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A Survey of Top-level Ontologies
A Survey of Top-level Ontologies-Appendix-F
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Selected candidate source top-level ontologies – details
F.1 Introduction
This appendix is intended to give a feel for the range of different approaches to top-level ontologies, including those that make little or no ontological commitment.
We provide a brief overview (usually a self-description) and a picture of their top structure, where available. We also include comments on key ontological characteristics. A selection of relevant extracts is included as to give more insight into the characteristics. -
F.2 BFO – Basic Formal Ontology
F.2.1 Overview
The Basic Formal Ontology (BFO) framework developed by Barry Smith and his associates consists of a series of sub-ontologies at different levels of granularity. The ontologies are divided into two varieties: relating to continuant entities such as three-dimensional enduring objects, and occurrent entities (primarily) processes conceived as unfolding in successive phases through time. BFO thus incorporates both three-dimensionalist and four-dimensionalist perspectives on reality within a single framework. Interrelations are defined between the two types of ontologies in a way which gives BFO the facility to deal with both static/spatial and dynamic/temporal features of reality. A continuant domain ontology descending from BFO can be conceived as an inventory of entities existing at a time. Each occurrent ontology can be conceived as an inventory of processes unfolding through a given interval of time. Both BFO itself and each of its extension subontologies can be conceived as a window on a certain portion of reality at a given level of granularity.
From https://en.wikipedia.org/wiki/Basic_Formal_Ontology
F.2.2 Top-level
F.2.3 Key characteristics
BFO is a well-documented heavyweight foundational ontology.
It has an interesting horizontal stratification, which is documented in the journey in Figure 28.
Figure 28 – BFO Stratification Journey – six strata (time indexed relations suffixed with ‘… at a time)
This shows an unusual architecture for spacetime. While electing to be separatist about space and time, the TLO also retains spacetime. This results in both spatial and temporal redundancy. Another is the single spatio-temporal reference frame (currently) – see 3.2.1 below.
Single super-sub-type parent-arity for universals – The monohierarchy principle see 2.7 below.
Possibilia: Actualist about worlds – see 3.14 below – that is, no possible worlds. Uses dispositions for some aspects of modality. It is unclear whether it supports a full-blown ontology of modality.
Interpenetration – for example, material and immaterial entities can interpenetrate – a person (material) can stand in a doorway (immaterial) – they are related by having overlapping spatial regions not sharing parts.
Mereology – own version. For example – an immaterial object – the hold of the ship – is a part of the ship, but the material objects in the hold are not part of it, though they are situated in it. Based upon Minimal Extensional Mereology (see below).
Extensible – currently excludes numbers.
F.2.4 Relevant extracts
These extracts from: Basic Formal Ontology 2.0 – SPECIFICATION AND USER’S GUIDE (https://github.com/BFO-ontology/BFO/raw/master/docs/bfo2-reference/BFO2-Reference.pdf).
Extract 1 – Single Inheritance2.7 The monohierarchy principle
BFO rests on a number of heuristic principles that are designed to advance its utility to formal reasoning. These take the form of simple rules – analogous to the rules of the road – that are designed to promote consistency in the making of both domain-neutral and domain-specific choices in ontology construction. [19] One heuristic principle of this kind – expressing what we can think of as a principle of good behavior in the realm of universals – asserts that the asserted taxonomies of types and subtypes in BFO-conformant ontologies should be genuine trees (in the graph-theoretic sense), so that each node in the graph of universals should have at most one asserted is_a parent. (On the use of ‘asserted’ here, see [19].) This principle is of value not only because it supports a simple strategy for the formulation of definitions and thereby helps to prevent certain common kinds of error in ontology construction, but also because it brings technical benefits when ontologies are implemented computationally.
[19] Barry Smith and Werner Ceusters, “Ontological Realism as a Methodology for Coordinated Evolution of Scientific Ontologies”, Applied Ontology, 5 (2010), 139–188. PMC3104413
Extract 2 – Modality – Actualist
3.7.8 Material basis
Dispositions (and thus also functions) are introduced into BFO in order to provide a means for referring to what we can think of as the potentials or powers of things in the world without the need to quantify over putative ‘possible worlds’ or ‘possible objects’.
…
Extract 3 – Location – Separatist and Unitist
3.14 Spatiotemporal region
ELUCIDATION: A spatiotemporal region is an occurrent entity that is part of spacetime. [095-001]
‘Spacetime’ here refers to the maximal instance of the universal spatiotemporal region.
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3.15 Temporal region
Given a temporal reference frame R, we can define ‘timeR’ as the maximal instance of the universal temporal region.
ELUCIDATION: A temporal region is an occurrent entity that is part of time as defined relative to some reference frame. [100-001]
AXIOM: Every temporal region t is such that t occupies_temporal_region t. [119-002]
AXIOM: All parts of temporal regions are temporal regions. [101-001]
zero-dimensional temporal region
ELUCIDATION: A zero-dimensional temporal region is a temporal region that is without extent. [102-001]
EXAMPLES: a temporal region that is occupied by a process boundary; right now; the moment at which a finger is detached in an industrial accident; the moment at which a child is born, the moment of death.
SYNONYM: temporal instant.
…
Extract 4 – Location – Reference frames
3.2.1 Excursus on frames
The four dimensions of the spacetime continuum are not homogeneous. Rather there is one time-like and three space-like dimensions. This heterogeneity is sufficient, for the purposes of BFO, to justify our division of reality in a way that distinguishes spatial and temporal regions. In a future version, however, we will need to do justice to the fact that there are multiple ways of dividing up the spacetime continuum into spatial and temporal regions, corresponding to multiple frames that might be used by different observers.
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3.6.3 Spatial region
We recommend that users of BFO region terms specify the coordinate frame in terms of which their spatial and temporal data are represented. When dealing with spatial regions on the surface of the Earth, for example, this will be the coordinate frame of latitude and longitude, potentially supplemented by the dimension of altitude.
…
Extract 5 – Endurantist – Occurrent dependence on Continuants
3.7.2 No s-dependence of higher order
BFO does not recognize universals of higher order (for example, the universal universal). All universals are instantiated by instance entities which are not universals.
Extract 6 – Mereology – Minimal Extensional Mereology
3 Specification
3.1 Relations of parthood
As our starting point in understanding the parthood relation, we take the axioms of Minimal Extensional Mereology as defined by Simons [46, pp. 26-31], assuming, with Simons, the axioms of first order predicate calculus.
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F.3 BORO
F.3.1 Overview
Business Objects Reference Ontology is an upper ontology designed for developing ontological or semantic models for large complex operational applications that consists of a top-level ontology as well as a process for constructing the ontology. It is built upon a series of clear metaphysical choices to provide a solid (metaphysical) foundation. A key choice was for an extensional (and hence, four-dimensional) ontology which provides it with a simple criteria of identity. Elements of it have appeared in a number of standards. For example, the ISO standard, ISO 15926 – Industrial automation systems and integration – was heavily influenced by an early version. The IDEAS (International Defence Enterprise Architecture Specification for exchange) standard is based upon BORO, which in turn was used to develop DODAF 2.0.From https://en.wikipedia.org/wiki/BORO
See also: https://www.borosolutions.net/, https://en.wikipedia.org/wiki/BORO.
