Knowledge modeling based on ontology for disaster warning information release technology of urban industrial disaster

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1 2016 International Conference on Network and Information Systems for Computers Knowledge modeling based on ontology for disaster warning information release technology of urban industrial disaster Huixin YUWEN School of mechanical engineering of Beijing Institute of Technology Beijing,China Xinmin QIAN School of mechanical engineering of Beijing Institute of Technology Beijing,China Xueqiu HE North China Institute Of Science And Technology Langfang,China Mengqi YUAN School of mechanical engineering of Beijing Institute of Technology Beijing,China Abstract: The author uses the concept about human disaster early warning which was defined by the United Nations International Disaster Reduction Strategy (ISDR),according to the present situation of urban industrial disaster early warning information released, the author uses the method of knowledge acquisition to obtain the relationship between the main concepts and concepts of the urban industrial disaster warning information release technology events. The authors use ontology modeling tool protégé to establish knowledge model of urban industrial disaster warning information publishing technology based on ontology, and studied the knowledge model. Research results show: the knowledge model is built to describe the semantic relations between knowledge points,conceptual relations are closely related, support for reasoning on semantic logic, strong expansibility. Key words: urban industrial disaster; early warning information release; ontology modeling; OWL; knowledge acquisition In recent years, China's urban industrial disasters is increasingly frequent,the destruction is increasingly serious situation. Research on disaster information dissemination has become a hot research direction of communication science. Ontology theory and technology development and application provides a new way for the research of emergency and special research in the field of emergency [1]. The ontology technology is applied to the urban industrial disaster early warning information release technology event, event object of the urban industrial disaster early warning information release technology was analyzed,to make clear the relationship between the concept and the concept of the urban industrial disaster early warning information release technology event, then create the urban industrial disaster warning information release technology event ontology model and inference and apply. It is of great significance to realize the sharing and reuse of the knowledge of the technology event of the urban industrial disaster early warning information release. At present, there is less research on the establishment of the technology event knowledge model of the urban industrial disaster early warning information based on ontology. Therefore, the author based the definition of human disaster warning which made by the United Nations International Disaster Reduction Strategy (ISDR), combined with the characteristics of cities and towns, the author obtains the main concepts and relationship between the concepts by using various channels and methods of knowledge acquisition,the concept is about the urban industrial disaster warning information release technology events, The author uses the ontology modeling tool protégé to establish knowledge model about the urban industrial disaster warning information release technology event. The model describes the semantic relations between knowledge points, support for reasoning on semantic logic,this can solve many problems such as the transformation from the knowledge of the reality to the form of encoding in order to the computer can underst. This makes it possible to reuse ontology. 1 ONTOLOGY RELATED BASIC THEORIES AND TOOLS Ontology is a formal specification of a shared conceptual model. It has the domain object description method and the description method, it can express the concept in a clear and consistent way, it can clearly describe the concepts and relations in the domain knowledge base, this will achieve knowledge sharing and reuse in the field. we can build the knowledge model by using ontology, it is more convenient to find the information, it is more easy to maintain model.the formal representation of the model also greatly improves the reliability of the model,this makes knowledge model all levels of the system organization structure more clear and perfect[2]. Perez [3] and others believe that the ontology can be organized according to the classification method, The ontology consists 5 basic modeling primitive: classes, relations, functions, axioms, instances, usually classes is also writed concepts /16 $ IEEE DOI /ICNISC

