"In computer science, information science and systems engineering, ontology engineering is a field which studies the methods and methodologies for building ontologies, which encompasses a representation, formal naming and definition of the categories, properties and relations between the concepts, data and entities."
Overview of the different types of ontologies such as top-level ontologies, domain ontologies, and task-specific ontologies.
Basic principles of ontology: This topic covers the fundamental principles of ontology, including the essential concepts, the different types of ontologies, and their applications.
Top-level ontologies: This topic discusses the most common and popular top-level ontologies that are used in various domains, such as DOLCE, SUMO, BFO, and others.
Domain-specific ontologies: This topic covers the various types of domain-specific ontologies, how they are developed, and their applications. It also includes the development of ontology engineering methodologies.
Ontology languages: This topic discusses the different ontology languages such as OWL, RDF, and others used for ontology development.
Ontology modeling: This topic covers the various modeling techniques used in ontology development. It also includes the modeling tools and techniques, including UML, and their applications.
Ontology integration: This topic discusses the integration aspects of different ontologies, including the techniques used to integrate multiple ontologies to achieve interoperability.
Ontology evaluation: This topic covers the various methods used to evaluate the quality and effectiveness of ontologies.
Ontology mapping: This topic discusses the process of mapping between different ontology models and formats.
Ontology reasoning: This topic covers the principles of inference and reasoning on ontologies, including the use of rule-based reasoning and query-based reasoning techniques.
Semantic web: This topic discusses the conceptualization of the Semantic Web and its impact on ontology development, including the role of ontologies in Linked Data.
Ontology applications: This topic covers the various applications of ontologies in different domains, including e-commerce, healthcare, e-learning, and others.
Ontology maintenance: This topic covers the maintenance of ontologies, including updating, versioning, and end-user support.
Domain Ontology: A representation of the concepts, terms, and relationships within a specific domain or subject area, such as medicine or finance.
Upper Ontology: A formal representation of categories and concepts that are common across different domains, such as time, space, and causality.
Task Ontology: A formal representation of the tasks or activities involved in a particular domain or task, and the resources and methods needed to accomplish them.
Application Ontology: A formal representation of the entities and relationships involved in a specific software application or system, such as a database management system or an e-commerce platform.
Process Ontology: A formal representation of the procedures and workflows involved in a particular domain or activity, such as a manufacturing process or a healthcare treatment plan.
Meta-Ontology: A formal representation of the principles, rules, and conventions of ontology engineering itself, including methods of ontology development, ontology evaluation, and ontology reuse.
Social Ontology: A framework for understanding the social structures, interactions, and relationships between agents or entities within a particular domain, such as organizational structures or cultural practices.
Biomedical Ontology: A formal representation of the biomedical knowledge domain, including the concepts, relationships, and entities involved in biology, medicine, and healthcare.
Legal Ontology: A formal representation of the rules, regulations, and concepts involved in the legal domain, including legal concepts and relationships, and legal processes and procedures.
Linguistic Ontology: A formal representation of the concepts, relationships, and entities involved in language, including linguistic categories, phonetics, and syntax.
"Ontology engineering offers a direction towards solving the interoperability problems brought about by semantic obstacles, i.e. the obstacles related to the definitions of business terms and software classes."
"Ontology engineering aims at making explicit the knowledge contained within software applications, and within enterprises and business procedures for a particular domain."
"Automated processing of information not interpretable by software agents can be improved by adding rich semantics to the corresponding resources, such as video files."
"One of the approaches for the formal conceptualization of represented knowledge domains is the use of machine-interpretable ontologies, which provide structured data in, or based on, RDF, RDFS, and OWL."
"They contain terminological, assertional, and relational axioms to define concepts (classes), individuals, and roles (properties) (TBox, ABox, and RBox, respectively)."
"A common way to provide the logical underpinning of ontologies is to formalize the axioms with description logics, which can then be translated to any serialization of RDF, such as RDF/XML or Turtle."
"This information, based on human experience and knowledge, is valuable for reasoners for the automated interpretation of sophisticated and ambiguous contents, such as the visual content of multimedia resources."
"Application areas of ontology-based reasoning include, but are not limited to, information retrieval, automated scene interpretation, and knowledge discovery."
"A large-scale representation of abstract concepts such as actions, time, physical objects, and beliefs would be an example of ontological engineering."
"Ontology engineering is one of the areas of applied ontology, and can be seen as an application of philosophical ontology."
"In a broader sense, this field also includes a knowledge construction of the domain using formal ontology representations such as OWL/RDF."
"Core ideas and objectives of ontology engineering are also central in conceptual modeling."
"...aims at making explicit the knowledge contained within software applications, and within enterprises and business procedures for a particular domain."
"Ontology engineering offers a direction towards solving the interoperability problems brought about by semantic obstacles, i.e. the obstacles related to the definitions of business terms and software classes."
"Ontology engineering is a relatively new field of study concerning the ontology development process, the ontology life cycle, the methods and methodologies for building ontologies, and the tool suites and languages that support them."
"The concept definitions can be mapped to any kind of resource or resource segment in RDF, such as images, videos, and regions of interest, to annotate objects, persons, etc., and interlink them with related resources across knowledge bases, ontologies, and LOD datasets."
"This information, based on human experience and knowledge, is valuable for reasoners for the automated interpretation of sophisticated and ambiguous contents."
"Automated processing of information not interpretable by software agents can be improved by adding rich semantics to the corresponding resources, such as video files."
"Application areas of ontology-based reasoning include...knowledge discovery."