes easy, either by direct

access of the system to the information requested

through requests or by exploring the ontology while

navigating the network that represents it. PNs were

chosen to model ontology; indeed, knowledge is

clearly represented and easily identified. Also, the

dynamic aspect will be present in the activity of

conceptual knowledge and in requests where the

evolution of marks in places facilitate for the system

the search of the requested information, especially in

the search for intersection among two distant

concepts in the network but that have temporarily

dynamic link (with operators) which disappears after

reply to the request.

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(a)

(c)

(b)

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