Ontology Engineering

3. A C B C B A Initial state Goal Conceptualization 1 Objects Relations block A On(X, Y) block B Clear(X) block C Holding(X) table A HandEmpty() hand A Conceptualization 2 Objects Relations block A On(X, Y) block B Clear(X) block C OnTable(X) Holding(X) HandEmpty()

4. Axiom：above(X,Z):-on(X,Y), on(Y,Z). IS-A

7. Knowledge Reorganization 8th 7th Structural and Semantic Transformation of Schemas 6th Standardization (of Terminology or of Tasks) 4th Answer Competence Questions Backbone Information for Using a Knowledgebase 3rd Conceptual Schema of a Relational Database 2nd Common Vocabulary for Communication 1st

9. Methodology Type of development Collaborative construction Reusability support Degree of application dependency Life cycle recommendation Strategies for identifying concepts Methodology details Interoperabili ty support Ushold and King’s Stage based No Yes Independent No Middle out strategy Some details No TOVE Stage based No Yes Semi independent No Middle out strategy Some details No METHONTOLOGY Evolving prototype No Yes Independent Yes Middle out strategy Sufficient details No On-To-Knowledge Evolving prototype No No Dependent Yes Middle out strategy Some details No UPON Evolving prototype No Yes Independent Yes Middle out strategy Some details No

10. Ethics Politics Art Metaphysics Logic Physics Zoology Matter Quality Quantity Relation Time Location

11. Ontology languages in the Semantic Web Architecture

12.

13. OBOL Open Bio-Ontology Language

14.

15. 15 Perdurantism الگوهاي طراحي فيلسوفانه Endurantism

Editor's Notes

Ontology (Ontological) Engineering in computer science and information science is a field which studies the tasks, methods, methodologies for build ontologies. Ontology engineering helps us design rationale of a knowledge base, kernel conceptualization of the world of interest, strict definition of basic meanings of basic concepts together with sophisticated theories, technologies, methods, tools, and languages enabling accumulation of knowledge through ontologies. Simply, the ultimate purpose of ontology engineering is to provide a basis of building ontologies of all things in which computer science is intereste
Eight Level of Ontological Engineering[2] Eight levels (from shallow to deep) of using ontologies can be defined. At level 1, ontologies are used as a common vocabulary for communication. At level 2, it is used as a conceptual schema of a relational data base. At the 3rd level, ontologies are used as backbone information for using a knowledge base. The remaining five levels are the levels where ontology engineering comes into play. Ontologies at the 4th level are used to answer competence questions and then they are used for standardization (of terminology or of tasks) at level 5. At level 6, ontologies are used for structural and semantic transformation of schemas. Reusing knowledge is done at the 7th level and knowledge reorganization is considered the 8th and highest level of using ontologies. Competency questions. One of the ways to determine the scope of the ontology is to sketch a list of questions that a knowledge base based on the ontology should be able to answer, competency questions (Gruninger and Fox 1995). These questions will serve as the litmus test later: Does the ontology contain enough information to answer these types of questions? Do the answers require a particular level of detail or representation of a particular area? These competency questions are just a sketch and do not need to be exhaustive. In the wine and food domain, the following are the possible competency questions: � Which wine characteristics should I consider when choosing a wine? � Is Bordeaux a red or white wine? � Does Cabernet Sauvignon go well with seafood? � What is the best choice of wine for grilled meat? � Which characteristics of a wine affect its appropriateness for a dish? � Does a bouquet or body of a specific wine change with vintage year? � What were good vintages for Napa Zinfandel? Judging from this list of questions, the ontology will include the information on various wine characteristics and wine types, vintage years—good and bad ones—classifications of foods that matter for choosing an appropriate wine, recommended combinations of wine and food.
Methodologies[4][5] Ontology development is not an easy task. It requires skills and is still an art rather than technology. People need a sophisticated methodology to help them develop an ontology. There are some methodologies available although ontology building methodologies are not matured enough and most of them are based on experience of one or few projects like the methodology by Ushold and King[6] and TOronto Virtual Enterprise (TOVE)[8] project ontology at University of Toronto. METHONTOLOGY[9] methodology was introduced for ontology engineering to build domain ontologies from scratch like chemicals[10]. On-To-Knowledge Methodology[11] was developed at Karlsruhe University based on a two-loop architecture: Knowledge process and knowledge meta process for introducing and maintaining ontology-based knowledge management. OntoEdit[12] is a support tool for this methodology. UPON[13][14] is another ontology development methodology derived from the Unified Software Development Process. Ontology development using UPON consists of cycles, phases, iterations and workflows, it follows the Unified Process paradigm. NeOn Methodology is the systematic, theoretical analysis of the methods applied to a field of study. It comprises the theoretical analysis of the body of methods and principles associated with a branch of knowledge. Typically, it encompasses concepts such as paradigm, theoretical model, phases and quantitative or qualitative techniques.[1] http://mycamerajournal.wordpress.com/tag/tool/
Comparison of methodologies based on the established criterion methodologies

Ontology Engineering

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Ontology Engineering

Editor's Notes