Ontology Design Task

AKA: Ontological Engineering, Ontology Modeling.
Context:
- Task Input: Corpus, Website or Text Document.
- Task Output: Ontology.
- Task Requiremets: It requires to solve the following sub-tasks:
  - an Ontology Management Task,
  - an Ontology Learning Task,
  - an Ontology Validation Task.
- It can involve Create, Read, Update and Delete an Ontology.
- It can involve Create, Read, Update and Delete an Ontology Concept.
- It can involve Create, Read, Update and Delete an Ontology Concept Type.
- It can involve Create, Read, Update and Delete an Ontology Relation.
- It can involve Create, Read, Update and Delete an Ontology Relation Instance.
- It can be solved by an Ontology Design System.
- It can (typically) be performed by an Ontology Engineer (with ontology engineering skills).
- It can (typically) be followed by an Ontology Population Task.
- It can range from being a Heuristic Ontology Design Task to being a Data-Driven Ontology Design Task.
- It can be a Labor Intensive Task.
- It can be a part of an Ontology Authoring Task.
- ...
Example(s):
Counter-Example(s):
- a Business Process Engineering Task,
- a Ontology Population System,
- a Sentiment Analysis System,
- an Opinion Mining System,
- a Data Model Design System,
- a Knowledge Base Design System.
See: Ontology, Natural Language Processing Task, Knowledge Base, Database, Annotated Text Corpus, Semantic Web.

References

(Wikipedia, 2017) ⇒ https://en.wikipedia.org/wiki/ontology_engineering Retrieved:2017-5-9.
- Ontology engineering in computer science and information science is a field which studies the methods and methodologies for building ontologies: formal representations of a set of concepts within a domain and the relationships between those concepts.
  A large-scale representation of abstract concepts such as actions, time, physical objects and beliefs would be an example of ontological engineering. ^[1] Ontology engineering is one of the areas of applied ontology, and can be seen as an application of philosophical ontology. Core ideas and objectives of ontology engineering are also central in conceptual modeling.

(Navigli, 2015) ⇒ Roberto Navigli. (2015). “Ontologies.” In: Reference Book Journal.
- QUOTE: This chapter is about ontologies, that is, knowledge models of a domain of interest. We introduce ontologies, view them from the perspective of several fields of knowledge, and present existing ontologies and the different tasks of ontology building, learning, matching, mapping and merging.

(Rector et al., 2004) ⇒ Alan Rector, Natasha Noy, Holger Knublauch, Guus Schreiber, and Mark Musen. (2004). “Ontology Design Patterns and Problems: Practical Ontology Engineering using Protege-OWL." Tutorial at the Third International Semantic Web Conference (ISWC 2004).

(Gómez-Pérez et al., 2004) ⇒ Asunción Gómez-Pérez, Mariano Fernández-López, and Oscar Corcho. (2004). “Ontological Engineering. Springer. ISBN:1852335513 http://webode.dia.fi.upm.es/ontologicalengineering/

(Corcho et al., 2003) ⇒ Oscar Corcho, Mariano Fernández-López, and Asunción Gómez-Pérez. (2003). “Methodologies, Tools and Languages for Building Ontologies. Where is their meeting point?.” In: Data & Knowledge Engineering Journal, 46(1). doi:10.1016/S0169-023X(02)00195-7
(Jarra et al., 2003) ⇒ Mustafa Jarrar, Jan Demey, Robert Meersman. (2003). “On Using Conceptual Data Modeling for Ontology Engineering.” In: Journal on Data Semantics, 1.