Ontology Development Life Cycle: A Review
Siti Nurulain Mohd Rum1, Lilly Suriani Affendey.2and RazaliYaakob.3 Faculty of Computer Science & Information Technology1, 2,3
Universiti Putra Malaysia, 43400 Serdang, Selangor
Abstract. It is widely known that ontology plays a major role in semantic web as machine understandable language.
There are manymethodologies have been developed in the last two decades. However, the degree of maturity and acceptance of these methodologies is still lacking due to the insufficient information about the techniques employed in them. Different methodologies provide different notions of development life cycle.Some methodologies can be manually created orsemi-automatically created. Some methodologies areapplication-driven orapplication independent. Some methodologies followan iterative development life cycle and some are not.Their application in real project is comparatively limited despites of their growing number. This study presents the similarities and differences of some prominent methodologies based on six phases of the ontology development life cycle, namelyscope definition, capturing, encoding, integration, evaluation and documentation.Another important aspect of ontology development is the ontology evolution. This topic is also presented in this paper.
Keywords: Ontology; Ontology development life cycle;Ontology Methodologies; Ontology Evolution.
I. INTRODUCTION
In the last two decades, ontologies are widely used in various domains such as engineering [1], artificial intelligent [2], natural language processing [3], e-commerce [4], bio-informatics [5, 6], information retrieval [7, 8], library information and so on. There are different types of methodologies for building an ontology. In general, it can be categorized into three types of models, (1) evolving prototype model, (2)a stage-based model and (3) guidelines-based model [9]. These methodologies support collaborative development, reusabilityand interoperability. However, some of the methodologies are application independent in nature that possibly depending on the development model approach [9]. Methodologyplays an important part for the ontology development.Itprovides a set of standards for the development process that covered the principles, methods, and tools for the creation and maintenance. Staab et al. [10] pointed out that methodology need to provideend-to-end development’s life cycle from requirement specification up to the implementation phase. To date, there are many attempts have been madeto introduce a new methodology from different group of researchers under different projects. These methodologies have been either developed from scratch or emerged from existing ontology acrossdomains. Some are used as a guideline, some are used for other purposes such as ontology learning [11, 12], ontology engineering [13], ontology evaluation [14] , ontology evolution [15, 16], ontology merging [17, 18].
The one that developed by Uschold [19] is the fundamental approach for building ontologies. The idea is resulted from the construction of the Enterprise Ontology. It consists of four steps, firstly, objectives identification, secondly,ontology development, thirdly, ontology evaluation and lastly is ontology documentation. It becomes the underlying concept for many existing methodologies that have been emerging over the last couple of years. Both methodologies, Enterprise Ontology [19] and Toronto Virtual Enterprise (TOVE) [20] are in the field of enterprise modelling that later refined by [21]. The TOVE project was initiated by Fox et al[22] at the University of Toronto. The main objectives of TOVE are to create an knowledge understanding ofthe ontology through the sharing of agents in the distributed enterprise, to determine the purpose of each semantics through a set of axioms (that provide information for many general questions), and lastly, to express the conceptin the form of agraphicalcontext.
CommonKADs [23] is widely used in knowledge management for theenterprise solution and pioneeredin structured knowledge-engineering. It provides the methods to perform analysis using the knowledge-intensive approach. The KACTUS [24]
methodology is driven by application approach.It emphasised on ontology redesign, reusability and modular design approach[25].
The ontology that represents the knowledge is built during application development. Based on the literature, most of the methodologies are insufficient in terms of information aboutthe activities and techniques employed in them. However, METHONTOLOGY [26] providesmore information about itsactivities and techniques.This methodological approach allows the ontology to bebuilt at the knowledge level. It supports activities for both approaches; for new ontology development or for the enhancement of the existing one. Some examples of ontologies built based on this methodology are presented in [27-29].
