Community hub
0 subscribers7 pages, 0 posts
Recent from talks
Be the first to start a discussion here.
Be the first to start a discussion here.
Be the first to start a discussion here.
Be the first to start a discussion here.
Contribute something
Welcome to the community hub built to collect knowledge and have discussions related to Ontology components.
Nothing was collected or created yet.
Ontology components
View on Wikipediafrom Wikipedia
 | This article needs additional citations for verification. (June 2009) |
Contemporary ontologies share many structural similarities, regardless of the ontology language in which they are expressed. Most ontologies describe individuals (instances), classes (concepts), attributes, and relations.
List
[edit]Common components of ontologies include:
- Individuals
- instances or objects (the basic or "ground level" objects; the tokens).
- Classes
- sets, collections, concepts, types of objects, or kinds of things.[1]
- Attributes
- aspects, properties, features, characteristics, or parameters that individuals (and classes and relations) can have.[2]
- Relations
- ways in which classes and individuals can be related to one another. Relations can carry attributes that specify the relation further.[3]
- Function terms
- complex structures formed from certain relations that can be used in place of an individual term in a statement.
- Restrictions
- formally stated descriptions of what must be true in order for some assertion to be accepted as input.
- Rules
- statements in the form of an if-then (antecedent-consequent) sentence that describe the logical inferences that can be drawn from an assertion in a particular form.
- Axioms
- assertions (including rules) in a logical form that together comprise the overall theory that the ontology describes in its domain of application.[4] This definition differs from that of "axioms" in generative grammar and formal logic. In these disciplines, axioms include only statements asserted as a priori knowledge. As used here, "axioms" also include the theory derived from axiomatic statements.[citation needed]
- Events
- the changing of attributes or relations.
- Actions
- types of events.
Ontologies are commonly encoded using ontology languages.
Individuals
[edit]Individuals (instances) are the basic, "ground level" components of an ontology. The individuals in an ontology may include concrete objects such as people, animals, tables, automobiles, molecules, and planets, as well as abstract individuals such as numbers and words (although there are differences of opinion as to whether numbers and words are classes or individuals). Strictly speaking, an ontology need not include any individuals, but one of the general purposes of an ontology is to provide a means of classifying individuals, even if those individuals are not explicitly part of the ontology.
In formal extensional ontologies, only the utterances of words and numbers are considered individuals – the numbers and names themselves are classes. In a 4D ontology, an individual is identified by its spatio-temporal extent. Examples of formal extensional ontologies are BORO, ISO 15926 and the model in development by the IDEAS Group.
Classes
[edit]
In knowledge representation, a class is a collection of individuals or individuals objects.[5] A class can be defined either by extension (specifying members), or by intension (specifying conditions), using what is called in some ontology languages like OWL. According to the type–token distinction, the ontology is divided into individuals, who are real worlds objects, or events, and types, or classes, who are sets of real world objects. Class expressions or definitions gives the properties that the individuals must fulfill to be members of the class. Individuals that fulfill the property are called instances (as in the computing concept).
Attributes
[edit]Objects in an ontology can be described by relating them to other things, typically aspects or parts. These related things are often called attributes, although they may be independent things. Each attribute can be a class or an individual. The kind of object and the kind of attribute determine the kind of relation between them. A relation between an object and an attribute express a fact that is specific to the object to which it is related. For example, the Ford Explorer object has attributes such as:
- ⟨has as name⟩ Ford Explorer
- ⟨as by definition as part⟩ 6-speed transmission
- ⟨as by definition as part⟩ door (with as minimum and maximum cardinality: 4)
- ⟨as by definition as part one of⟩ {4.0L engine, 4.6L engine}
The value of an attribute can be a complex data type; in this example, the related engine can only be one of a list of subtypes of engines, not just a single thing.
Ontologies are only true ontologies if concepts are related to other concepts (the concepts do have attributes). If that is not the case, then you would have either a taxonomy (if hyponym relationships exist between concepts) or a controlled vocabulary. These are useful, but are not considered true ontologies.
