"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."
Methods for evaluating the quality and effectiveness of ontologies such as the OWL Test Cases and OAEI.
Ontology Evaluation: An overview of what ontology evaluation is, its objectives, and the challenges associated with it.
Ontology Metrics: Measures used to assess the quality and effectiveness of ontologies such as completeness, clarity, coherence, and consistency.
Ontology Benchmarking: The process of comparing different ontologies against a set of predefined criteria to determine their relative strengths and weaknesses.
Ontology Visualization: Techniques used for representing ontologies graphically or through other visual means to facilitate their comprehension, analysis, and evaluation.
Ontology Alignment: The process of mapping concepts and relationships from one ontology to another or to a reference ontology to facilitate interoperability and integration.
Ontology Testing: Techniques used to verify and validate the logical consistency, correctness, and completeness of an ontology.
Ontology User Satisfaction: Methods for measuring how well an ontology meets the needs and expectations of its intended users.
Ontology Explanation: Mechanisms for generating explanations of the ontology's contents, structure, and reasoning processes to improve its transparency and understandability.
Ontology Comparison: Methods used to compare ontologies and identify differences in their structure, semantics, and purpose.
Ontology Usability: Approaches used to evaluate how easy it is to use an ontology, how intuitive its interface is, and how well it supports user tasks and goals.
Content evaluation: This type of ontology evaluation focuses on the comprehensiveness and inclusiveness of the ontology's content, assessing if it covers all relevant concepts and if the relationships between the concepts are accurately represented.
Consistency evaluation: It ensures that the ontology's axioms and rules are not contradictory or conflicting, thereby assuring its internal consistency.
Completeness evaluation: It evaluates the completeness of the ontology with respect to its coverage and compliance with relevant domain standards and guidelines.
Axiom evaluation: This evaluation checks the validity and coherence of the axioms encoded in the ontology.
Formal evaluation: This type of evaluation checks the ontology's adherence to formal semantics, including computational completeness and decidability.
Use case-based evaluation: It evaluates the ontology's ability to be used in a particular context or application and its effectiveness in supporting knowledge-based tasks.
User-centered evaluation: This type of evaluation assesses the ontology's usability, accessibility, and understandability for different types of users.
Interoperability evaluation: It evaluates the ontology's ability to interoperate with other ontologies and external knowledge sources.
Scalability evaluation: It evaluates the ontology's ability to handle larger datasets and accommodate future growth.
Evolution and maintenance evaluation: This type of evaluation assesses the ontology's ability to evolve over time to keep up with changes in the knowledge domain and to maintain its relevance and accuracy.
"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."