Created by Hevok on Dec. 18, 2012, 3:08 a.m.
Ontological Engineering consists of Ontology Management Activities they contain Scheduling, Control and Quality Assurance. On the other side one has Ontology Support Activities which have the Knowledge Acquisition as the most important thing and Evaluation, Documentation, Integration, Merging, Alignment and Configuration Management. Then there are Ontology Development Orientated Activities which contains a Pre-Development Phase with an Environment Study and Feasibility Study. During Development Phase Specification, Conceptualization, Formalization and Implementation take place. In the Post-Development Phase there is Maintenance, Use and Reuse.
Only comparison is not enough sometimes one need to combine Ontologies. Especially if one wants to try to reuse existing Ontologies and want to adapt them to a new Problem, then one applies Ontology Merging.
Then there are methods to support the Ontological Engineering like learning new Ontologies from a given set of already available resources (Ontology Learning) or the population of existing Ontologies where one has only Classes and Relations with Individuals that come from other Information Resources for example Text Documents.
Ontology Engineering shall be very
intuitive and as
simple as possible, but at the same time
allow deep sophistication. One way to approach this would be to enable the
simple creation of Triplicates with an
attractive interface and then allow, if desired, to
refine such Associations in greater detail. Optimally, the Crowd needs to define what suits them the best.
It deals with what is real in the World. Therefore, the basic Question is what does really exists and what can be said to exist? It is a question of general Metaphysics in Philosophy. It is in contrast to the Epistemology that only deals with things of our perceptions, so what we see, what we hear and so on. Often our perceptions are betraying us. We can only experience the world with out perception but sometimes the perceptions might betray us so one has to know what is real in the world, i.e. what is True. To define what is really True and independent of our Perception is what Ontology original was intended to define.
An Ontology is an
explicit, formal specification of a shared conceptualization. The Term is borrowed from Philosophy, where an Ontology is a systematic account of Existence. For Artificial Intelligence Systems, what "exists" is that which can be represented.
A Conceptualization is nothing else than a Model. One tries to form a model about a domain one is talking about. Inside this domain one tries to identify relevant Concepts and how this Concepts are related to each other. This model (i.e. the conceptualization) has to be explicit which means all Meanings of all Concepts has to be defined, nothing has to be left out. Everything need to be defined. This Definition must be formal, which means it must be understood by the Machine, i.e. it has to be Machine-Understandable, not only Machine-Readable but must be interpreted correctly. Only if you read it and interpret it correctly means that you understand it. One of the most important thing is that the things one is referring to must be shared among communication partner, so this model of conceptualization must be a shared conceptualization, there must be consensus about the Ontology. This is required otherwise one can not communicate.
For Communication the Semantic Triangle applies. In Language on has a Symbol that stands for a certain Object. However language is ambiguous a term might have multiple Meanings. One can only communicate with other if two or ore communication partners apply a shared Concept (i.e. the same Concept). Then communication and understanding is possible.
Ontology is the most critical enabling Technology in Semantic Web Applications. Basically an Ontology describes Terms, and Types of Relationships between Pairs of Terms. In such an Ontology can be expressed/represented by a List of Tuples in the form of (
term y). For instance,
Normally an Ontology is developed by a small Group of Experts. However, this Approach does not scale with the ever increasing amount of Information. Specifically Experts have difficulty keeping up with Advances in Knowledge in the open dynamic World Wide Web Environment. Crowd Sourcing has the potential to be the most influential way to solve the problem of Ontology Development, by outsourcing a Task traditionally done by Experts to also non-Experts (typically a large Group of People) in the form of an open call (
The Call of Duty).
An expression is correct if the majority of the users agree on it.
An Ontology is a Data Model that represents a Domain and is utilized to reason both the Object in this Domain and the Relations between them. The application of Ontologies includes Artificial Intelligence, Semantic Web, Software Engineering and Information Architecture, where it is used as a form of Knowledge Representation about the World. An Ontology can also be understand as a set of Definitions of a formal Vocabulary with a huge potential in Information Technology.
Ontology is a Semantic skeleton of a Domain, i.e. it defines what we can have in Annotations. It defines Categories, Properties, Rules, etc. We can have many Ontologies to describe one Domain. It is only an Idea. We are not there.