Relations
[edit]Relations (also known as relationships) between objects in an ontology specify how objects are related to other objects. Typically a relation is of a particular type (or class) that specifies in what sense the object is related to the other object in the ontology. For example, in the ontology that contains the concept Ford Explorer and the concept Ford Bronco might be related by a relation of type ⟨is defined as a successor of⟩. The full expression of that fact then becomes:
- Ford Explorer is defined as a successor of : Ford Bronco
This tells us that the Explorer is the model that replaced the Bronco. This example also illustrates that the relation has a direction of expression. The inverse expression expresses the same fact, but with a reverse phrase in natural language.
Much of the power of ontologies comes from the ability to describe relations. Together, the set of relations describes the semantics of the domain: that is, its various semantic relations, such as synonymy, hyponymy and hypernymy, coordinate relation, and others. The set of used relation types (classes of relations) and their subsumption hierarchy describe the expression power of the language in which the ontology is expressed.
An important type of relation is the subsumption relation (is-a-superclass-of, the converse of is-a, is-a-subtype-of or is-a-subclass-of). This defines which objects are classified by which class. For example, we have already seen that the class Ford Explorer is-a-subclass-of 4-Wheel Drive Car, which in turn is-a-subclass-of Car.
The addition of the is-a-subclass-of relationships creates a taxonomy; a tree-like structure (or, more generally, a partially ordered set) that clearly depicts how objects relate to one another. In such a structure, each object is the 'child' of a 'parent class' (Some languages restrict the is-a-subclass-of relationship to one parent for all nodes, but many do not).
Another common type of relations is the mereology relation, written as part-of, that represents how objects combine to form composite objects. For example, if we extended our example ontology to include concepts like Steering Wheel, we would say that a "Steering Wheel is-by-definition-a-part-of-a Ford Explorer" since a steering wheel is always one of the components of a Ford Explorer. If we introduce meronymy relationships to our ontology, the hierarchy that emerges is no longer able to be held in a simple tree-like structure since now members can appear under more than one parent or branch. Instead this new structure that emerges is known as a directed acyclic graph.
As well as the standard is-a-subclass-of and is-by-definition-a-part-of-a relations, ontologies often include additional types of relations that further refine the semantics they model. Ontologies might distinguish between different categories of relation types. For example:
- relation types for relations between classes
- relation types for relations between individuals
- relation types for relations between an individual and a class
- relation types for relations between a single object and a collection
- relation types for relations between collections
Relation types are sometimes domain-specific and are then used to store specific kinds of facts or to answer particular types of questions. If the definitions of the relation types are included in an ontology, then the ontology defines its own ontology definition language. An example of an ontology that defines its own relation types and distinguishes between various categories of relation types is the Gellish ontology.
For example, in the domain of automobiles, we might need a made-in type relationship which tells us where each car is built. So the Ford Explorer is made-in Louisville. The ontology may also know that Louisville is-located-in Kentucky and Kentucky is-classified-as-a state and is-a-part-of the U.S. Software using this ontology could now answer a question like "which cars are made in the U.S.?"
Notes
[edit]- ^ See Class (set theory), Class (computer science), and Class (philosophy), each of which is relevant but not identical to the notion of a "class" here.
- ^ Taniar, David (28 February 2006). Web Semantics & Ontology. Idea Group Inc (IGI). ISBN 978-1-59140-907-6.
- ^ Asunción Gómez-Pérez; Mariano Fernandez-Lopez; Oscar Corcho (18 April 2006). Ontological Engineering: With Examples From the Areas of Knowledge Management, E-Commerce and the Semantic Web (1st ed.). Springer Science & Business Media. ISBN 978-1-85233-840-4.
- ^ Maureen Donnelly; Giancarlo Guizzardi (2012). Formal Ontology in Information Systems: Proceedings of the Seventh International Conference (FOIS 2012). IOS Press. ISBN 978-1-61499-083-3.
- ^ Diego Calvanese; Giuseppe De Giacomo; Maurizio Lenzerini (2002). Description Logics: Foundations for Class-based Knowledge Representation. Logic in Computer Science.