F.3.2 Top-level
F.3.3 Key characteristics
BORO is a well-documented heavyweight foundational ontology. It is an extensional ontology with a general unifying approach – illustrated in the journey in Figure 29.Figure 29 – BORO Stratification Journey – one stratum
Unlike most other TLOs BORO has a position on the Indexicals for ‘here’ and ‘now’. See Business objects: re-engineering for re-use. Chapter 8 – Section 4 (Partridge, 1996) – The time-based ‘consciousness’ of information systems – which discusses a ‘now’ and ‘here’ object. In a later paper, a more sophisticated way of handling indexicality using agentology is described (Partridge, 2018). The paper suggests that there is an agentology layer indexed to the agent/ system under the ontology.
Unlike most other TLOs it has a clearly documented position on the unstratified type-instance hierarchy. See Developing an Ontological Sandbox: Investigating Multi-Level Modelling’s Possible Metaphysical Structures (Partridge, 2017) and Coordinate Systems: Level Ascending Ontological Options for a detailed discussion (Partridge, 2019).
F.3.4 Relevant extracts
These extracts from: BORO as a Foundation to Enterprise Ontology - https://www.academia.edu/33717627/ – for references see the original document.
BORO includes a foundational (or upper) ontology and a closely intertwined methodology for information systems (IS) re-engineering (Partridge, 1996), hence the term BORO refers to both the ontology and the methodology. BORO was originally conceived in the late 1980s to address a particular need for a solid legacy re-engineering process and then evolved to address a wider need for developing enterprise systems in a ‘better way’; in other words in a way that was less cumbersome, compared to the heavyweight methodologies of the time, enabling higher levels of reuse and, as a consequence, capable of reducing the effort and cost of (re-)developing, maintaining and interoperating enterprise systems. It was eventually publicly documented in (Partridge, 1996).
The BORO Foundational Ontology is strongly rooted in philosophical ontology. Ontology is defined by Jonathan Lowe as “the set of things whose existence is acknowledged by a particular theory or system of thought” (Honderich, 2006, 670). This definition is particularly relevant in the context of enterprise modeling and systems development since it grounds ontology in reality (i.e. “the things whose existence is acknowledged”) rather than one’s subjective conception of what constitutes the real world. As such BORO is a realist ontology, one that recognizes the existence of an objective reality.
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In the BUML model of Figure 1 Objects represents the three top level BORO categories: Elements, Types and tuples. Every object belongs to one and only one of the three categories which are framed, as mentioned earlier, by a range of metaphysical choices. These choices mean that, within BORO, each category has its own identity criteria.- Elements are individual objects whose identity is given by the element’s spatiotemporal extent (or extension); i.e. the space and time it occupies. BORO simplifies things by assuming that matter and space-time are identical (this is a metaphysical stance that has been called super-substantivalism (Sklar, 1974; Schaffer, 2009). An example of an element would be the person John.
- Types are collections of any type of object (in other words, objects of any of the three categories). The identity of a type is determined by its extension, the collection of its instances (i.e. members). For example, the extension of the type Persons is the set of all people. In BORO, Types play a similar role to universals in other foundational ontologies.
- Tuples are relationships between objects. The identity of a tuple is defined by the laces in the tuple. An example is (Mary, John) in which the elements Mary and John occupy places 1 and 2 in the tuple respectively. Tuples can be collected into types, called tuple types. An example is parent Of, which is the collection of all relationships between parents and their children. Section 2.3 will describe tuple types and their top level patterns in more detail.
There is a system of ontological dependence relations between these categories. One rather abstract way of developing an understanding of these, and so developing a better understanding of the categories is through grounding (Fine, 2010), which provides a kind of ontogenesis narrative for the objects in the ontology. The grounding (ontogenesis) narrative starts with a single element, the pluriverse of all possible worlds (a position Schaffer (2010) calls ‘priority monism’). Consider the generative operation of decomposition that divides an element into all its parts. If we apply this to the pluriverse we then have all the elements.
This operation exhausts all elements as the pluriverse and its parts are all the elements. Then consider the generative type-builder operation; we can then apply this to the (previously generated) elements to build the type Elements; this is the ontological category of Elements. Then consider the generative operation power-type-builder (power-types are described in more detail below). Apply the powertype-builder operation to the set Elements – this builds the type that has all the subsets of Elements as its members. Applying the power type-builder operation repeatedly builds a type hierarchy. Finally, consider the generative tuplebuilder operation, this takes a number of any type of object, including tuples, and organizes them into a tuple. This grounding approach is reflected in the BORO methodology. An example is provided in Partridge (2002a).
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F.4 CICOC
F.4.1 Overview
Although “CIDOC object-oriented Conceptual Reference Model” (CRM) is a domain ontology, specialised to the purposes of representing cultural heritage, a subset called CRM Core is a generic upper ontology, including:
- Space-Time – title/identifier, place, era/ period, time-span, relationship to persistent items
- Events – title/identifier, beginning/ending of existence, participants (people, either individually or in groups), creation/ modification of things (physical or conceptional), relationship to persistent items
- Material Things – title/identifier, place, the information object the material thing carries, part-of relationships, relationship to persistent items
- Immaterial Things – title/identifier, information objects (propositional or symbolic), conceptional things, part-of relationships.
A persistent item is a physical or conceptional item that has a persistent identity recognized within the duration of its existence by its identification rather than by its continuity or by observation. A persistent item is comparable to an endurant.
A propositional object is a set of statements about real or imaginary things.
A symbolic object is a sign/symbol or an aggregation of signs or symbols.
From https://en.wikipedia.org/wiki/Upper_ ontology#CIDOC_Conceptual_Reference_Model
See also: http://www.cidoc-crm.org/ Also ISO 21127:2014 Information and documentation – A reference ontology for the interchange of cultural heritage information –
https://www.iso.org/standard/57832.htmlF.4.2. Top-level
F.4.3 Key characteristics
CIDOC is a lightweight foundational ontology. It does not have much documentation of its ontological commitments.
F.4.4. Relevant extracts
These extracts from: ISO 21127:2014 Information and documentation – A reference ontology for the interchange of cultural heritage information – https://www.iso.org/standard/57832.html.
3.1 – class – category of items that share one or more common traits.
(Hence, intensional criterion of identity)
3.11 – multiple inheritance – possibility for a class to have more than one immediate superclass
E77 Persistent Item – Scope note: This class comprises items that have a persistent identity, sometimes known as “endurants” in philosophy. They can be repeatedly recognized within the duration of their existence by identity criteria rather than by continuity or observation. Persistent Items can be either physical entities, such as people, animals, or things; or conceptual entities, such as ideas, concepts, products of the imagination, or common names. … The main classes of objects that fall outside the scope of the E77 Persistent Item class are temporal objects such as periods, events and acts, and descriptive properties.
(Hence, endurant stratification)
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F.5. CIM
F.5.1 Overview
The Common Information Model (CIM) is an open standard that defines how managed elements in an IT environment are represented as a common set of objects and relationships between them. The Distributed Management Task Force maintains the CIM to allow consistent management of these managed elements, independent of their manufacturer or provider.
From https://en.wikipedia.org/wiki/Common_ Information_Model_(computing)
See also:
https://www.dmtf.org/standards/cimF.5.2. Top-level
F.5.3. Key characteristics
CIM is a generic top-level data model. It has few, if any, foundational ontological commitments). It is understandably domain focussed.
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F.6 ConML + CHARM – Conceptual Modelling Language and Cultural Heritage Abstract Reference Model
F.6.1 Overview
ConML is a conceptual modelling language that has been constructed from scratch with three major goals in mind:
Ease of use for non-experts in information technologies.
Simplicity. - Expressiveness in complex domains, such as those in the humanities
Capturing “soft” issues such as temporality, subjectivity and vagueness.
CHARM is a cultural heritage abstract reference model that extends ConML.