2 1.1 Classes or Concepts It can be used to describe any task, function, behavior, strategy, process, etc. From the semantic point of view, the class represents a collection of objects with the same features, which is defined by the set of relations with other classes, as well as the description of its natural language. Ontology modeling aims to provide semantic coordination mechanism within the event, this is the fundamental difference between the class in the ontology and the class in the object oriented programming. In object oriented model, the only external interface is message mode, in addition to vertical inheritance, there is no cross class relationship. Ontology model has a good semantic coordination mechanism, and there is a horizontal relationship between the class and the class. 1.2 Relations The relationship is an interaction between events within the concept of n-dimensional Cartesian product of a subset of the formal definition, that is: R C 1 C 2 C n.such as, part-of is relationship between the expression of the concept and the whole. 1.3 Functions Function is a kind of special relation.it can be expressed as: F C 1 C 2 C n-1 C n,it Indicates that the n'th elementis uniquely determined by the first n-1 element. 1.4 Axioms The axiom stands for truth. In certain events, the relations and constraints between the elements can be described by axioms. Axioms plays an important role in verifying the consistency and reasoning of ontology. 1.5 Instances Instances is the specific elements of the expression class, it is the object of semantic representation. Modeling primitive is not required to have functions and instances, which can be choice though the ontology modeling of the details. Due to the ontology project is still in a relatively immature stage, domain ontology construction is still in the exploratory stage, so there are still a lot of problems in the construction process. Compared with standard software development life cycle method for IEEE [IEEE96], there is not a kind of ontology construction method system is completely mature, various methods have different application and background, and also apply to different fields, the domestic scholar Li jing[4]:sorted the maturity of the construction method of the current ontology: Seven step >Methontology>IDEF-5> enterprise modeling method > skeleton method > loop acquisition method >KACTUS. Although the method of Constructing Ontology is different, in 1995, it is generally accepted that Gruber[5] proposed 5 rules clarity and objectivity, completeness, consistency, maximum monotonic scalability, minimum commitment. Ontology can be described by natural language, and it can be described by frame, semantic network or logic language. But to achieve a clear, formal concept description, ontology language should meet the following requirements [6]: well defined grammar, well defined semantics, valid reasoning support, full expressive power, and expressive convenience. In recent years, the birth of a lot of ontology language were birth after a large number of research units study, in accordance with the application of the field can be divided into three categories[7]:1)network related description language: RDF and RDFS, OIL, DAML, OWL, XOL, etc; 2) specific systems related to the language: Ontolingua, Cyel, LOOM, etc; 3) KIF which is a kind of exchange formathas become the language of American national standard, but it has not been widely used yet. Ontology description language is used to realize the sharing and exchange of information in the field of information technology, W3C has recommended the ontology description language for Web application[8]: RDF (S), OIL, DAML+OIL and OWL etc. These languages are based on XML. OWL[9] (Ontology Language Web) language is standard about language that W3C (World Wide Web Consortium) recommend ontology description language in Semantic Web, it is the semantic Web ontology markup language. W3C designers want to meet various needs o,then they developed 3 corresponding OWL language: Lite OWL, DL OWL and Full OWL, the expression ability of these languages is increasing. Ontology modeling tools provide strong support for ontology building process, it can simplify and optimize the whole modeling process. With the wide use of ontology, many ontology modeling tools have emerged. Choosing the most appropriate construction tool can ensure the efficiency and correctness of the ontology construction. It commonly used ontology modeling tools[10]: OILED, OntoEdit, WebOnto, Ontosaurus, Ontolingua, Protégé,etc. The author considers the characteristics of modeling tools (general, cooperative, and ease of use)[6,11-14],the author selected the protégé software developed by the medical information research group of Stanford University as the modeling tool in this paper, Protégé features: 1)the friendly development interface; 2) the extensible knowledge model allows the user to re define the original knowledge set; 3) with a powerful plug-in system and the opening of the modular style; 4)it can be customized to a variety of file output format, including XML, RDF (S), OIL, DAML, DAML+OII, OWL and other languages of the format; 5) there is a scalable architecture that can be used in combination with other applications, providing an extensible API interface, and the user interface can be customized; 6) it support the ontology model of database storage, improve the ontology of data storage capacity; 7) open source, and in addition to the function modules of the individual plug-in support chinese. 2 THE ESTABLISHMENT OF ONTOLOGY INSTRUCTION MODEL IN THE TECHNICAL FIELD OF THE EARLY WARNING INFORMATION RELEASE OF THE CITIES AND TOWNS The author uses ontology modeling language and modeling tool in order to establish ontology knowledge 288