Ontology components
View on Grokipediafrom Grokipedia
Ontology components are the fundamental building blocks of a formal knowledge representation system, enabling the explicit specification of concepts, relationships, and instances within a domain to support reasoning, interoperability, and shared understanding in computational environments.[1] In the Semantic Web, these components are standardized in the Web Ontology Language (OWL), which defines ontologies as sets of axioms expressed using vocabulary from RDF and OWL itself, facilitating machine-readable descriptions of knowledge.[1]
The primary components of an OWL ontology include classes, which represent sets of individuals sharing common characteristics, such as "Person" or "Animal," and can be organized into hierarchies using subclass relationships to model taxonomic structures.[1] Properties further define relationships between elements: object properties link individuals to other individuals (e.g., "hasChild" connecting a parent to their offspring), while datatype properties associate individuals with literal data values (e.g., "hasAge" linking a person to an integer like 30), each with specified domains and ranges to constrain their use.[1] Individuals are the specific, named instances or objects within the ontology, such as "John" asserted as an instance of the "Person" class, allowing for assertions of class membership, equality, or inequality between them.[1] Finally, data values provide typed literals, drawn from XML Schema datatypes like strings or integers, to capture factual details without introducing new individuals, ensuring precise and verifiable information in the ontology.[1]
These components collectively form a logical theory over a vocabulary, as outlined in foundational definitions of ontologies as explicit specifications of conceptualizations, where axioms and annotations enhance interpretability and enable automated inference.[2] By integrating these elements, ontologies support applications in areas like knowledge graphs, semantic search, and artificial intelligence, promoting consistency across distributed systems.[1]
Overview
Definition
Ontology components are the foundational building blocks of an ontology, serving as the representational primitives that formally specify a domain's knowledge through entities, properties, and rules to define its structure and semantics.[3] In computational contexts, these components include classes for categorizing concepts, individuals for specific instances, attributes and relations as properties, and axioms for constraints and inferences, enabling a precise, machine-interpretable model of the domain.[4] Classes and individuals represent the primary entities, forming the core of this knowledge structure.[3] The purpose of ontology components is to support machine-readable knowledge representation, automated inference, and semantic interoperability across diverse systems and applications in areas such as artificial intelligence and the Semantic Web.[5] By providing a formalized vocabulary of terms and their interrelations, these components allow for the sharing and reuse of knowledge, facilitating reasoning over complex data and enhancing accessibility for computational processes.[5] A key example is the Web Ontology Language (OWL), where ontology components formalize concepts from description logics to model rich domain semantics, including classes, properties, individuals, and data values stored as Semantic Web documents.[4] This approach enables precise definitions and entailments, supporting applications like ontology-driven data integration.[1] In contrast to philosophical ontology, which examines the metaphysical nature of being and existence, computational ontology components emphasize practical formal structures for knowledge processing and interoperability rather than abstract theorizing.[6]Historical Context
The concept of ontology traces its roots to ancient philosophy, where Aristotle in the 4th century BCE outlined a system of categories in his work Categories, serving as an early precursor to modern notions of classes and attributes by classifying entities into ten fundamental types such as substance, quantity, and quality.[7] This framework provided a foundational structure for understanding being and predication, influencing subsequent metaphysical inquiries into the nature of existence and properties.[7] In the 20th century, philosophical developments further shaped ontological thinking, particularly through Willard Van Orman Quine's exploration of ontological commitment in the 1950s, which emphasized the implications of theoretical language for what exists, thereby laying groundwork for formal commitments in knowledge representation.[8] Building on this, John F. Sowa's introduction of conceptual graphs in the 1980s offered a graphical notation for logic and semantics, significantly influencing the modeling of relations between concepts in computational systems.[9] The shift to computational ontology occurred in the 1990s, marked by Thomas Gruber's influential 1993 definition of ontology as "an explicit specification of a conceptualization," which formalized ontologies as structured components within knowledge bases to enable shared understanding in artificial intelligence and information systems.[10] This definition catalyzed the integration of philosophical principles into practical tools for knowledge engineering. A key milestone in the adoption of ontology components came in the 2000s with the Semantic Web initiative, where the World Wide Web Consortium (W3C) standardized RDF in 1999 for resource description and OWL in 2004 for web ontology language, facilitating the widespread use of classes, properties, and axioms in distributed knowledge representation.[11][12]Core Entities
