Formal ontology

Last updated

In philosophy, the term formal ontology is used to refer to an ontology defined by axioms in a formal language with the goal to provide an unbiased (domain- and application-independent) view on reality, which can help the modeler of domain- or application-specific ontologies to avoid possibly erroneous ontological assumptions encountered in modeling large-scale ontologies.

Contents

By maintaining an independent view on reality, a formal (upper level) ontology gains the following properties:

Historical background

Theories on how to conceptualize reality date back as far as Plato and Aristotle. The term 'formal ontology' itself was coined by Edmund Husserl in the second edition of his Logical Investigations (1900–01), where it refers to an ontological counterpart of formal logic. Formal ontology for Husserl embraces an axiomatized mereology and a theory of dependence relations, for example between the qualities of an object and the object itself. 'Formal' signifies not the use of a formal-logical language, but rather: non-material, or in other words domain-independent (of universal application). Husserl's ideas on formal ontology were developed especially by his Polish student Roman Ingarden in his Controversy over the Existence of the World. [1] The relations between the Husserlian tradition of formal ontology and the Polish tradition of mereology are set forth in Parts and Moments. Studies in Logic and Formal Ontology , [2] edited by Barry Smith.

Existing formal ontologies (foundational ontologies)

Common terms in formal (upper-level) ontologies

The differences in terminology used between separate formal upper-level ontologies can be quite substantial, but most formal upper-level ontologies apply one foremost dichotomy: that between endurants and perdurants.

Endurant

Also known as continuants, or in some cases as "substance", endurants are those entities that can be observed-perceived as a complete concept, at no matter which given snapshot of time. Were we to freeze time we would still be able to perceive/conceive the entire endurant.

Examples include material objects (such as an apple or a human), and abstract "fiat" objects (such as an organization, or the border of a country).

Perdurant

Also known as occurrents, accidents or happenings, perdurants are those entities for which only a part exists if we look at them at any given snapshot in time. When we freeze time we can only see a part of the perdurant. Perdurants are often what we know as processes, for example: "running". If we freeze time then we only see a part of the running, without any previous knowledge one might not even be able to determine the actual process as being a process of running. Other examples include an activation, a kiss, or a procedure.

Qualities

In a broad sense, qualities can also be known as properties or tropes.

Qualities do not exist on their own, but they need another entity (in many formal ontologies this entity is restricted to be an endurant) which they occupy. Examples of qualities and the values they assume include colors (red color), or temperatures (warm).

Most formal upper-level ontologies recognize qualities, attributes, tropes, or something related, although the exact classification may differ. Some see qualities and the values they can assume (sometimes called quale) as a separate hierarchy besides endurants and perdurants (example: DOLCE). Others classify qualities as a subsection of endurants, e.g. the dependent endurants (example: BFO). Others consider property-instances or tropes that are single characteristics of individuals as the atoms of the ontology, the simpler entities of which all other entities are composed, so that all the entities are sums or bundles of tropes.

Formal versus nonformal

In information science an ontology is formal if it is specified in a formal language, otherwise it is informal.

In philosophy, a separate distinction between formal and nonformal ontologies exists, which does not relate to the use of a formal language.

Example

An ontology might contain a concept representing 'mobility of the arm'. In a nonformal ontology a concept like this can often be classified as for example a 'finding of the arm', right next to other concepts such as 'bruising of the arm'. This method of modeling might create problems with increasing amounts information, as there is no foolproof way to keep hierarchies like this, or their descendant hierarchies (one is a process, the other is a quality) from entangling or knotting.

In a formal ontology, there is an optimal way to properly classify this concept, it is a kind of 'mobility', which is a kind of quality/property (see above). As a quality, it is said to inhere in independent endurant entities (see above), as such, it cannot exist without a bearer (in the case the arm).

Applications for formal (upper-level) ontologies

Formal ontology as a template to create novel specific domain ontologies

Having a formal ontology at your disposal, especially when it consists of a Formal upper layer enriched with concrete domain-independent 'middle layer' concepts, can really aid the creation of a domain specific ontology. It allows the modeller to focus on the content of the domain specific ontology without having to worry on the exact higher structure or abstract philosophical framework that gives his ontology a rigid backbone. Disjoint axioms at the higher level will prevent many of the commonly made ontological mistakes made when creating the detailed layer of the ontology.

Formal ontology as a crossmapping hub: crossmapping taxonomies, databases and nonformal ontologies

Aligning terminologies and ontologies is not an easy task. The divergence of the underlying meaning of word descriptions and terms within different information sources is a well known obstacle for direct approaches to data integration and mapping. One single description may have a completely different meaning in one data source when compared with another. This is because different databases/terminologies often have a different viewpoint on similar items. They are usually built with a specific application-perspective in mind and their hierarchical structure represents this.