METHONTOLOGY, IDEF5 [30]are the examples of evolving prototype methodology, in which the application of these methodologies are independent in nature. Some example of ontologies built using IDEF5 are [31] and [32]. The IDEF5 methodology consists of five main activities; firstly, project identification (e.g, establishing the objective, perspective, and project’s context for development of the ontology, as well as determining roles for each team member), secondly, data collection
which involves the process of data acquisition for the ontology development, thirdly, analyse data; to facilitate the ontology extraction, fourthly, the development of preliminary ontology from the data acquisition, and lastly the refinement and verification of the developed ontology. Although there are many methodologies supporting the ontology engineering and development, still the field lacks of the accepted paradigms due to the different notions and perceptions in the knowledge representation. This paper discussed the similarities and the differences between methodologies based on six general processes of ontology development that covered the ontology scope definition process, capturing process, encoding process, integration process, evaluation and documentation process.
II. ONTOLOGY SCOPE DEFINITION
The objective of this stage is to identifythe scope and specification of the ontology. During this stage, the ontology developer needs tomake a decision of whether to develop a new ontology or to just reuse theavailable ontology. The range of users is identified during this stage and a set of questions that relevant to the domain are constructed. These questions should be able to be addressed by the ontology. The main objective of this activity isto ensure that the developed ontology is aligned with the scope andspecifications of the domain. It is very important to have a clear picture of why it is developed [19]. In the specification phase ofMETHONTOLOGY [26], the activities and techniques used are detailed in more specific. It’ssupport the ontologies development by followingthe ontology development life cycle notions. The ontology specification is generated during this stage.It can be formal, informal and semi-formal. The purpose, scope and the set of terms were described during this stage. In ontology development, the knowledge acquisition process(e.g. from books, experts, handbooks, table, figures and even other available ontologies are considered as sources of knowledge) is an independent activity. In IDEF5 [30], the domain experts were interviewed and sample of data (e.g. input, control, policies, knowledge) are collected. Noy et al. [33] suggested that the basic questions about the domain should be able to be addressed by the ontology.For example, what should be covered by the ontology?,who are the users ?, and who will do the maintenance of the ontology?.The answers to these questions may change from time to timethroughout the development activity [33].In the Enterprise Ontology project, Uschold [20] presented a unified methodology in which, the scope and specification of the ontology are defined after the capturing process. The motivating scenario(s) facilitate the generation of scope and specification. The questions generated from this actitivy is called informal competency questions. LikewiseKACTUS [24] methodology, the motivating scenario is identified first and ends with scope specification theorems.
III. ONTOLOGY CAPTURING
In this stage, the relationships between objects andtheirkey conceptsof thedomainis identified. The definition to describe the relationship between the classes and subclasses will also develop during this stage.The Noy and McGuiness [33]suggestedthat the ontology developer should consider on what has been done by others.The reuse of existing ontology can be worthwhile, especially when there is the needed to integrate with an application that has already been compiled using specific ontology. The list of applicable terms is alsocreated during this stage. The relations of class hierarchy is indicated using the “is-a” standard of terms. The siblings should be in the same generality level and cycle must be avoided. The terms and the properties of the classesare specified together with their relations.
In METHONTOLOGY [26], the domain knowledge is represented in structural models. This structural model consists of problem description as well as its solving method identified during specification. A Glossary of Terms (GT)thatconsists of concepts, verbs, properties, and instances of the ontology will be developed. The terms are thengroupedinto verbs and concepts.
The grouping process involves the activity of building the treeof classificationto represent the relationshipsbetween concepts.Gomez [34] provides guidelines for ontology designers to build concept classification. In TOVE project, the motivating factorfor the development of ontology is derived froma sequence of events thatevolvedduringthe application development[20].