Classes
In ontologies, classes serve as abstract categories or concepts that represent sets of individuals sharing common properties, forming the foundational hierarchical structure for organizing domain knowledge.[13] These classes are typically arranged in taxonomies through subclass-superclass relationships, where a subclass inherits properties from its superclass, enabling a structured representation of "is-a" hierarchies such as "Mammal" as a superclass of "Dog."[13] Key features of classes include subclass relations, which establish inheritance and transitivity (e.g., if "Canine" is a subclass of "Mammal" and "Dog" is a subclass of "Canine," then "Dog" is a subclass of "Mammal"); disjointness, where classes are declared to have no overlapping instances (e.g., "Red Wine" and "White Wine" share no common individuals); and equivalence, where two classes are considered identical if they mutually subsume each other.[13] Individuals, or instances, belong to one or more classes, populating these abstract categories with concrete entities.[13] In description logics, the formal foundation for many ontology languages, classes are denoted by concept names such as or , and complex classes are constructed using operators including union (), which denotes the set of individuals in either or ; intersection (), the set of individuals in both; and complement (), the set of individuals not in . These constructors allow for expressive definitions of class structures within ontologies. For instance, in the Disease Ontology, the class "Disease" encompasses subclasses such as "Infectious Disease," which further branches into specific types like "Viral Disease," facilitating standardized representation of medical concepts. This hierarchical organization supports interoperability in biomedical data sharing. Classes play a crucial role in inference through subsumption, where a class subsumes class (denoted ) if every individual in is also an individual in , allowing automated reasoning to infer properties across the hierarchy (e.g., all instances of "Dog" inherit traits of "Animal"). Such subsumption enables efficient classification and query answering in ontology-based systems.Individuals
In ontologies, individuals, also known as instances, represent specific, concrete entities that instantiate one or more classes, serving as the atomic elements that populate the abstract structure defined by classes.[13][1] These entities model real-world objects or abstract particulars within a domain, such as a particular person, place, or item, distinguishing them from the general categories provided by classes.[14] For instance, in a wine ontology, "Château-Morgon-Beaujolais" functions as an individual instance of the class "Beaujolais," capturing a unique wine with attributes like light body and red color.[13] Key features of individuals include their uniqueness within the ontology's interpretation, where no two individuals are assumed identical unless explicitly stated through equality assertions, and their membership in classes via class assertions.[1] In formal languages like OWL 2, individuals can be named using Internationalized Resource Identifiers (IRIs), such as:Mary for a specific person, or anonymous, particularly in existential restrictions where an unnamed entity satisfies a condition without needing explicit identification.[1] This anonymity allows for compact representations, as seen in expressions denoting "individuals who have a wife," without naming them individually.[1] Every individual implicitly belongs to the universal class owl:Thing, ensuring all instances fit within the ontology's top-level structure.[15]
Individuals play a crucial role in grounding the ontology's abstract classes with tangible examples, enabling practical applications such as querying specific entities, performing reasoning over instance data, and building knowledge bases that extend beyond mere schema definitions.[13] For example, in a geography ontology, "Paris" serves as a named individual of the class "City," allowing inferences about its properties and facilitating domain-specific queries like locating European capitals.[1] By instantiating classes, individuals transform the ontology from a taxonomic framework into a populated knowledge representation system, supporting tasks like semantic search and automated decision-making.[14]
Properties
Attributes
Attributes, also known as data properties in ontology languages like OWL, are binary relations that connect entities—such as classes or individuals—to literal data values, including numbers, strings, dates, or other primitive types.[1] These properties describe inherent characteristics of entities without referencing other ontological entities, distinguishing them from object properties that form links between individuals.[4] Key features of attributes include their domain, which specifies the classes or individuals to which the property applies; their range, which defines the permissible data types for the values, such as xsd:integer for numerical data or xsd:string for textual content; and their functionality, where functional attributes allow at most one value per entity (e.g., declared as owl:FunctionalProperty), while non-functional ones permit multiple values.[1] For instance, the domain of an attribute might be restricted to the class Person, ensuring it only applies to human individuals, while the range could be limited to non-negative integers to enforce valid inputs.