From http://www.conml.org/ and http://www.charminfo.org/.
See also: http://www.conml.org/Resources/ TechSpec.aspx
F.6.2 Top-level
F.6.3 Key characteristics
ConML(+CHARM) is a lightweight foundational ontology. It has few foundational ontological commitments. The combined ontology is focussed on its domain.
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F.7 COSMO – COmmon Semantic MOdel
F.7.1 Overview
Developed with the goal of developing a foundation ontology that can serve to enable broad general Semantic Interoperability.
From https://en.wikipedia.org/wiki/Upper_ontology#COSMO.
See also: http://www.micra.com/F.7.2 Top-level
Taken from http://micra.com/COSMO/COSMO.owl
.
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F.8 Cyc
F.8.1 Overview
Cyc is a long-term artificial intelligence project that aims to assemble a comprehensive ontology and knowledge base that spans the basic concepts and rules about how the world works. Hoping to capture common sense knowledge, Cyc focuses on implicit knowledge that other AI platforms may take for granted. This is contrasted with facts one might find somewhere on the internet or retrieve via a search engine or Wikipedia. Cyc enables AI applications to perform human-like reasoning and be less “brittle” when confronted with novel situations.
The first version of OpenCyc was released in spring 2002 and contained only 6,000 concepts and 60,000 facts. The knowledge base was released under the Apache License.
From https://en.wikipedia.org/wiki/Cyc
See also: https://www.cyc.com/the-cyc-platformF.8.2 Top-level
The developers of Cyc did not believe that the top-most levels of the ‘ontology’ mattered a great deal. They thought the hard work is done lower down. And so, their top-level is very simple – with no real explicit foundational ontological commitments.
Cyc allows multiple inheritance (multiple is-a parents): for example, Intangible Stuff has Intangible Object and Stuff as parents. It uses Collection for higher order types.
F.8.3 Key characteristics
Cyc is a generic TLO. It intentionally has few ontological commitments.
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F.9 DC – Dublin Core
F.9.1 Overview
The Dublin Core schema is a small set of vocabulary terms that can be used to describe digital resources (video, images, web pages, etc.), as well as physical resources such as books or CDs, and objects like artworks.
From https://en.wikipedia.org/wiki/Dublin_Core
See also: http://dublincore.org/
F.9.2 Top-level
Figure 1 – the DCMI resource model
Figure 2 – the DCMI description set model
From https://www.dublincore.org/specifications/dublin-core/abstract-model/.
F.9.3. Key Characteristics
Dublin Core is a generic TLO with a focus on digital resources.
F.9.4 Relevant extracts
From https://www.dublincore.org/specifications/ dublin-core/abstract-model/
Extract 1 – The DCMI Vocabulary Model2.3 The DCMI Vocabulary Model
The abstract model of the vocabularies used in DC metadata descriptions is as follows:
- A vocabulary is a set of one or more terms. Each term is a member of one or more vocabularies.
- A term is a property (element), class, vocabulary encoding scheme, or syntax encoding scheme.
- Each property may be related to one or more classes by a has domain relationship. Where it is stated that a property has such a relationship with a class and the property is part of a property/value pair, it follows that the described resource is an instance of that class.
- Each property may be related to one or more classes by a has range relationship. Where it is stated that a property has such a relationship with a class and the property is part of a property/value pair, it follows that the value is an instance of that class.
- Each resource may be an instance of one or more classes.
- Each resource may be a member of one or more vocabulary encoding schemes.
Each class may be related to one or more other classes by a sub-class of relationship (where the two classes are defined such that all resources that are instances of the sub-class are also instances of the related class).
Each property may be related to one or more other properties by a sub-property of relationship. Where it is stated that such a relationship exists, the two properties are defined such that whenever the sub-property is part of a property/value pair describing a resource, it follows that the resource is also described using a second property/value pair made up of the property and the value.
Each syntax encoding scheme is a class (of literals).
Note that the word “vocabulary” is used here to refer specifically to a set of terms, a set in which the members are properties (elements), classes, vocabulary encoding schemes, and/or syntax encoding schemes.
Figure 3 – the DCMI vocabulary model
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F.10 DOLCE – Descriptive Ontology for Linguistic and Cognitive Engineering
F.10.1 Overview
Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE) is a foundational ontology designed in 2002 in the context of the WonderWeb EU project, developed by Nicola Guarino and his associates at the Laboratory for Applied Ontology (LOA). As implied by its acronym, DOLCE is oriented toward capturing the ontological categories underlying natural language and human common sense. DOLCE, however, does not commit to a strictly referentialist metaphysics related to the intrinsic nature of the world. Rather, the categories it introduces are thought of as cognitive artifacts, which are ultimately depending on human perception, cultural inprints, and social conventions. In this sense, they intend to be just descriptive (vs prescriptive) notions, which support the formal specification of domain conceptualizations.
From https://en.wikipedia.org/wiki/Upper_ ontology#DOLCE
See also: http://www.loa.istc.cnr.it/dolce/overview.html
F10.2 Top-Level
The top object is labelled ‘Particular’ indicating that all instances of this and its sub-types are particulars. One implication of this is that the ontology is first order – that there are no higher order ontologies.
F.10.3 Key characteristics
DOLCE is a well-documented heavyweight natural language ontology aiming to capture the ontological categories underlying natural language and human common sense.
F.10.4 Relevant extracts
None added.
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F.11. EMMO
F.11.1. Overview
The EMMO top-level is the group of fundamental axioms that constitute the philosophical foundation of the EMMO. Adopting a physicalistic/nominalistic perspective, the EMMO defines real world objects as 4D objects that are always extended in space and time (i.e. real-world objects cannot be spaceless nor timeless). For this reason, abstract objects, i.e. objects that do not extend in space and time, are forbidden in the EMMO. It has been instigated by materials science and provides the connection between the physical world, the experimental world (materials characterisation) and the simulation world (materials modelling).
From https://github.com/emmo-repo/EMMO
See also: https://materialsmodelling.com/2019/06/14/european-materials-modelling-ontology-emmo-release/
F.11.2. Top-level
F.11.3. Key characteristics
This is light-weight ontology.
F.11.4. Relevant extracts
Extracted from https://github.com/emmo-repo/EMMO
The Reductionistic perspective class uses the fundamental non-transitive parthood relation, called direct parthood, to provide a powerful granularity description of multiscale real world objects. The EMMO can in principle represents the Universe with direct parthood relations as a direct rooted tree up to its elementary constituents.
The Holistic perspective class introduces the concept of real world objects that unfold in time in a way that has a meaning for the EMMO user, through the definition of the classes Process and Participant.
The Phenomenic perspective class introduces the concept of real world objects that express of a recognisable pattern in space or time that impress the user. Under this class the EMMO categorises e.g. formal languages, pictures, geometry, mathematics and sounds. Phenomenic objects can be used in a semiotic process as signs.
The Physics perspective class introduces the concept of real world objects that have a meaning for the under applied physics perspective.
The semiotics module introduces the concepts of semiotics and the Semiosis process that has a Sign, an Object and an Interpreter as participants. This forms the basis in EMMO to represent e.g. models, formal languages, theories, information and properties.
EMMO relations
All EMMO relations are subrelations of the relations found in the two roots: mereotopological and semiotical. The relation hierarchy extends more vertically (i.e. more subrelations) than horizontally (i.e. less sibling relations), facilitating the categorisation and inferencing of individuals.
Imposing all relations to fall under mereotopology or semiotics is how the EMMO force the developers to respect its perspectives. Two entities are related only by contact or parthood (mereotopology) or by standing one for another (semiosis): no other types of relation are possible within the EMMO.