The possible solutions for every problem should be able to be presented by the developed ontology. The solutions may be in the form of an informal semantic that consists of relations and objects.The competency questions are used as a tool for validation checking purpose to ensure the developed ontology is aligned with the objectives.Another methodology which is independent in nature and evolves from prototype model is IDEF5 [30]. In this methodology, ontology is initially expressed in schematic language or by using graphic notation. The methodology developed by [19], invented from the idea and experience in the development of Enterprise Ontology (EO). The terms defined in the EO are semantic in nature that consist of objects and relations to describe the business enterprises. Uschold and King [19]are the first people who felt that there is the need to have a methodology for building ontologies. Back in the day, only guidelines were considered sufficient for this purpose. Though it was the first methodology proposed for ontology development, it does not specifically determine the techniques and activities [35]. In KACTUS [24], the preliminary design of an ontology is based on the conceptual knowledge of the domainthat consists of concept, attributes, and relations and the list of terms as well as the axioms were developed from views of the global model.
IV. ONTOLOGY ENCODING
This phase is regards to theselection of ontology representation language. There are many available representation languages, for example, DAML +OIL, Resource Description Framework (RDF), Simple HTML Ontology Extension (SHOE), Web Ontology Language (OWL) and RDF Schema (RDFS). The DAML+OIL is the combination of DAML and OIL language that later superseded by OWL. The RDF is a family of W3C that originally developed for data model of metadata data. In this paper, ontology encoding process refers to the process of coding the ontology uses any of the representation language as mentioned above. This process is done by developing a semantic pattern using authoring editor such as protégé, Ontolingua, WebOnto, DUET etc. The codification of ontology is the output produced in the form of formal language such us: LOOM, BACK, CLASSIC, Prolog, Ontolingua, or any ontology developer preferred language. Any ontology development environment (or known as an editor) should have at least a lexical and syntactic analyser to assure the portability of the specification and the definitions of other target languages (e.g. to modify, add, remove, search, inspect the ontology's library and definitions, to detect incompleteness, inconsistencies, evaluators,).
WebODE, for example, is a tool to support METHONTOLOGY. In methodology proposed by [19], the coding process involves representing knowledge in a formal language explicitly. The coding may be implemented during capture or coding process. The definition of terms and the specification of axioms are codified using the formal language of the ontology. In TOVE methodology, the completeness of the encoding process is established once competency questions have formally been answered.
Noy & McGuinness [33] build their methodology based on their experience using Protégé [36] and Chimaera [37] as the editing tools for ontology development. In IDEF5, the initial representation of the schematic language is later analyzed and transformed into structured language based on KIF [38]. However, there is no specific representation language and tool used to support the ontology described in most of the methodologies discussed above (TOVE and Enterprise Ontology project, KACTUS and IDEF5 methodologies).
V. ONTOLOGY INTEGRATION
Ontology development process is verytimeconsuming. One of the basic problems in semantic web development is the integration of ontologies. Some progress has been made by Skuce [39] in this area. They suggested that a lot of work must be done to ensure that the developed ontologycanbe shared by the communities. They alsosuggested that all assumptionsunderlying the ontology must be made explicit. Sometimes, when the terms were specified, the definitions are not provided in ontology. In this case, METHONTOLOGY [26], suggested that the ontology developers should place the missing definitions and states the advantages of such inclusion during ontology maintenance.
Ferandez [26]suggested that the developer must choose the meta-ontologies that better fit the concepts of the ontology. They also suggested thatthe new creation of meta-ontology is needed if the existing ontologies is not suitable with the concepts. Work by [33] in 101 methods, pointed out that the integration process that occurred in the early development life cycle should take into consideration the work that have been done by others in ontology development. There are many ontologies that are made available for adoption for customization thatcan better suit the environment development. The formalism expressed by ontology is usually not an issue since many systems (knowledge-representation) allow the import-export process of an ontology. In a situation when the knowledge-representation system cannot be integrated with a particular formalism, the translating process from one meta-ontology formalism to another can generally be done in many ways [40-43]. In the Enterprise Ontology [19], the process of integrating existing ontologies with other constructed ontology should be performed during either the capturing or coding processes or both. The KACTUS methodology follows an engineering approach, that emphasis on ontology redesign, modular design, and reuse [25].There is no specific activities identified in environment study,training and configuration management for methodology introduced by [20] in TOVE as well as in Enterprise Ontology[19].