[4] In description logics, the formal foundation for many ontology languages, attributes are represented through role constructors and restrictions on concrete domains, such as existential restrictions ∃r.C (where C is a concrete concept, e.g., {v} for a specific value v, asserting at least v), universal value restrictions ∀r.C (all fillers in C), and qualified number restrictions like (≤ n r.C) to limit the number of fillers in C to at most n. Exact values can be enforced by combining these, such as with functional roles.[16] These mechanisms extend basic description logics like ALC to handle data-valued features, enabling precise constraints on literal assignments. A representative example is the attribute "age" defined for the class Person with a range of integers, where an individual like John might be asserted to have age 51, expressed in OWL as DataPropertyAssertion(:hasAge :John "51"^^xsd:integer).[1] This assignment captures a quantifiable trait directly tied to the individual. Attributes play a crucial role in ontologies by facilitating the description of quantifiable or textual features, which supports advanced querying, data validation, and inference in knowledge bases, such as ensuring consistency in search operations over literal values.[4]Relations
In ontologies, relations, also known as object properties, are formal constructs that define binary relationships between entities, such as classes or individuals, thereby enabling the composition and navigation of ontological structures.[1] These properties link one entity to another, facilitating the representation of complex interconnections within the domain knowledge, as opposed to attributes that assign literal values to entities.[1] Key features of relations include their domain and range, which specify the classes of entities that can participate as subjects and objects, respectively; for instance, a relation might connect instances of class A to instances of class B.[1] Additional characteristics encompass inverse relations, where the direction of the link can be reversed (e.g., if R connects x to y, then the inverse R⁻ connects y to x); symmetry, where the relation holds bidirectionally (R ≡ R⁻); transitivity, allowing the reflexive transitive closure R⁺ such that if x R y and y R z, then x R⁺ z; and cardinality restrictions, which limit the number of related entities (e.g., at most n fillers for a role).[1][16] In description logics, relations are represented as roles R, which are atomic or complex expressions; composition is denoted by R ∘ S, with semantics defining pairs (x, z) where there exists y such that (x, y) ∈ R^I and (y, z) ∈ S^I, and the inverse by R⁻, with semantics {(y, x) | (x, y) ∈ R^I}.[16] Transitivity is captured through the transitive closure R⁺ = ⋃_{i≥1} (R^I)^i, enabling inferences over chained relations.[16] A representative example is the relation "capitalOf," which links an individual such as "Paris" to another individual "France," with domain restricted to the class City and range to the class Country, allowing assertions like capitalOf(Paris, France).[1] Relations play a crucial role in supporting complex queries and reasoning, such as computing the transitive closure for "ancestorOf" to identify all descendants in a family hierarchy.[1][16]Advanced Elements
Axioms
In ontologies, particularly those formalized in the Web Ontology Language (OWL), axioms serve as declarative statements that assert logical truths or constraints about the domain of discourse, forming the foundational rules from which inferences can be drawn. These statements impose restrictions on classes, properties, and individuals, ensuring that the ontology remains consistent and enabling automated reasoning to uncover implicit knowledge. Unlike mere definitions, axioms carry normative force, specifying what must hold true within the modeled world, and they are integral to description logics (DL), the logical foundation underlying OWL.[1][4] Key types of axioms include subclass axioms, which assert that one class is a subset of another, denoted in DL syntax as , meaning every instance of is also an instance of . Property axioms characterize the behavior of relations, such as functional axioms, expressed as , indicating that each domain element relates to at most one range element; symmetric axioms, where , stipulating that if holds, then also holds; and disjointness axioms, written as , ensuring that no entity can belong to both and simultaneously. These types, rooted in DL constructs, allow ontologies to model hierarchical structures, relational constraints, and mutual exclusions effectively.[4][17] Axioms in OWL are represented either as RDF triples for web-compatible serialization, where statements like subclass relations are encoded using predicates such asrdfs:subClassOf, or in DL syntax for precise semantic analysis and theorem proving. This dual representation facilitates interoperability with Semantic Web technologies while supporting formal verification through DL-based tools. For instance, the axiom "Every Dog is an Animal," formalized as , explicitly declares a taxonomic inclusion, while the axiom for sibling symmetry, , captures bidirectional familial relations without redundancy.[18][1]
The primary role of axioms is to power automated inference engines, which use them to derive entailments—such as classifying instances or detecting inconsistencies—and maintain ontology coherence. Reasoners like HermiT, implementing a hypertableau calculus for OWL 2 DL, process these axioms to compute subsumption hierarchies, check satisfiability, and realize class memberships, thereby supporting applications in knowledge engineering and semantic integration. Axioms involving classes and properties as their subjects thus enable scalable reasoning over complex domains.[19][20]