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F.12 FIBO – Financial Industry Business Ontology
F.12.1. Overview
The Financial Industry Business Ontology (FIBO) defines the sets of things that are of interest in financial business applications and the ways that those things can relate to one another.
From https://spec.edmcouncil.org/fibo/
F.12.2. Top-level
F.12.3. Key characteristics
We were unable to find adequate resources to assess this TLO.
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F.13. FrameNet
F.13.1 Overview
In computational linguistics, FrameNet is a project housed at the International Computer Science Institute in Berkeley, California which produces an electronic resource based on a theory of meaning called frame semantics. FrameNet reveals for example that the sentence "John sold a car to Mary" essentially describes the same basic situation (semantic frame) as "Mary bought a car from John", just from a different perspective. A semantic frame can be thought of as a conceptual structure describing an event, relation, or object and the participants in it. The FrameNet lexical database contains over 1,200 semantic frames, 13,000 lexical units (a pairing of a word with a meaning; polysemous words are represented by several lexical units) and 202,000 example sentences. FrameNet is largely the creation of Charles J. Fillmore, who developed the theory of frame semantics that the project is based on, and was initially the project leader when the project began in 1997.
From https://en.wikipedia.org/wiki/FrameNet
See also: https://framenet.icsi.berkeley.edu/fndrupal/
F.13.2. Top-level
FrameNet has noted several broad categories, including Event, Relation, State, Entity, Locale, and Process. Many frames inherit from these "top-level" categories, and from those inherited frames, many frames are related via relationships such as Using, Precedes, Subframe, etc. Further effort has extracted potential "top-level" frames which do not inherit from any other frames. These potential "top-level" frames (and all related frames) have been gathered as smaller groups. Finally, frames which neither inherit nor have inheritors are listed as "Singletons".
https://framenet.icsi.berkeley.edu/fndrupal/FrameLatticeList
F.13.3. Key characteristics
A natural language ontology.
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F.14. GFO – General Formal Ontology
F.14.1. Overview
Realistic ontology integrating processes and objects. It attempts to include many aspects of recent philosophy, which is reflected both in its taxonomic tree and its axiomatizations.
From https://en.wikipedia.org/wiki/Upper_ontology#General_Formal_Ontology_(GFO)
See also: https://www.onto-med.de/ontologies/gfo, https://en.wikipedia.org/wiki/General_formal_ontology
F.14.2. Top-level
F.14.3. Key characteristics
GFO is a well-documented heavyweight foundational ontology.
F.14.4. Relevant Extracts
From General Formal Ontology (GFO) – Part I: Basic Principles – Version 1.0 – No. 8 – July 2006
Extract 1 – Higher order
14.3 Instantiation and Categories
… Since we assume categories of arbitrary (finite) type, there can be arbitrarily long (finite) chains of iteration of the instantiation relation.
Extract 2 – First order – apart from one exception – persistants, a special category of second order
3.4 Basic Level
The basic level of GFO contains all relevant top-level distinctions and categories. One should distinguish between primitive categories (whose instances are individuals), and higher order categories. In the present document we consider primitive categories and the category of persistants (which is a special category of second order). These categories will be extended in the future using a number of non-primitive categories. Primitive categories and persistants of the basic level will be discussed further in the following sections and are the main content of the current report.
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F15. gist
F.15.1. Overview
gist is developed and supported by Semantic Arts. gist (not an acronym – it means to get the essence of) is a “minimalist upper ontology”. gist is targeted at enterprise information systems, although it has been applied to healthcare delivery applications. The major attributes of gist are:
- it is small (there are 140 classes and 127 properties)
- it is comprehensive (most enterprises will not find the need to create additional primitive classes, but will find that most of their classes can be defined and derived from gist)
- it is robust – all the classes descend from 12 primitive classes, which are mostly mutually disjoint. This aids a great deal in subsequent error detection. There are 1342 axioms, and it uses almost all of the DL constructs (it is SROIQ(D))
- it is concrete – most upper ontologies start with abstract philosophical concepts that users must commit to in order to use the ontology. Gist starts with concrete classes that most people already do, or reasonably could agree with, such as Person, Organization, Document, Time, UnitOfMeasure and the like)
- it is unambiguous – ambiguous terms (such as “term”) have been removed as they are often overloaded and confused. Also terms that frequently have different definitions at different enterprises (such as customer and order) have been removed, also to reduce ambiguity.
- it is understandable – in addition to being built on concrete, generally understood primitives, it is extremely modular. The 140 classes are implemented in 18 modular ontologies, each can easily be understood in its entirety, and each imports only the other modules that it needs.
From https://en.wikipedia.org/wiki/Upper_ontology#gist
See also: https://www.semanticarts.com/gist/
F.15.2. Top-level
F.15.3. Key characteristics
gist is a generic TLO. It clearly states it intentionally has few ontological commitments.
F.15.4 Relevant Extracts
Extract 1 – Avoids “abstract philosophical concepts”
“it is concrete – most upper ontologies start with abstract philosophical concepts that users must commit to in order to use the ontology. Gist starts with concrete classes that most people already do, or reasonably could agree with, such as Person, Organization, Document, Time, UnitOfMeasure and the like)”
Extract 2 – “Gist has extensive and fine grained disjointness at the highest level.”
“Gist has a small number of top level concepts from which everything else derives. And these concepts are not philosophical abstractions like endurants and perdurants, or qualia, they are normal terms whose definitions are quite close to what you already believe.
Gist has extensive and fine grained disjointness at the highest level. It turns out that in order for an upper ontology to help you avoid making logical errors in your derived enterprise or application ontology, it needs to make use of disjointness. Without disjointness, the reasoner does not find logic errors.”
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F.16. HQDM – High Quality Data Models
F.16.1. Overview
The High Quality Data Models (HQDM) Framework is a four-dimensional top-level ontology with extensional identity criteria that aims to support large scale data integration. As such it aims to ensure there is consistency among data created using the framework. The HQDM Framework is based on work developing and using ISO 15926 and lessons learnt from BORO, which influenced ISO 19526-2.
F.16.2. Top-level
F.16.3 Key characteristics
This is a well-documented heavyweight foundational ontology. It is an extensional ontology with a general unifying approach – illustrated in the journey in Figure 30. It draws heavily on ISO 15926-2 – and shares some of its technical background.
It introduces some novel ideas – such as interpreting possible worlds as branching.
F.16.4 Relevant extracts
Nothing added yet.
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F.17 IDEAS – International Defence Enterprise Architecture Specification
F.17.1 Overview
The upper ontology developed by the IDEAS Group is higher-order, extensional and 4D. It was developed using the BORO Method. The IDEAS ontology is not intended for reasoning and inference purposes; its purpose is to be a precise model of business.
From https://en.wikipedia.org/wiki/IDEAS_Group
F.17.2 Top-level
F17.3 Key characteristics
This is a well-documented heavyweight foundational ontology. It is an extensional ontology with a general unifying approach – illustrated in the journey in Figure 31. It is largely based upon BORO.
Figure 31 – IDEAS Stratification Journey – one stratum
F.17.4 Relevant extracts
Nothing added yet.
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F.18 IEC 62541
F.18.1 Overview
OPC Unified Architecture (OPC UA) is a machine to machine communication protocol for industrial automation developed by the OPC Foundation.