3 model in the field of urban industrial disaster early warning information release technology, this is a clear and formal specification of the concept of the urban industrial disaster early warning information release technology event sharing. 2.1 The Concept for Urban Industrial Disaster Warning Information Release Technology Field Is Obtained First, we need to get the concept and define the class. This need to be fully understanded the organizational structure of knowledge through the collection of knowledge and information of urban industrial disaster warning information technology events, it need to get key concepts from the domain experts, professional authority books and periodicals, the network and the existing related ontology. This analysis abstract and generalize the concept, basis concept only, the same concept between disjoint and union coverage requirements of the scope of the parent class concept, this is a hierarchical model that is mainly related to the inheritance relationship Event layer The occurrence of unexpected events is very complex, often not only there is a type of emergency. Under normal circumstances, after the occurrence of the original event, there will be accompanied by secondary events and derivative events occur[4] Stage layer In this paper, crisis cycle is divided into five stages by the crisis management expert Mitroff. The first stage is signal detection: to identify the crisis warning signal and take preventive measures; The second phase is to detect and prevent: members of the organization are to search for known risk factors and try to reduce the potential damage; The third stage is to control the damage: the organization members strive to make it do not affect the organization of the operation of other parts or external environment; The fourth stage is the recovery phase: as soon as possible to allow the organization to operate normally; The fifth stage is the stage of learning: the organization members review and examine the crisis management measures taken, and make it become the basis for future operation. In the first and two phase, the government should release the warning information in a timely manner, causing public attention, so that the public should raise the alarm, full psychological preparation for disaster arrival, The third and fourth phase of crisis information are to become the focus of the audience, it is the continuing impact of the crisis stage, should be updated in a timely manner the latest developments in the disaster and the government is taking measures to deal with and other relevant information, master public information initiative, and improve the public's confidence in the government and encourage the public and government coordination and cooperation, as far as possible to reduce the disaster caused losses to local disaster recovery scientific and orderly control. In the fifth stage, we should sum up the success and deficiency of disaster response in a timely manner, and the success of the disaster should be improved, and the deficiencies should be improved Index layer The reason which causes the occurrence of unexpected events[15]: the first reason is that the external environment of the system changes, that is the environment vector; The second reason is caused by the accumulation of internal contradictions, that is, the state indicators; The third reason is caused by the combination of the former two factors. Emergency as a system, it follows the input and output state equation model, the external environment of the event system to impose an input, this causes changes in the internal state of the system, some of the changes in the external environment to make it happen chaos, disorder and imbalance, changes in the external environment will lead to changes in the state of the event. Based on the systematic understanding of the urban industrial disasters, the author attempts to put forward the technical scheme of the early warning information release of the urban industrial disaster based on the changes of the two. Index layer show the characteristics of the urban industrial disaster warning information released be at each stage through the environmental vector and the status indicators and the relationship between the two. The technical indexes of urban industrial disaster warning information release are classified into: human error, knowledge loss, natural environment, social environment, state and state control Resource layer We can obtain information from resource layer for upcoming events,it contain the basic information, monitoring information, resource information, decision-making information. Its purpose is to prepare for the early warning information release of the urban industrial disaster. In the identification of the urban industrial disaster warning signs, search for the early warning information resources, and then according to the focus of each stage of urban industrial disaster warning, staff select the appropriate warning information resources in accordance with the priority of the early warning of the urban industrial disaster in each stage of life to evaluate the feasibility of existing resources and measures. In addition, when there is no correlation between the information resources of the relevant early warning, or there is no corresponding early warning information resources, or case and early warning information resources are matched but not suitable for the early warning information resources, we can determine the specific problem from the relationship between the event and the environmental vector and the state index of each stage, adjustment and development of early warning information, and constantly modify the basic information, monitoring information, resource information, decision-making information resources though the system feedback, in order to achieve a better service for urban industrial disaster warning information release technology Information response layer When the urban industrial disaster event may happen or is about to occur, the government departments, the individual and the community of urban industrial disasters will be different activities.individuals who meet urban industrial disasters carry out disaster avoidance and self-help in the first time. When government face urban industrial disasters, the government must first conduct a 289

