"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."
Examples of how ontologies are applied in various domains such as healthcare, finance, and e-commerce.
Introduction to Ontology: Understanding the basic principles and concepts of ontology, including its definition, scope, and role in knowledge representation and management.
Types of Ontology: Categorizing ontology based on its application, such as domain ontology, task ontology, and upper ontology.
Ontology Design: The process of creating a new ontology, including selecting a domain, identifying relevant terms and concepts, and creating relationships between them.
Ontology Development: The tools, techniques, and methods used in building an ontology, including ontology editors, reasoning engines, and semantic web technologies.
Ontology Languages: Understanding the different languages used in ontology development, including RDF, OWL, and RDFS.
Ontology Integration: The process of combining multiple ontologies to create a more comprehensive representation of knowledge.
Ontology Mapping: The process of creating relationships between concepts in different ontologies to facilitate interoperability.
Ontology-Based Information Retrieval: Using ontology to improve the accuracy and efficiency of information retrieval systems.
Ontology-Based Data Integration: Using ontology to integrate data from multiple sources and make it more accessible and usable.
Ontology-Based Reasoning: Using ontology to perform logical reasoning and make inferences based on the relationships between concepts.
Ontology-Based Semantic Web Services: Using ontology to define and describe web services and enable their automated discovery and composition.
Ontology Applications: Real-world applications of ontology, including health informatics, e-commerce, and scientific research.
Semantic Web: Ontology enables the creation of a semantic web where information can be consistently identified and understood by machines.
Knowledge Management: Ontology is used in knowledge management to help categorize and manage knowledge in a structured and efficient manner.
Natural Language Processing: Ontology is used in natural language processing to improve language understanding by machines and enable human-like communication.
Information Retrieval: Ontology helps in information retrieval by providing a structured and semantic search engine that can improve accuracy and efficiency.
E-commerce: Ontology is used in e-commerce to help with product classification, identification, and recommendation systems.
Healthcare: Ontology is used in healthcare to develop personalized medicine, assist in diagnosis, and standardize medical records.
Robotics: Ontology is used in robotics to help with decision-making, perception, and understanding of the environment.
Social Media: Ontology is used in social media to categorize and categorize content, enable content-based recommendations, and facilitate user interactions.
Education: Ontology is used in education to help with curriculum development, course design, and learning management systems.
Geographic Information Systems: Ontology is used in GIS to improve data quality, support decision-making, and create interoperable geospatial data.
Legal Reasoning: Ontology can be used in legal reasoning to help with legal knowledge management and support reasoning about complex legal questions.
Finance: Ontology is used in finance to standardize and classify financial data, support fraud detection, and enable better investment decisions.
Engineering: Ontology is used in engineering to support modeling and simulation of complex systems, enable interoperability, and promote knowledge reuse.
Agriculture: Ontology is used in agriculture to support precision farming, food traceability, and decision-making related to crop management.
Transportation: Ontology is used in transportation to support traffic management, improve safety, and enable interoperability among different transportation modes.
"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."