VI. ONTOLOGY EVALUATION
To evaluate the ontology, a set of criteria need be fulfilled. The evaluation is categorized into two, the specific criteria, andthe generic criteria. In the specific criteria, the accuracy, completeness, clarity, conciseness, consistency and adaptability are evaluated in an ontology[29, 44, 45]. In the generic criteria, the developed ontology should be able to meet the objective as well as the user requirements. The evaluation process in the ontology development life cycle is the process to ensure that the developed ontology has met the requirement specifications in terms of its environment, software, and documentation. Verification and validation are two tasks involved in evaluation process. Verification refers to the process of ensuring the correctness of ontology in technically sound that associated with the environment of a software as well as its documentation. Validation in the valuation process to assure that the ontologies, software environment andthe documentationare aligned withthe required specifications.
Gomez-Perez [34] have presented a framework to evaluate theknowledge sharing technology in METHONTOLOGY in terms of ontologies, software, and documentation. Many documents related to evaluationgenerated from METHONTOLOGY described how evaluation processes of the ontology development were carried out, what are the techniques and the source employed for the
evaluation and what are the types of errors arise in each activity. Some detailed was done by[19] in Enterprise Ontology on the evaluation of ontologies and the approach was based on the experience of the development of the knowledge-based system (KBS). According to [19], software environment and the documentation of the developed ontology must be aligned with the frame of references (e.g. competency questions, requirement specification). In the[20] methodology, the newly axioms or object must be created until it is sufficient enough to answer the competency questions. This process is performed iteratively. Neither the activities nor the technique of ontology evaluation are described in this methodology. In IDEF5 [30], the refinement and validation of ontology were also done iteratively for the deductive validation procedure. Whereby in the KACTUS methodology [24], to reach at a more reliable design, ontology refinement need to be performed. However, there is very little information about how it is done. The principle of minimum coupling can be used to ensure that designed ontology is independent and as coherent as possible in order get maximum homogeneity[46].
VII. ONTOLOGY DOCUMENTATION
Ontology documentation is a very important phase that record and describe classes, properties and their relationship that relevant to the domain. It must be done with at most care and all assumptions must be recorded explicitly. There is no specific principle or set of standards for how to perform these tasks. In many cases, ‘codes of ontology’ are the only written documentation available which can be published in journals and conference proceedings for the ontology that are already built. To facilitate this process, METHONTOLOGY includes documentation as part of the activity that needs to be done throughout the whole process of ontology development.
If the ontology designer followed each step in this METHONTOLOGY, they will realize that each phase requiresa proper documentation for example, after the specification phase, the requirement specification report should be produced, after the knowledge acquisition stage, a report on knowledge acquisition should be produced together with the conceptual model that need to be documented after the conceptualization phase. Skuce [39] stated that all-important matters need to be properly documented for both information; about the ontology primary concepts together with the primitives employed to represent the meaning in the ontology (meta-ontology). For IDEF5, there is no specific phase of documentation, but this activity must be performed throughout the ontology development process from organizing and scoping phase, data collection phase, data analysis phase, initial creation phase and until the refinement as well as validation phase. There is no detailed description of documenting techniques specified in methodology developed by [19] and [20].
VIII. ONTOLOGY EVOLUTION
In the last few sections, we have discussed about the phases inthe ontology development life cycle, but how can we assure that the created ontology can be dynamically evolve over time. Stojanovic and motic[47] has termed ontology evolution as the ability of an ontology to consistently adapt, managed and propagate in a timely manner with the changes of business requirements in ontology-based applications. In an open distributed environments, ontology evolution allows knowledge expansion for every node created in an ontology [48]. This can be implemented by acquiring resource descriptions from nodes in the different ontologies.
Klein and Fensel [49] defines ontology versioning as the capability to cope with the changes or modification in ontologies, by developing or managing their differences. Any changes involved in components can be regarded as an ontology revision as defined by Heflif and Hendler [50]. Noy and Klein [51] defined the concept of ontology evolution and versioning as the capability of ontology not only to deal with the changes but also their impacts. They have also pointed out that ontologies have the capability to evolve over time due to its dynamic development approach that usually able to cope with the changes of rhythm o f the domain (new business rules, new concepts, etc.), the changes shared conceptualization and the changes of user requirements for keeping them up-to-date.