- Focus on communicating with industrial equipment and systems for data collection and control
- Open – freely available and implementable under GPL 2.0 license
- Cross-platform – not tied to one operating system or programming language
- Service-oriented architecture (SOA)
- Inherent complexity – the specification consists of 1250 pages in 14 documents
- Offers security functionality for authentication, authorization, integrity and confidentiality
- Integral information model, which is the foundation of the infrastructure necessary for information integration where vendors and organizations can model their complex data into an OPC UA namespace to take advantage of the rich service-oriented architecture of OPC UA. There are over 35 collaborations with the OPC Foundation currently. Key industries include pharmaceutical, oil and gas, building automation, industrial robotics, security, manufacturing and process control.
From https://en.wikipedia.org/wiki/OPC_Unified_Architecture.
See also: https://opcfoundation.org/developer-tools/specifications-unified-architecture
F.18.2 Top-level
ISO Standard document not available.
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F.19. IEC 63088
F19.1. Overview
IEC PAS 63088:2017(E) describes a reference architecture model in the form of a cubic layer model, which shows technical objects (assets) in the form of layers, and allows them to be described, tracked over their entire lifetime (or “vita”) and assigned to technical and/or organizational hierarchies. It also describes the structure and function of Industry 4.0 components as essential parts of the virtual representation of assets.
From https://webstore.iec.ch/publication/30082
F.19.2 Top-level
ISO Standard document not available.
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F.20 ISO 12006-3
F.20.1 Overview
ISO 12006-3:2007 specifies a language-independent information model which can be used for the development of dictionaries used to store or provide information about construction works. It enables classification systems, information models, object models and process models to be referenced from within a common framework.
From https://www.iso.org/standard/38706.html
See also: https://en.wikipedia.org/wiki/ISO_12006
F.20.2 Top-level
This appears to be more a base model for data than a representation of the domain.
F.20.3 Key characteristics
This is a generic top-level data model
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F.21 ISO 15926-2
F.21.1 Overview
ISO 15926-2:2003 specifies a conceptual data model for computer representation of technical information about process plants. [A] generic 4D model that can support all disciplines, supply chain company types and life cycle stages, regarding information about functional requirements, physical solutions, types of objects and individual objects as well as activities.
From https://www.iso.org/standard/29557.html
See also: https://en.wikipedia.org/wiki/ISO_15926
F.21.2. Top-level
F.21.3. Key characteristics
This is a well-documented heavyweight foundational ontology. It is an extensional ontology with a general unifying approach – illustrated in the journey in Figure 32. It is an ISO standard, whose development was partly influenced by the BORO.
It was developed in the 1990s using the EXPRESS data modelling language and it includes a meta-model to support the implementation of an RDL to enable domain ontologies to be developed in data as extensions.
Figure 32 – ISO 15926 Stratification Journey – one stratum
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F.22 KKO: KBpedia Knowledge Ontology
F.22.1 Overview
KBpedia is a comprehensive knowledge structure for promoting data interoperability and knowledge-based artificial intelligence, or KBAI. The KBpedia knowledge structure combines seven 'core' public knowledge bases – Wikipedia, Wikidata, schema.org, DBpedia, GeoNames, OpenCyc, and standard UNSPSC products and services – into an integrated whole. KBpedia's upper structure, or knowledge graph, is the KBpedia Knowledge Ontology. We base KKO on the universal categories and knowledge representation insights of the great 19th century American logician, polymath and scientist, Charles Sanders Peirce. The upper structure of the KBpedia Knowledge Ontology (KKO) is informed by the triadic logic and universal categories of Charles Sanders Peirce. This trichotomy, also the basis for his views on semiosis (or the nature of signs), was in Peirce's view the most primitive or reduced manner by which to understand and categorize things, concepts and ideas.
From https://kbpedia.org/docs/kko-upper-structure/
F.22.2 Top-level
F.22.3 Key characteristics
This is a natural language ontology influenced by Charles Peirce.
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F.23 KR Ontology – Knowledge Representation Ontology
F.23.1 Overview
The KR Ontology is defined in the book Knowledge Representation by John F. Sowa. Its categories have been derived from a synthesis of various sources, but the two major influences are the semiotics of Charles Sanders Peirce and the categories of existence of Alfred North Whitehead. The primitive categories are: Independent, Relative, or Mediating; Physical or Abstract; Continuant or Occurrent.
From http://www.jfsowa.com/ontology/toplevel.htm
F.23.2 Top-level
F.23.3 Key characteristics
KR is a heavyweight foundational ontology influenced by Charles Peirce.
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F. 24 MarineTLO: A Top-Level Ontology for the Marine Domain
F24.1 Overview
Is a top-level ontology, generic enough to provide consistent abstractions or specifications of concepts included in all data models or ontologies of marine data sources and provide the necessary properties to make this distributed knowledge base a coherent source of facts relating observational data with the respective spatiotemporal context and categorical (systematic) domain knowledge.
From https://en.wikipedia.org/wiki/Upper_ontology#MarineTLO.
See also: https://projects.ics.forth.gr/isl/MarineTLO/
F.24.2 Top-level
The model is formulated as an object-oriented semantic model, hence it has meta-class and class levels, as shown below.
The first broad division into persistent items and temporal phenomenon, looks similar to the endurantist’s continuant and occurrent distinction.
F.24.3 Key characteristics
Appears to have a lightweight top-level – with few ontological commitments.
Possibly an endurantist commitment.
F.24.4. Excerpts
“Formulation – It is an object-oriented semantic model, expressed to a form comprehensible to both documentation experts and information scientists while readily can be converted to machine-readable formats such as RDF Schema, OWL, etc”
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F. 25 MIMOSA CCOM – (Common Conceptual Object Model)
F.25.1 Overview
MIMOSA CCOM (Machinery Information Management Open Systems Alliance – Common Conceptual Object Model) serves as an information model for the exchange of asset information. Its core mission is to facilitate standards-based interoperability between systems: providing an XML model to allow systems to electronically exchange data.
From https://www.mimosa.org/mimosa-ccom/.
See also: https://en.wikipedia.org/wiki/OpenO%26M
F.25.2 Top-level
Class
OrganizationType
DataQualityType
PurchaseConditionType
Organization
DocumentType
ReadinessType
EffectiveStatusType
EngineeringStudyEntryType
SeverityLevelType
AgentRoleType
EngineeringStudyType
RegionType
AgentType
EventType
RequestType
AmbiguitySetType
GPSDatumType
SegmentType
AssetType
GPSElevationType
SignalProcessBlockType
Enumeration
GPSPrecisionType
SignalProcessStreamType
EnumerationItem
HealthLevelType
SiteType
UOMQuantity
HighlightType
Site
UnitOfMeasure
Document
SolutionPackageType
AttributeSetType
InfoSource
SourceDetectorType
AttributeType
InfoSourceType
StandardDataType
AverageSynchType
LifecycleStatusKind
TestComponentType
AverageType
LifecycleStatusType
TestType
AverageWeightType
LogicalConnectorType
TransducerAxisDirectionType
BLOBDataType
LogisticResourceType
TransducerType
BreakdownStructureType
MeasurementLocationType
WindowType
CalculationType
MeasurementSourceType
WorkStatusType
CCOMClass
MeshType
WorkManagementType
ChangePatternType
OrderedListType
WorkTaskType
ConnectionType
PostScalingType
CriticalityScaleType
PriorityLevelType
F.25.3 Key characteristics
A generic data model with no explicit top-level ontological commitment.
Note the significant number of higher order types in the list of entities (the entity types ending in ‘Type’).