4 forecast and early warning, mainly for information collection, analysis and assessment and release;first publish information after the outbreak of the disaster: the disaster occurrence time, location, background, losses caused by the disaster, as well as the current development situation, has taken the relief measures, the needs of the affected areas and released the name of the agency etc., and after a period of time again released more than the content of information; once the disaster end, the government departments should do a good job of dealing with the aftermath, the work should have a variety of scientific and orderly. When social people face urban industrial disasters, first of all, should be the first time to take the initiative to cool from a variety of accessible media to collect information on the occurrence of urban industrial disaster warning information. Then the urban residents should actively cooperate with the government early warning information release work in the period of the urban industrial disaster. The concept hierarchy is the knowledge model framework of the urban industrial disaster early warning information release technology, and the relationship between the concept is the core of the event knowledge model. The technical representative concepts of the urban industrial disaster warning information release are shown in table 1, and the representative relationships between concepts are shown in table 2. TABLE 1 REPRESENTATIVE OF THE CONCEPT OF CLASS NAME OF THE CLASS MEANING EXPLANATION EVENT_LAYER IN THE EVENT GRANULARITY LEVEL EVENT CLASSES EMERGENCY EVENT TYPE PHASES_LAYER IN THE EVENT CYCLE STAGE STAGE STAGE CLASS SIZE EVENT PHASES TYPE INDEX_LAYER INDICATOR-BASED JUDGMENTS CLASS EVENTS INDICATORS TO DETERMINE THE TYPE RESOURCE_LAYER THE IMPLEMENTATION OF EARLY WARNING INFORMATION RELEASE REPRESENT CLASS COMPREHENSIVE INDICATES THE TYPE OF RESOURCE STATUS ACTION_LAYER THE IMPLEMENTATION OF SPECIFIC CLASSES OF EARLY WARNING INFORMATION RELEASE TECHNOLOGY SPECIFIC TYPES OF INFORMATION DISSEMINATION SIGNAL_DETECTION FOCUSING ON INFORMATION DISSEMINATION OBJECTS STAGE LAYER SUBCLASS LACK_OF_KNOWLEDGE POST IMPLEMENTATION FOCUS INDEX LAYER SUBCLASS BASIC_INFORMATION _POPULATION POST IMPLEMENTATION NEED TO MASTER RESOURCES RESOURCE SUBCLASSES LAYER INFORMATION_FAST_REPORTS IT IS ASSIGNED THE ROLE WHEN THE MESSAGE PROMULGATED INFORMATION LAYER SUBCLASS RESPOND TABLE 2 REPRESENTATIVE RELATIONSHIP PROPERTY DOMAIN RANGE IS_PHASE PHASE EVENT NEED INFORMATION RESPONSE RESOURCE IS-BEFORE SIGNAL DETECTION DETECTIONPREVENTION 290

5 DETECTIONPREVENTION DAMAGE CONTROL RECOVERY DAMAGE CONTROL RECOVERY STUDY CAUSE SECONDARYEVENT DERIVATIVEEVENT ORIGINALEVENT SECONDARYEVENT IS_FEATURE INDEX PHASE IS_KIND LACK OF KNOWLEDGE HUMAN ERROR SOCIAL SURROUNDING NATURAL CONDITION STATUS AND STATUS CONTROLLABILITY INDEX IS_PART SIGNAL DETECTION DETECTIONPREVENTION DAMAGE CONTROL RECOVERY STUDY PHASE USE INFORMATION PUBLISH MEDIA RESOURCES DYNAMICINFORMATION 2.2 It is established By Protégé For Ontology Knowledge Model Of Urban Industrial Disaster Warning Information Release Technology Event Protégé is an open source ontology editor developed by the medical information research group of Stanford University. The ontology structure is displayed though the tree hierarchy directory structure, You can clicking on the corresponding item to add or edit classes, subclasses, properties, instances, etc, event model design can be carried out at the conceptual level, and can be extended, the back-end support database storage, it is established which use protégé event knowledge model of urban industrial disaster warning information release technology, as shown in figure 1 and figure 2, the final model is shown in figure

6 Figure 1 based on the ontology of the urban industrial disaster early warning information release technology knowledge model concept level Figure 2 Technology based on ontology knowledge model for urban industrial disaster early warning information release of the attribute and relation set 292

7 Figure 3 The use of tools protégé to build the ontology based on the urban industrial disaster warning information technology knowledge model 293