A formal ontology, on the other hand, represents entities without a particular application scope. Its hierarchy reflects ontological principles and a basic class-subclass relation between its concepts. A consistent framework like this is ideal for crossmapping data sources. However, one cannot just integrate these external data sources in the formal ontology. A direct incorporation would lead to corruption of the framework and principles of the formal ontology.

A formal ontology is a great crossmapping hub only if a complete distinction between the content and structure of the external information sources and the formal ontology itself is maintained. This is possible by specifying a mapping relation between concepts from a chaotic external information source and a concept in the formal ontology that corresponds with the meaning of the former concept.

Where two or more external information sources map to one and the same formal ontology concept a crossmapping/translation is achieved, as you know that those concepts—no matter what their phrasing is—mean the same thing.

Formal ontology to empower natural language processing

In ontologies designed to serve natural language processing (NLP) and natural language understanding (NLU) systems, ontology concepts are usually connected and symbolized by terms. This kind of connection represents a linguistic realization. Terms are words or a combination of words (multi-word units), in different languages, used to describe in natural language an element from reality, and hence connected to that formal ontology concept that frames this element in reality.

The lexicon, the collection of terms and their inflections assigned to the concepts and relationships in an ontology, forms the ‘ontology interface to natural language’, the channel through which the ontology can be accessed from a natural language input.

Formal ontology to normalize database/instance data

The great thing about a formal ontology, in contrast to rigid taxonomies or classifications, is that it allows for indefinite expansion. Given proper modeling, just about any kind of conceptual information, no matter the content, can find its place.

To disambiguate a concept's place in the ontology, often a context model is useful to improve the classification power. The model typically applies rules to surrounding elements of the context to select the most valid classification.

See also

Related Research Articles

Knowledge representation and reasoning is the field of artificial intelligence (AI) dedicated to representing information about the world in a form that a computer system can use to solve complex tasks such as diagnosing a medical condition or having a dialog in a natural language. Knowledge representation incorporates findings from psychology about how humans solve problems, and represent knowledge in order to design formalisms that will make complex systems easier to design and build. Knowledge representation and reasoning also incorporates findings from logic to automate various kinds of reasoning.

<span class="mw-page-title-main">Ontology</span> Philosophical study of being and existence

In metaphysics, ontology is the philosophical study of being. It investigates what types of entities exist, how they are grouped into categories, and how they are related to one another on the most fundamental level. Ontologists often try to determine what the categories or highest kinds are and how they form a system of categories that encompasses the classification of all entities. Commonly proposed categories include substances, properties, relations, states of affairs, and events. These categories are characterized by fundamental ontological concepts, including particularity and universality, abstractness and concreteness, or possibility and necessity. Of special interest is the concept of ontological dependence, which determines whether the entities of a category exist on the most fundamental level. Disagreements within ontology are often about whether entities belonging to a certain category exist and, if so, how they are related to other entities.

In information science, an ontology encompasses a representation, formal naming, and definitions of the categories, properties, and relations between the concepts, data, or entities that pertain to one, many, or all domains of discourse. More simply, an ontology is a way of showing the properties of a subject area and how they are related, by defining a set of terms and relational expressions that represent the entities in that subject area. The field which studies ontologies so conceived is sometimes referred to as applied ontology.

<span class="mw-page-title-main">Data model</span> Model that organizes elements of data and how they relate to one another and to real-world entities.

A data model is an abstract model that organizes elements of data and standardizes how they relate to one another and to the properties of real-world entities. For instance, a data model may specify that the data element representing a car be composed of a number of other elements which, in turn, represent the color and size of the car and define its owner.

The Suggested Upper Merged Ontology (SUMO) is an upper ontology intended as a foundation ontology for a variety of computer information processing systems. SUMO defines a hierarchy of classes and related rules and relationships. These are expressed in a version of the language SUO-KIF, a higher-order logic that has a LISP-like syntax, as well as the TPTP family of languages. A mapping from WordNet synsets to SUMO has been defined. Initially, SUMO was focused on meta-level concepts, and thereby would lead naturally to a categorization scheme for encyclopedias. It has now been considerably expanded to include a mid-level ontology and dozens of domain ontologies.

A modeling language is any artificial language that can be used to express data, information or knowledge or systems in a structure that is defined by a consistent set of rules. The rules are used for interpretation of the meaning of components in the structure Programing language.

In logic, philosophy and related fields, mereology is the study of parts and the wholes they form. Whereas set theory is founded on the membership relation between a set and its elements, mereology emphasizes the meronomic relation between entities, which—from a set-theoretic perspective—is closer to the concept of inclusion between sets.