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F.26 OWL – Web Ontology Language
F.26.1 Overview
The Web Ontology Language (OWL) is a family of knowledge representation languages for authoring ontologies. Ontologies are a formal way to describe taxonomies and classification networks, essentially defining the structure of knowledge for various domains: the nouns representing classes of objects and the verbs representing relations between the objects.
F.26.2 Top-level
F.26.3 Key characteristics
A generic top-level data model with a lightweight (or no) foundational ontological commitments.
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F.27 ProtOn – PROTo ONtology
F.27.1 Overview
PROTON (PROTo ONtology) is a basic subsumption hierarchy which provides coverage of most of the upper-level concepts necessary for semantic annotation, indexing, and retrieval.
From https://en.wikipedia.org/wiki/Upper_ontology#PROTON.
See also: https://ontotext.com/documents/proton/Proton-Ver3.0B.pdf
F.27.2Top-level
F.27.3 Key characteristics
A natural language ontology.
F.27.4 Relevant extracts
Extracts from: https://ontotext.com/documents/proton/Proton-Ver3.0B.pdf
Extract 1 – Design principles
The PROTON ontology contains about 500 classes and 150 properties, providing coverage of
the general concepts necessary for a wide range of tasks, including semantic annotation,
indexing, and retrieval. The design principles can be summarized as follows:
- domain-independence;
- lightweight logical definitions;
- alignment with popular metadata standards;
- good coverage of named entity types and concrete domains (i.e. modelling of concepts such as people, organizations, locations, numbers, dates, addresses, etc.); and
good coverage of instance data in Linked Open Data Reasonable view Fact Forge.
The ontology is encoded in a fragment of OWL Lite and split into four modules: System, Top,
Extent, and KM (Knowledge Management). A snapshot of the PROTON class hierarchy is
given on Figure 1, showing the Top and the Extent modules.
Extract 2 – PROTON is relatively un-restrictive
1. Design Rationales
PROTON is designed as a lightweight upper-level ontology for use in Knowledge
Management and Semantic Web applications. The above mission statement has two important
implications:
PROTON is relatively un-restrictive. It specifies only a hierarchy of classes and domain and range of properties defined within it, but it does not impose any other restrictions on the meaning of the classes and properties.
PROTON is not precise in some aspects, for instance regarding the conceptualization of space and time. This is partly because proper models for these aspects would require using a logical apparatus, which is beyond the limits acceptable for many of the tasks to which we wish to apply PROTON (e.g. queries and management of huge datasets/knowledge bases); and partly because it is very hard to craft strict and precise conceptualizations for these concepts, which are adequate for a wide range of domains and applications.
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F.28 Schema.org
F..28.1 Overview
Schema.org is a collaborative, community activity with a mission to create, maintain, and promote schemas for structured data on the Internet, on web pages, in email messages, and beyond.
From https://en.wikipedia.org/wiki/Schema.org
See also: https://schema.org/
F.28.2 Top-level
Thing
- Action
- CreativeWork
- Event
- Intangible
- MedicalEntity
- Organization
- Person
- Place
- Product
F.28.3 Key characteristics
A generic top-level data model with lightweight (or no) foundational ontological commitments.
F.28.4 Relevant extracts
Extracts from: https://schema.org/docs/datamodel.html
Extract 1 – Data Model Design
The data model used is very generic and derived from RDF Schema (which in turn was derived from CycL, see History section for details ...).
- We have a set of types, arranged in a multiple inheritance hierarchy where each type may be a sub-class of multiple types.
-
We have a set of properties:
- each property may have one or more types as its domains. The property may be used for instances of any of these types.
- each property may have one or more types as its ranges. The value(s) of the property should be instances of at least one of these types.
The decision to allow multiple domains and ranges was purely pragmatic. While the computational properties of systems with a single domain and range are easier to understand, in practice, this forces the creation of a lot of artifical types, which are there purely to act as the domain/range of some properties.
Like many other systems, the schema presented here can be extended (with a few types like Class and Property and a few properties like domainIncludes and rangeIncludes) to allow for reflection, i.e., for the schema to be represented in terms of itself.
Extract 2 – Not intended to be a 'global ontology'
The type hierarchy presented on this site is not intended to be a 'global ontology' of the world. When founded in 2011 it was strictly focussed around the types of entities for which the project's founders (Microsoft, Yahoo!, Google and Yandex), could reasonably expect to provide some special treatment for via search engines. As the project has evolved, introducing more community collaboration and extension mechanisms, its scope has expanded gradually. However it is still the case that schema.org is not intended as a universal ontology. We expect it to be used alongside other vocabulary that shares our basic datamodel and our use of underlying standards like JSON-LD, Microdata and RDFa.
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F.29 SENSUS
F.29.1 Overview
We have constructed SENSUS, a 70,000-node terminology taxonomy, as a framework into which additional knowledge can be placed. SENSUS is an extension and reorganization of WordNet.
From https://www.isi.edu/natural-language/projects/ONTOLOGIES.html.
F.29.2 Top-level
See Wordnet.
F.29.3 Key characteristics
A natural language ontology based upon Wordnet.
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F.30 SKOS
F.30.1Overview
SKOS is an area of work developing specifications and standards to support the use of knowledge organization systems (KOS) such as thesauri, classification schemes, subject heading systems and taxonomies within the framework of the Semantic Web.
From: https://www.w3.org/2004/02/skos/.
See also: https://en.wikipedia.org/wiki/Simple_Knowledge_Organization_System
F.30.2 Top-level
Element categories
The principal element categories of SKOS are concepts, labels, notations, semantic relations, mapping properties, and collections. The associated concepts are listed in the table below.
SKOS Vocabulary Organized by Theme
Concepts
Labels & Notation
Documentation
Semantic Relations
Mapping Properties
Collections
Concept
prefLabel
note
broader
broadMatch
Collection
ConceptScheme
altLabel
changeNote
narrower
narrowMatch
orderedCollection
inScheme
hiddenLabel
definition
related
relatedMatch
member
hasTopConcept
notation
editorialNote
broaderTransitive
closeMatch
memberList
topConceptOf
example
narrowerTransitive
exactMatch
historyNote
semanticRelation
mappingRelation
scopeNote
From https://en.wikipedia.org/wiki/Simple_Knowledge_Organization_System#Element_categories
F.30.3 Key characteristics
A natural language ontology that has a structure similar to a thesaurus.
F.30.4 Relevant extracts
Extracts from: https://www.w3.org/TR/skos-reference/
Extract 1 – Similar structure to thesauri, etc.
Many knowledge organization systems, such as thesauri, taxonomies, classification schemes and subject heading systems, share a similar structure, and are used in similar applications. SKOS captures much of this similarity and makes it explicit, to enable data and technology sharing across diverse applications.
Extract 2 – Higher order concepts
9. Concept Collections
9.1. Preamble
SKOS concept collections are labeled and/or ordered groups of SKOS concepts.
Collections are useful where a group of concepts shares something in common, and it is convenient to group them under a common label, or where some concepts can be placed in a meaningful order.
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F.31 SUMO
F.31.1 Overview
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 which has a LISP-like syntax. A mapping from WordNet synsets to SUMO has been defined. Initially, SUMO was focused on meta-level concepts (general entities that do not belong to a specific problem domain), 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. SUMO is organized for interoperability of automated reasoning engines.
From https://en.wikipedia.org/wiki/Upper_ontology#SUMO_(Suggested_Upper_Merged_Ontology)
See also: http://www.adampease.org/OP/, https://en.wikipedia.org/wiki/Suggested_Upper_Merged_Ontology
F.31.2 Top-level
F.31.3 Key characteristics
A natural language ontology that supports higher order types (see ‘class’ and ‘set’ below)
F.31.4 Relevant Extracts
Extract 1 – Collection, Class and Set
(documentation Collection EnglishLanguage "Collections have members like Classes, but, unlike Classes, they have a position in space-time and members can be added and subtracted without thereby changing the identity of the Collection. Some examples are toolkits, football teams, and flocks of sheep.")
