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Ontology. Parmenides was among the first to propose an ontological characterization of the fundamental nature of reality. Etymology[edit] While the etymology is Greek, the oldest extant record of the word itself, the New Latin form ontologia, appeared in 1606 in the work Ogdoas Scholastica by Jacob Lorhard (Lorhardus) and in 1613 in the Lexicon philosophicum by Rudolf Göckel (Goclenius). The first occurrence in English of ontology as recorded by the OED (Oxford English Dictionary, online edition, 2008) came in a work by Gideon Harvey (1636/7–1702): Archelogia philosophica nova; or, New principles of Philosophy. Containing Philosophy in general, Metaphysicks or Ontology, Dynamilogy or a Discourse of Power, Religio Philosophi or Natural Theology, Physicks or Natural philosophy, London, Thomson, 1663.[5] The word was first used in its Latin form by philosophers based on the Latin roots, which themselves are based on the Greek.

Overview[edit] Some fundamental questions[edit] Concepts[edit] Types[edit] Ontology (information science) In computer science and information science, an ontology formally represents knowledge as a hierarchy of concepts within a domain, using a shared vocabulary to denote the types, properties and interrelationships of those concepts.[1][2] Ontologies are the structural frameworks for organizing information and are used in artificial intelligence, the Semantic Web, systems engineering, software engineering, biomedical informatics, library science, enterprise bookmarking, and information architecture as a form of knowledge representation about the world or some part of it. The creation of domain ontologies is also fundamental to the definition and use of an enterprise architecture framework.

The term ontology has its origin in philosophy and has been applied in many different ways. The word element onto- comes from the Greek ὤν, ὄντος, ("being", "that which is"), present participle of the verb εἰμί ("be"). According to Gruber (1993): Common components of ontologies include: Formal ontology (Philosophy) By maintaining an independent view on reality a formal (upper level) ontology gains the following properties: indefinite expandability: the ontology remains consistent with increasing content.content and context independence: any kind of 'concept' can find its place.accommodate different levels of granularity.

Theories on how to conceptualize reality date back as far as Plato and Aristotle. Existing formal upper level ontologies (foundational ontologies)[edit] Common terms in formal (upper-level) ontologies[edit] The differences in terminology used between separate formal upper-level ontologies can be quite substantial, but most formal upper-level ontologies apply one foremost dichotomy: that between endurants and perdurants.

Endurant[edit] Also known as continuants, or in some cases as 'substance', endurants are those entities that can be observed-perceived as a complete concept, at no matter which given snapshot of time. Perdurant[edit] Qualities[edit] Formal versus nonformal[edit] Formal concept analysis. Formal concept analysis finds practical application in fields including data mining, text mining, machine learning, knowledge management, semantic web, software development, chemistry and biology. Overview and history[edit] Pairs of formal concepts may be partially ordered by the subset relation between their sets of objects, or equivalently by the superset relation between their sets of attributes.

This ordering results in a graded system of sub- and superconcepts, a concept hierarchy, which can be displayed as a line diagram. The family of these concepts obeys the mathematical axioms defining a lattice, and is called more formally a concept lattice. In French this is called a treillis de Galois (Galois lattice) because of the relation between the sets of concepts and attributes is a Galois connection. Motivation and philosophical background[edit] Formal Concept Analysis aims at the clarity of concepts according to Charles S. Contexts and concepts[edit] A' = {m ∈ M | ∀ g ∈ A (gIm)}, Conceptual clustering. Conceptual clustering is a machine learning paradigm for unsupervised classification developed mainly during the 1980s.

It is distinguished from ordinary data clustering by generating a concept description for each generated class. Most conceptual clustering methods are capable of generating hierarchical category structures; see Categorization for more information on hierarchy. Conceptual clustering is closely related to formal concept analysis, decision tree learning, and mixture model learning.

Conceptual clustering vs. data clustering[edit] List of published algorithms[edit] A fair number of algorithms have been proposed for conceptual clustering. More general discussions and reviews of conceptual clustering can be found in the following publications: Michalski (1980)Gennari, Langley, & Fisher (1989)Fisher & Pazzani (1991)Fisher & Langley (1986)Stepp & Michalski (1986) Example: A basic conceptual clustering algorithm[edit] Knowledge representation[edit] , the likelihood that it is male is. Categorization. There are many categorization theories and techniques. In a broader historical view, however, three general approaches to categorization may be identified: Classical categorizationConceptual clusteringPrototype theory The classical view[edit] The classical Aristotelian view claims that categories are discrete entities characterized by a set of properties which are shared by their members. According to the classical view, categories should be clearly defined, mutually exclusive and collectively exhaustive.

Conceptual clustering[edit] Conceptual clustering developed mainly during the 1980s, as a machine paradigm for unsupervised learning. Categorization tasks in which category labels are provided to the learner for certain objects are referred to as supervised classification, supervised learning, or concept learning. Conceptual clustering is closely related to fuzzy set theory, in which objects may belong to one or more groups, in varying degrees of fitness. Prototype Theory[edit]


Event partitioning. System context diagram for a fictitious hotel. (By convention, bidirectional flows, with arrows at both ends, are often used when a dialogue is initiated externally. For example, “booking dialogue” contains the flow “booking request”, which is the initial trigger; “booking confirmation”, the result, is sent back.) Event partitioning is an easy-to-apply systems analysis technique that helps the analyst organize requirements for large systems into a collection of smaller, simpler, minimally-connected, easier-to-understand ‘mini systems’ / use cases. Overview[edit] The Event partitioning approach is explained by Stephen M. McMenamin and John F. Palmer in Essential Systems Analysis.[1] A brief version of the approach is described in the article on Data Flow Diagrams. Event partitioning topics[edit] Actor → Event → Detect → Respond[edit] The method has the following steps. 1.

The technique was extended with ‘non-event’ events by Paul T. Data dictionary notation[edit] NB. See also[edit] System context diagram. Use case. A UMLUse Case Diagram for the interaction of a client (the actor) within a restaurant (the system) In systems engineering, use cases are used at a higher level than within software engineering, often representing missions or stakeholder goals. The detailed requirements may then be captured in Systems Modeling Language (SysML) or as contractual statements. Use Cases are an important requirement technique that have been widely used in modern software engineering since their formal introduction by Ivar Jacobson in 1992. Use case driven development is a key characteristic of process models and frameworks such as the Unified Process (UP), Rational Unified Process (RUP), and Oracle Unified Method (OUM). With its iterative and evolutionary nature, the use case is also a good fit for agile development.

History[edit] In 1986 Ivar Jacobson first formulated textual, structural, and visual modeling techniques for specifying use cases. Templates[edit] Martin Fowler[edit] Alistair Cockburn[edit] Use Case: General Formal Ontology. Webonset.