As suggested by Stojanovic and motic[47], there are a number of requirements on ontology evolution (implicit and explicit). The functional requirements and the details of changes must be provided during the modification process. The log of all changes made and related meta-information (author, time and etc.) must also be recorded. The inconsistencies in an ontology should be able to be tracked and identifiedin the correctional process. Different ontology editing tools provide different functionalities depends on it approaches and algorithms. Table 1 provides a summary of a some available tools for authoring
ontology .
TABLE 1. ONTOLOGY EDITING TOOLS EVALUATION APPROACH.
Editor Tool
Evolutionary Approach
Advantages Disadvantages
Protégé [52, 53]
Manual Provide the features for ontology creation and maintenance (Merging, integration) for evolution.
Need to use third party tools and services for ontology consistency measurement.
OntoEdit [54]
Strategy- based Provides the authoring services for ontology editing as well as provide features to build strategy.
Provide an editing environment for collaborative editing.
Involve the process of ontology engineering to avoid any side effects.
OilED [55] Semi-automatic Provides ontology engineering support for semi-automated ontology evolution.
No facility for recovery, changes, consistency checking for an ontology and strict in its operations.
KAON [55]
Strategy- based Provides authoring services for ontology editing as well as the features to build pre-evolution strategy and using the deduce changes approach to avoid conflicts.
Require ontology engineering process to address conflicts, slow in response and complex.
As suggested by [56], ontology change management addressed the problems that regard tothe modification process to dealwith any requirement changes. The ontology change management needto ensure that the changes of the ontology are always consistent. For an instance, a new business requirement may required the changes from concepts to properties, and this could affect the existing ontology. Depending on the resources that are affected, any changes in the ontology can be a straightforward processes or complex. This process can be facilitated if both types of requirements, either explicit or implicit are well understood[57, 58]. The changes that required the renaming a property or a class can be classified as a straightforward process, wherebyto connect these two hierarchies together with their constraints can be classified as a complex change process.
As described by Klein [59] there are several types of changes in an ontology that may be overlapped, the first one is changes at class level (i.e. creating, updating, deleting, and renaming classes). The second type of changes is at the slotlevel that involves the process of adding, deleting, renaming and updating different slots (i.e. setting the slots as inverse, functional, symmetry). The third type of changes involves the changes in ontology’s hierarchy that regards to the process of modification at the ontology structure level. These include merging, adding, moving, and deleting of different slots classes in ontology. The last type of changes is the changes at instance level that occurs during the creation, modification and deletion of instances.
IX. DISCUSSION
In this study, we have discussed the most representative methodologies over the years based on six general processes; ontology scope definition, ontology captures, ontology encodes, ontology integration, ontology evaluation and ontology documentation.
The ontology scope definition deal with the objective definitions [21]. Ontology capturing is the process of identifying relationships and the key concepts of the constructed ontology in particular domains. Ontology encoding process refers to coding the ontology. Ontology integration is the process of integrating the constructed ontology with the existing available ontology.
Ontology evaluation is the process of technically judging the constructed ontology and ontology documentation refers to the process of recording important information throughout the ontology development life cycle.
Apparently, from this study, the findings shows that developing methodologies for building ontology remains challenged. The literature also revealed that some of the available methodologies are derived from the experience of developing projects such as Enterprise Ontology and TOVE. In TOVE methodology, the processes of defining the ontology scope and capturing process were done concurrently. Eventually the undergoing project that employed this methodology aims to build a set of integrated semantic axioms (ontology) for modelling public or commercial enterprise. Likewise, Enterprise, in this methodology, the scope definition takes place after the capturing process. Although both methodologies actively referred to the literature, the techniques and activities involved were not provided in detail. The 101 Method and METHONTOLOGY are the two methodologies discussed in this study provide almost whole ontology development lifecycle ranging from ontology requirements specification to the implementation. METHONTOLOGY is among comprehensive methodology as it is one of building ontologies either from the process of re-engineering them; reusing available ontologies that already developed or constructs them from scratch.