(documentation Class EnglishLanguage "Classes differ from Sets in three important respects. First, Classes are not assumed to be extensional. That is, distinct Classes might well have exactly the same instances. Second, Classes typically have an associated `condition' that determines the instances of the Class. So, for example, the condition `human' determines the Class of Humans. Note that some Classes might satisfy their own condition (e.g., the Class of Abstract things is Abstract) and hence be instances of themselves. Third, the instances of a class may occur only once within the class, i.e. a class cannot contain duplicate instances.")
(documentation Set EnglishLanguage "A SetOrClass that satisfies extensionality as well as other constraints specified by some choice of set theory. Sets differ from Classes in two important respects. First, Sets are extensional – two Sets with the same elements are identical. Second, a Set can be an arbitrary stock of objects. That is, there is no requirement that Sets have an associated condition that determines their membership. Note that Sets are not assumed to be unique sets, i.e. elements of a Set may occur more than once in the Set.")
NB: Collections of collections are fusion of their members and so do not ascend a type.
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F.32 TMRM/TMDM – Topic Map Reference/Data Models
F.32.1 Overview
A topic map is a standard for the representation and interchange of knowledge, with an emphasis on the findability of information. Topic maps were originally developed in the late 1990s as a way to represent back-of-the-book index structures so that multiple indexes from different sources could be merged. However, the developers quickly realized that with a little additional generalization, they could create a meta-model with potentially far wider application. The ISO standard is formally known as ISO/IEC 13250:2003.
A topic map represents information using
- topics, representing any concept, from people, countries, and organizations to software modules, individual files, and events,
- associations, representing hypergraph relationships between topics, and
- occurrences, representing information resources relevant to a particular topic.
Topic maps are similar to concept maps and mind maps in many respects, though only topic maps are ISO standards. Topic maps are a form of semantic web technology similar to RDF.
From https://en.wikipedia.org/wiki/Topic_map
See also: https://www.isotopicmaps.org/tmrm/, https://www.iso.org/standard/40757.html (ISO/IEC 13250-5:2015 Information technology – Topic Maps – Part 5: Reference model), https://www.isotopicmaps.org/sam/sam-model/ (Topic Maps – Data Model)
F.32.2 Top-level
F.32.3 Key characteristics
A generic ontology with little ontological commitment. Allows higher order levels. Potentially non-well bounded (no notion of particulars). Instantiation is possibly gunky and junky across topics. Sub-class is possibly gunky and junky across topics. No explicit commitment to mereological relations.
F.32.4 Relevant Extracts
Extract 1 (https://en.wikipedia.org/wiki/Topic_map#Ontology_and_merging)
Ontology and merging
Topics, associations, and occurrences can all be typed, where the types must be defined by the one or more creators of the topic map(s). The definitions of allowed types is known as the ontology of the topic map.
Extract 2 (https://www.isotopicmaps.org/tmrm/ – http://www.isotopicmaps.org/TMRM/TMRM-7.0/tmrm7.pdf)
The Topic Maps Reference Model - 5 Ontological Commitments
This Standard deliberately leaves undefined the methods whereby subject proxies are derived or created. No specific mechanism of subject identification is inherent in or mandated by this Standard, nor does it predefine any subject proxies.
NOTE 1 Any subject proxy design choices would be specific to a particular application domain and would exclude equally valid alternatives that might be appropriate or necessary in the contexts of various requirements.
Two types of relationships, sub (subclass of) and isa (instance of), are defined. These predicates are always interpreted relative to a given map m:
a) Two proxies c, c0 can be in a subclass-superclass relationship, subm _ m × m. In such a case, the same relationship can be stated either c is a subclass of c0 or c0 is a superclass of c.
subm is supposed to be reflexive and transitive. Reflexive implies that any proxy is a subclass of itself, regardless whether the proxy is used as a class in the map or not: x subm x for all x 2 m.
Transitive implies that if a proxy c is a subclass of another, c0, and that subclasses c00, then c is also a subclass of c00, i.e. if c subm c0 and c0 subm c00 then also c subm c00 must be true.NOTE 2 Circular subclass relationships may exist in a map.
b) Two proxies a, c can be in an isa relationship, isam _ m × m. In such a case, the same relationship can be stated either a is an instance of c or c is the type of a.
The isa relationship is supposed to be non-reflexive, i.e. x isam x for no x 2 m, so that no proxy can be an instance of itself. Additionally, whenever a proxy a is an instance of another c, then a is an instance of any superclass of 😄 if x isam c and c subm c0, then x isam c0 is true.NOTE 3 This Standard does not mandate any particular way of representing such relationships inside a map. One option is to model such a relationship simply with a property using a certain key (say type). An alternative way is to provide a proxy for each such relationship. Such relationship proxies could, for example, have properties whose keys are instance and class, or respectively subclass and superclass.
Extract 3 (https://www.isotopicmaps.org/sam/sam-model/#sect-pubsubj)
7 Core subject identifiers
…
7.2 The type-instance relationship
A topic type is a subject that captures some commonality in a set of subjects. Any subject that belongs to the extension of a particular topic type is known as an instance of that topic type. A topic type may itself be an instance of another topic type, and there is no limit to the number of topic types a subject may be an instance of.
The type-instance relationship is not transitive. That is, if B is an instance of the type A, and C is an instance of the type B, it does not follow that C is an instance of A.
…
7.3 The supertype-subtype relationship
The supertype-subtype relationship is the relationship between a more general type (the supertype) and a specialization of that type (the subtype). If B is the subtype of A, it follows that every instance of B is also an instance of A. The converse is not necessarily true. A type may have any number of subtypes and supertypes.
The supertype-subtype relationship is transitive, which means that if B is a subtype of A, and C a subtype of B, C is also a subtype of A.
NOTE:
Loops in this relationship are allowed, and should be interpreted to mean that the sets of instances for all types in the loop are the same. This does not, however, necessarily imply that the types are the same.
NOTE:
The semantics of the supertype-subtype relationship implies the existence of further type-instance and supertype-subtype relationships in addition to those explicitly represented by associations in the topic map. This part of ISO/IEC13250 does not require associations to be created for inferred relationships.
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F.33 UFO
F.33.1 Overview
Incorporates developments from GFO, DOLCE and the Ontology of Universals underlying OntoClean in a single coherent foundational ontology.
F.33.2 Top-level
F.33.3 Key characteristics
UFO is a well-documented heavyweight foundational ontology that have evolved over time.
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F.34 UMBEL
F.34.1 Overview
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. Since UMBEL is an open-source extract of the OpenCyc knowledge base, it can also take advantage of the reasoning capabilities within Cyc.
F.34.2 Top-level
See Cyc.
F.34.3 Key characteristics
A natural language ontology with a generic top-level.
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F.35 UML
F.35.1 Overview
The Unified Modeling Language (UML) is a general-purpose, developmental, modeling language in the field of software engineering that is intended to provide a standard way to visualize the design of a system.
The creation of UML was originally motivated by the desire to standardize the disparate notational systems and approaches to software design. It was developed by Grady Booch, Ivar Jacobson and James Rumbaugh at Rational Software in 1994 – 1995, with further development led by them through 1996.