8 When it is establish with protégé for event ontology model of the urban industrial disaster early warning information release technology, you can get a formal OWL file, which defines the basic concepts, relationships, and examples, this can be seen as the basic knowledge base of the emergency ontology, which can be used to implement semantic reasoning and retrieval. Some of the representative code fragments are as follows: <?xml version="1.0"?> <rdf:rdf xmlns:xsp=" p.owl#" xmlns=" 216.owl#" xmlns:swrlb=" xmlns:swrl=" xmlns:protege=" protege#" xmlns:rdf=" xmlns:xsd=" xmlns:rdfs=" xmlns:owl=" xml:base=" owl"> <owl:ontology rdf:about=""/> <owl:class rdf:id="detection_prevention"> <rdfs:subclassof> <owl:class rdf:id="phase"/> </rdfs:subclassof> <owl:class rdf:id="disaster_warning"/> <owl:class rdf:id="damage_control"> <rdfs:subclassof> <owl:class rdf:about="#phase"/> </rdfs:subclassof> <owl:class rdf:id="study"> <rdfs:subclassof> <owl:class rdf:about="#phase"/> </rdfs:subclassof> <owl:class> <owl:unionof rdf:parsetype="collection"> <owl:class rdf:about="#damage_control"/> <owl:class rdf:about="#detection_prevention"/> <owl:class rdf:id="recovery"/> <owl:class rdf:id="signal_detection"/> <owl:class rdf:about="#study"/> </owl:unionof> <owl:class rdf:about="#phase"> <rdfs:subclassof rdf:resource="#disaster_warning"/> <owl:class rdf:about="#recovery"> <rdfs:subclassof rdf:resource="#phase"/> <owl:class rdf:about="#signal_detection"> <rdfs:subclassof rdf:resource="#phase"/> <owl:objectproperty rdf:id="is-before"> <rdfs:domain> <owl:class> <owl:unionof rdf:parsetype="collection"> <owl:class rdf:about="#signal_detection"/> 294

9 <owl:class rdf:about="#detection_prevention"/> <owl:class rdf:about="#damage_control"/> <owl:class rdf:about="#recovery"/> </owl:unionof> </rdfs:domain> <rdfs:range> <owl:class> <owl:unionof rdf:parsetype="collection"> <owl:class rdf:about="#detection_prevention"/> whether it can support the reasoning of ontology in semantic logic. Scalability is to refer to the evolution of ontology, the ontology can be extended in the hierarchy, can be rich in semantics and perfect, can join the new term concept.compatibility is the openness and interoperability of ontology, ontology and other event ontology and related resources system mapping, including system layer, logical layer, semantic layer, presentation layer, such as compatibility and interoperability. In this paper, the author wants to make a comprehensive evaluation knowledge model which built with protégé based on the ontology of urban industrial disaster warning information. In combination with the actual situation of cities and towns, the author chooses the index to reflect the effect of urban industrial disaster warning information release. Select the relevant subject experts and ontology researchers as the object of investigation. The author uses software Yaahp to determine the weight of evaluation index, as shown in figure 4, 5, 6. The evaluation results are shown in table 3. <owl:class rdf:about="#damage_control"/> <owl:class rdf:about="#recovery"/> <owl:class rdf:about="#study"/> </owl:unionof> </rdfs:range> <rdf:type rdf:resource=" operty"/> Figure. 4 The hierarchical structure model of the urban industrial disaster early warning information release technology model </owl:objectproperty> </rdf:rdf> 3 EXPERTS COMPREHENSIVLY EVALUATED KNOWLEDGE MODEL THOUGH PROTÉGÉ,IT IS BUILD THAT IS BASED ONTOLOGY OF THE URBAN INDUSTRIAL DISASTER WARNING INFORMATION RELEASE TECHNOLOGY A good event ontology model should be complete, clear, consistent, scalable and compatible. Integrity refers to whether or not the ontology includes an important concept of the event, the concept and the relationship is complete, the concept of the hierarchy, whether the level of diversification. Clarity refers to whether or not the definition of knowledge is clear and unambiguous. Consistency refers to whether or not the relationship between concepts in ontology is strict and consistent, Figure 5 Urban industrial disaster warning information release technology model evaluation judgment matrix 295

10 Figure 6 Urban industrial disaster warning information release technology model evaluation software Yaahp calculation results TABLE 3 TOWN INDUSTRIAL DISASTER WARNING INFORMATION RELEASE BODY MODEL TO EVALUATE THE RESULTS OF THE TECHNICAL FIELD INDEX QUANTIZATION EXPERT RATING RESULTS CONSTRUCTION IS SCIENTIFIC AND RATIONAL WHETHER THE USE OF ADVANCED DEVELOPMENT TOOLS MEETS URBAN INDUSTRIAL DISASTER WARNING INFORMATION RELEASE UNIVERSAL LAW THE CONCEPT IS COMPLETE AND CLEAR WHETHER TO SUPPORT THE INFERENCE HOW EVOLUTION AND APPLICATION PROSPECTS THE AVERAGE SCORE Table 3 shows the results of the evaluation, In this paper, the urban industrial disaster early warning information release technology event ontology model contains the main concepts and concepts of the urban industrial disaster warning information release technology events, and the concept of the level of diversity, integrity is better. The definition of the concept is clear, clear, strict, no ambiguity, the relationship between concepts is logically consistent and consistent. Support for reasoning on semantic logic. Ontology can be extended to facilitate the addition of new concepts. Therefore, in this paper, the establishment of the urban industrial disaster warning information release technology event ontology model is available in practice. 4 CONCLUSIONS 1 The author study philosophy, cognitive psychology, logic and knowledge representation, the author systematically and scientifically study the technology of the 296