Making an analogy, the development phases comprises in METHONTOLOGY is similar to the domain of manufacturing’s production chains, as the final product is the constructed ontology produced from production chain. The 101 Method developed by Natasha Noy from Stanford University is an iterative approached methodology designed to be application independent.
However, recommendation on the development life cycle seems to be absent, although this methodology covers some critical design issues. Whereby ontology development processes in IDEF5 require extensive di0scussions, iterations, introspection, and review. IDEF5 focused on an ontology that is constructed from the human engineered system. Nevertheless, the integration process did not mention anywhere in IDEF5 in ontology development process life-cycle. Most of the methodologies discussed in this paper are not equipped with sufficient information about activities together with the techniques employed in them. Command KADs and KACTUS are the two methodologies that support structured knowledge engineering, and both have three basic aspects; knowledge management, analysis, and system development. Table 2. Shows examples of applications of methodologies.
TABLE 2. SUMMARY OF ONTOLOGY DEVELOPMENT PROCESSES IN METHODOLOGIES.
Methodology Application example
Scope Definition
Capturing Process
Encoding Process
Integration Process
Evaluation Process
Documentation Process METHONTOLOGY Chemical ontology
[35], Legal ontology [60], DNS ontology [61]
Yes Yes.
Using Glossary of Terms (GT) to build the terms
Yes Yes Yes Yes
TOVE Enterprise ontology for public administration[62]
,Small and medium- sized ontology [63]
Processes are combined together and used motivational scenario for capturing process.
Not Specified
Yes Not
Specified
Not Specified
Common KADs and KACTUS
Healthcare ontology [64], Fraud detection in Medical Insurance [65], Haze management system [66]
Conditioned by application development
Not Specified
Yes Not
Specified
Not Specified
Enterprise Ontology Emergency Management in Hospital [67], Enterprise engineering [68]
Yes.
Defined after capturing process
Yes May
occurred during capturing process
Not Specified
Not Specified
Not Specified
IDEF5 Supply Chain
system [69], Enterprise process model [70]
Yes.
Defined using schematic language (a graphical notation).
Yes Yes Not
Specified
Yes Yes
101 Method Food ontology for diabetes [71], Ontology for legal concept [72]
Processes are combined together
Yes Yes Yes Yes
Ontology evolution plays another important rolein ontology development. This is to ensure that the developed ontology has the flexibility to cope with trend changes as well as the changes in business requirement. Changes can be from a very simple to complexdepending on how it will affect the resources. Two types of known evolution are ontology population andontology enrichment.In population evolution, instances of concept and relation are inserted into an existing ontology and does not change the structure. It involved the process data linking to various sources. Inontology enrichment, the additional concepts and semantic relations are extended into an existing ontology that caused the change the structure of an ontology. The more flexible the ontology, the better it deals with the changes of request, representation, implementation, and propagation due to the changes.
X. CONCLUSION
In this paper, we have discussed the differences and similarities between somemethodologies based on six phases,namelythe ontology scope definition, ontology captures, ontology encodes, ontology integration, ontology evaluation and documentation.
It can be concluded that most of the methodologies were designed based on experience of projects. Each has adifferent way of developing an ontology. Some methodologies considered the notion of reusability, some are not. Some of them are principally manually created, or semi-automatic. Some are application driven, some are application independent and some are scenario driven. Our finding shows that METHONTOLOGY is the methodology that supportsalmost all processes inthe ontology development life cycle. This methodologynot only support the development ofontology from scratch, but it is also supports the re-engineering development of ontologies. However, there is the lack of the details regarding the techniques and activities employed in them. The ontology evolution is another critical aspect that needto be considered by ontology developers to ensure the knowledge representation of the ontology can be evolved to stay consistent, rich and reliable. It is believed that methodology plays an important part in ontology development, therefore, it suggested that more exploration will be made for future work.
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