In 1997, UML was adopted as a standard by the Object Management Group (OMG), and has been managed by this organization ever since. In 2005, UML was also published by the International Organization for Standardization (ISO) as an approved ISO standard. Since then the standard has been periodically revised to cover the latest revision of UML.
From https://en.wikipedia.org/wiki/Unified_Modeling_Language
See also: http://uml.org/, https://www.iso.org/standard/32620.html (ISO Standard NB v1.4.2)
F.35.2 Top-level
From OMG® Unified Modeling Language® (OMG UML®) – Version 2.5.1 (Normative URL: https://www.omg.org/spec/UML/)
F.35.3 Key characteristics
A generic data model with a focus on describing computer systems. Supports multiple inheritance and classification, though as these are typically not feasible in programming languages, these are not usually used. Typically, first order, though limited functionality through generalisation sets (powertypes) to move up to higher orders (see extract below).
F.35.4 Relevant Extracts
From OMG® Unified Modeling Language® (OMG UML®) – Version 2.5.1 (Normative URL: https://www.omg.org/spec/UML/)
Extract 1
6.3 On the Semantics of UML
6.3.1 Models and What They Model
A model is always a model of something. The thing being modeled can generically be considered a system within some domain of discourse. The model then makes some statements of interest about that system, abstracting from all the details of the system that could possibly be described, from a certain point of view and for a certain purpose. For an existing system, the model may represent an analysis of the properties and behavior of the system. For a planned system, the model may represent a specification of how the system is to be constructed and behave.
A UML model consists of three major categories of model elements, each of which may be used to make statements about different kinds of individual things within the system being modeled (termed simply “individuals” in the following). These categories are:
- Classifiers. A classifier describes a set of objects. An object is an individual with a state and relationships to other objects. The state of an object identifies the values for that object of properties of the classifier of the object. (In some cases, a classifier itself may also be considered an individual; for example, see the discussion of static structural features in sub clause 9.4.3.)
- Events. An event describes a set of possible occurrences. An occurrence is something that happens that has some consequence with regard to the system.
- Behaviors. A behavior describes a set of possible executions. An execution is a performance of a set of actions (potentially over some period of time) that may generate and respond to occurrences of events, including accessing and changing the state of objects. (As described in sub clause 13.2, behaviors are themselves modeled in UML as kinds of classifiers, so that executions are essentially modeled as objects. However, for the purposes of the present discussion, it is clearer to consider behaviors and executions to be in a separate semantic category than classifiers and objects.)
Extract 2
9.7 Generalization Sets
9.7.1 Summary
GeneralizationSet provides a way to group Generalizations into orthogonal dimensions. A GeneralizationSet may be associated with a Classifier called its powertype. These techniques provide additional expressive power for organizing classification hierarchies.
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9.7.3 Semantics
Generalizations may be grouped to represent orthogonal dimensions of generalization. Each group is represented by a GeneralizationSet. The generalizationSet property designates the GeneralizationSets to which the Generalization belongs. All of the Generalizations in a particular GeneralizationSet shall have the same general Classifier.
The isCovering property of GeneralizationSet specifies whether the specific Classifiers of the Generalizations in that set are complete, in the following sense: if isCovering is true, then every instance of the general Classifier is an instance of (at least) one of the specific Classifiers. The isDisjoint property specifies whether the specific Classifiers of the Generalizations in that set may overlap, in the following sense: if isDisjoint is true, then no instance of any of the specific Classifiers may also be an instance of any other of the specific Classifiers. By default, both properties are false.
A GeneralizationSet may optionally be associated with a Classifier called its powertype. This means that for every Generalization in the GeneralizationSet, the specializing Classifier is uniquely associated with an instance of the powertype, i.e., there is a 1-1 correspondence between instances of the powertype and specializations in the GeneralizationSet, so that the powertype instances and the corresponding Classifiers may be treated as semantically equivalent. How this semantic equivalence is implemented and how its integrity is maintained is not defined within the scope of UML.
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F.36 UMLS – Unified Medical Language System
F.36.1 Overview
The Unified Medical Language System (UMLS) is a compendium of many controlled vocabularies in the biomedical sciences (created 1986). It provides a mapping structure among these vocabularies and thus allows one to translate among the various terminology systems; it may also be viewed as a comprehensive thesaurus and ontology of biomedical concepts. UMLS further provides facilities for natural language processing. It is intended to be used mainly by developers of systems in medical informatics.
From https://en.wikipedia.org/wiki/Unified_Medical_Language_System
See also: https://www.nlm.nih.gov/research/umls/index.html
F.36.2 Top-level
F.36.3 Key characteristics
A natural language ontology.
F36.4 Relevant extracts
Semantic Network
The Semantic Network consists of (1) a set of broad subject categories, or Semantic Types, that provide a consistent categorization of all concepts represented in the UMLS Metathesaurus, and (2) a set of useful and important relationships, or Semantic Relations, that exist between Semantic Types. This section of the documentation provides an overview of the Semantic Network, and describes the files of the Semantic Network. Sample records illustrate structure and content of these files.
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F.37 WordNet
F.37.1 Overview
WordNet is a lexical database of semantic relations between words in more than 200 languages. WordNet links words into semantic relations including synonyms, hyponyms, and meronyms. The synonyms are grouped into synsets with short definitions and usage examples. WordNet can thus be seen as a combination and extension of a dictionary and thesaurus.
From https://en.wikipedia.org/wiki/WordNet
See also: https://wordnet.princeton.edu/, https://en.wikipedia.org/wiki/Upper_ontology#WordNet
F.37.2 Top-level
F.37.3 Key characteristics
A natural language ontology.
F.37.4 Relevant extracts
From: What is WordNet? (https://wordnet.princeton.edu/)
Extract 1 – Type-instance distinction
WordNet distinguishes among Types (common nouns) and Instances (specific persons, countries and geographic entities). Thus, armchair is a type of chair, Barack Obama is an instance of a president. Instances are always leaf (terminal) nodes in their hierarchies.
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F.38 YAMATO – Yet Another More Advanced Top-level Ontology
F.38.1 Overview
YAMATO is developed by Riichiro Mizoguchi, formerly at the Institute of Scientific and Industrial Research of the University of Osaka, and now at the Japan Advanced Institute of Science and Technology. Major features of YAMATO are:
- an advanced description of quality, attribute, property, and quantity,
- an ontology of representation,
- an advanced description of processes and events,
- the use of a theory of roles.
YAMATO has been extensively used for developing other, more applied, ontologies such as a medical ontology, an ontology of gene, an ontology of learning/instructional theories, an ontology of sustainability science, and an ontology of the cultural domain.
From https://en.wikipedia.org/wiki/Upper_ontology#YAMATO_(Yet_Another_More_Advanced_Top_Ontology)
F.38.2 Top-level
F.38.3 Key characteristics
This is a first order ontology - vide the top object called ‘Particular’. For the endurant horizontal aspect it separates occurrents and continuants - vide continuant-occurrent division of physical.
F.38.4 Relevant extracts
From: Mizoguchi, R. (2010). YAMATO: Yet another more advanced top-level ontology. In Proceedings of the sixth Australasian ontology workshop (pp. 1-16).
Extract 1 – Strict single inheritance
It adopts strict single inheritance in is-a hierarchy which is organized according to the rigid definition of is-a and instance-of relations based on the set membership with the notion of essential property.
Extract 2 – Genuine multiple inheritance
For the cases where genuine multiple inheritance is necessary, Hozo prepares IS-A relation which is nothing to do with identity problem of instances but only with property inheritance like subclass of relation in OWL. It may be used only when is-a relation already exists between the two types of interest.
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