11 early warning information release of the urban industrial disaster, the author attempts to establish an ontology based knowledge model of urban industrial disaster early warning information release technology events. The author built the knowledge model which has a solid foundation and can meet all kinds of needs. The knowledge model is established not only represent the static knowledge but also describe the dynamic nature of knowledge. It can not only express the discrete event but also describe the continuous action. It described the determination of information and expressed the fuzzy knowledge. At the same time, it is main line that the urban industrial disaster warning information release technology event ontology knowledge model based on the hierarchical structure of the event class,reduce the complexity of the relationship. 2 It will provide support for the following research for the establishment of ontology based knowledge model of urban industrial disaster early warning information release technology event knowledge model: (1) reasoning based on the event ontology semantic logic; (2) the text categorization based on the event ontology; (3) the text understanding based on the event ontology; (4) the event ontology based text automatic text summarization; (5) query retrieval technology based on the event ontology; (6) the representation of common sense knowledge; (7) simulation; (8) decision analysis, etc. 3 It is still need to be further improved for the establishment of the event ontology knowledge model, such as the need to realize the automatic semantic annotation and query, that will meet the needs of the information needs of the staff results output and tagging, at the same time to avoid the disadvantages of complicated work by human settlement,it will to realize semantic retrieval for the urban industrial disaster warning information publishing technology ontology model Semantic Web. IEEE Intelligent Systems.vol.17,no.1,pp.54-60,Feb,2002. [9]. OWL Overview Recom endation. [10]. Guohu Xu and Fang Xu. A Comparative Study of Ontology-building Tools. LIBRARY AND INFORMATION SERVICE.vol.50,no.1,pp.44-48,Jan,2006. [11]. Michael Denny Ontology tools survey.[ ] ml [12]. Oscar Corcho, Mariano Fernández-López and Asunción Gómez-Pérez. Methodologies, tools and languages for building ontologies. Where is their meeting point?data & Knowledge Engineering.vol.46,no.02,pp.41-46,July,2003. [13]. Jing LI. Study on the Theory and Practice of Ontology and Ontology-based Agricultural Document Retrieval System-Floricultural Ontology Modeling.Beijing:Graduate University of Chinese Academy of Sciences,2004. [14]. X Su and L Ilebrekke. A comparative study of ontology languages and Tools.( ) pdf [15]. Li WEN. The Study of Ontology for Knowledge Model Based on Emergency Decision-making.Dalian:Dalian University of Technology REFERENCE [1]. Zhanhua YUAN.A study of ontology-back knowledge base modeling for emergency system. Taiyuan:Taiyuan University of Technology,2005 [2]. Kun WU.Research and Implementation of Ontology-based knowledge in the field of exchange.hangzhou: Zhengjiang University, [3]. AG Perez and VR Benjamins. Overview of Knowledge Sharing and Reuse Components: Ontologies and Problem-Solving Methods. Proceedings of the IJCAI-99 workshop on Ontologies and Problem-Solving Methods (KRR5) Stockholm, Sweden, August 2, 1999,pp [4]. Jing LI and Liansheng MENG.Comparison of Seven Approaches in Constructing Ontology.NEW TECHNOLOGY OF LIBRARY AND INFORMATION SERVICE.no.7,pp.17-22,July,2004. [5]. Thomas R. Gruber Towards Principles for the Design of Ontology Used for Knowledge Sharing.International Journal of Human-Computer Studise.Vol.43,Issues4-5,pp ,Nov,1995. [6]. M Denny.Oniology Building:A Survey of Editing Tools. [7]. Grigoris Antoniou and Frank van Harmelen.Web Ontology Language:OWL. in Staab S. and Studer R., Handbook on Ontologies in Information Systems, Springer-Verlag,vol.33,no.4,pp.67-92,July,2003. [8]. A Gomez-Perez and O Corcho. Ontology Languages for the 297