<span class="mw-page-title-main">Barry Smith (ontologist)</span> American philosopher

Barry Smith is an academic working in the fields of ontology and biomedical informatics. Smith is the author of more than 700 scientific publications, including 15 authored or edited books, and he is one of the most widely cited living philosophers.

<span class="mw-page-title-main">SNOMED CT</span> System for medical classification

SNOMED CT or SNOMED Clinical Terms is a systematically organized computer-processable collection of medical terms providing codes, terms, synonyms and definitions used in clinical documentation and reporting. SNOMED CT is considered to be the most comprehensive, multilingual clinical healthcare terminology in the world. The primary purpose of SNOMED CT is to encode the meanings that are used in health information and to support the effective clinical recording of data with the aim of improving patient care. SNOMED CT provides the core general terminology for electronic health records. SNOMED CT comprehensive coverage includes: clinical findings, symptoms, diagnoses, procedures, body structures, organisms and other etiologies, substances, pharmaceuticals, devices and specimens.

In information science, an upper ontology is an ontology that consists of very general terms that are common across all domains. An important function of an upper ontology is to support broad semantic interoperability among a large number of domain-specific ontologies by providing a common starting point for the formulation of definitions. Terms in the domain ontology are ranked under the terms in the upper ontology, e.g., the upper ontology classes are superclasses or supersets of all the classes in the domain ontologies.

In philosophy, mereological essentialism is a mereological thesis about the relationship between wholes, their parts, and the conditions of their persistence. According to mereological essentialism, objects have their parts necessarily. If an object were to lose or gain a part, it would cease to exist; it would no longer be the original object but a new and different one.

The CIDOC Conceptual Reference Model (CRM) provides an extensible ontology for concepts and information in cultural heritage and museum documentation. It is the international standard (ISO 21127:2023) for the controlled exchange of cultural heritage information. Galleries, libraries, archives, museums (GLAMs), and other cultural institutions are encouraged to use the CIDOC CRM to enhance accessibility to museum-related information and knowledge.

Ontology-based data integration involves the use of one or more ontologies to effectively combine data or information from multiple heterogeneous sources. It is one of the multiple data integration approaches and may be classified as Global-As-View (GAV). The effectiveness of ontology‑based data integration is closely tied to the consistency and expressivity of the ontology used in the integration process.

Metaphysics is the branch of philosophy that investigates principles of reality transcending those of any particular science. Cosmology and ontology are traditional branches of metaphysics. It is concerned with explaining the fundamental nature of being and the world. Someone who studies metaphysics can be called either a "metaphysician" or a "metaphysicist".

<span class="mw-page-title-main">Ontology engineering</span> Field that studies the methods and methodologies for building ontologies

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 of a given domain of interest. In a broader sense, this field also includes a knowledge construction of the domain using formal ontology representations such as OWL/RDF. 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. Core ideas and objectives of ontology engineering are also central in conceptual modeling.

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.

In philosophy, a process ontology refers to a universal model of the structure of the world as an ordered wholeness. Such ontologies are fundamental ontologies, in contrast to the so-called applied ontologies. Fundamental ontologies do not claim to be accessible to any empirical proof in itself but to be a structural design pattern, out of which empirical phenomena can be explained and put together consistently. Throughout Western history, the dominating fundamental ontology is the so-called substance theory. However, fundamental process ontologies have become more important in recent times, because the progress in the discovery of the foundations of physics has spurred the development of a basic concept able to integrate such boundary notions as "energy," "object", and those of the physical dimensions of space and time.

<span class="mw-page-title-main">Taxonomy</span> Science of classification

Taxonomy is the practice and science of categorization or classification.

<span class="mw-page-title-main">UMBEL</span>

UMBEL is a logically organized knowledge graph of 34,000 concepts and entity types that can be used in information science for relating information from disparate sources to one another. It was retired at the end of 2019. UMBEL was first released in July 2008. Version 1.00 was released in February 2011. Its current release is version 1.50.

The Unified Foundational Ontology (UFO). is an ontological framework developed in the early 2000s with the objective of providing foundational support for conceptual modeling. It synthesizes elements from formal ontology, cognitive science, linguistics, and philosophical logic to inform the structure and semantics of conceptual models. The ontology is utilized to articulate a variety of fundamental notions within conceptual modeling, offering a systematic approach to categorizing entities and delineating their properties.

References

  1. Roman Ingarden, Controversy over the Existence of the World. Volumes I and II, translated by Arthur Szylewicz, Bern: Peter Lang, 2013 / 2016.
  2. Barry Smith (ed.), Parts and Moments. Studies in Logic and Formal Ontology, Munich: Philosophia, 1982, reprinted 2001.