In recent years has the word ontology - besides its philosophical meaning - come to mean a kind of knowledge organization system. An ontology has been defined as “a specification of a representational vocabulary for a shared domain of discourse - definitions of classes, relations, functions, and other objects ” (Gruber, 1993a).


The word "ontology" has a long history in philosophy, in which it refers to the subject of existence. In the context of knowledge sharing Gruber (1993a)use the term ontology to mean a specification of a conceptualization. That is, an ontology is a description (like a formal specification of a program) of the concepts and relationships that can exist for an agent or a community of agents. This definition is consistent with the usage of ontology as set-of-concept-definitions, but more general.


Jermey & Browne (2004, glossary)."Specification of a conceptualisation of a knowledge domain. An ontology is a controlled vocabulary that describes objects and the relations between them in a formal way, and has a grammar for using the vocabulary terms to express something meaningful within a specified domain of interest. The vocabulary is used to make queries and assertions. Ontological commitments are agreements to use the vocabulary in a consistent way for knowledge sharing".


"Ontologies resemble faceted taxonomies but use richer semantic relationships among terms and attributes, as well as strict rules about how to specify terms and relationships. Because ontologies do more than just control a vocabulary, they are thought of as knowledge representation. The oft-quoted definition of ontology is "the specification of one's conceptualization of a knowledge domain." (Lombardi, 2004).



“… Information systems as simple as catalogs, in which each product type has a unique code (e.g. the item number), have been dubbed ‘ontologies’. A catalog is, in a sense, the ontology of the things a company sells. A slightly more complex information system may provide simple natural language texts and allow string matching. Glossaries are information systems that provide natural language descriptions of terms, thus imposing some structure on the text (indexing by terms). Thesauri are standardized information systems that provide, in addition to descriptions of terms, also relations to other more general or more specific terms within a common hierarchy. The fields of knowledge representation, database development, and object-oriented software engineering all employ ontologies conceived as taxonomies in which properties of more general classes are inherited by the more specific ones. Frame-based systems provide, in addition to taxonomic structure, relations between objects and restrictions on what and how classes of objects can be related to each other. Finally, the most expressive and complex information system ontologies use the axioms of full first order, higher order, or modal logic. All these types of information systems satisfy Gruber’s definition, and all are now common bedfellows under the rubric of ‘ontology.’ (Smith & Welty, 2001).


"Uschold [17] writes that an ontology:

. . . is often conceived as a set of concepts (e.g. entities, attributes, processes), their definitions and their inter-relationships

. . . An ontology may take a variety of forms, but will necessarily include a vocabulary of terms and some specification of their meaning (i.e. definitions). It may be:

The term ontology have been used in information studies by, among others, Ding, 2001, Soergel, 1999, Soergel et al., 2004, Svenonius 2000, Vickery, 1997 and Wang & Wang, 1995.


Soergel et al. (2004) provides information on how to reengineer thesauri to rich ontologies.


Table 1: Statements and rules of a hypothetical ontology versus the information given in the ERIC thesaurus (broader term (BT), related term (RT))

(from Soergel et al, 2004)

Eric Thesaurus

Hypothetical ontology

reading instruction

BT instruction

RT reading
RT learning standards 

reading ability

BT ability
RT reading
RT perception


reading instruction 

<isa> instruction

<hasDomain> reading

governedBy> learning standards

reading ability

<isa> ability

<hasDomain> reading
supportedBy> perception


Rule 1

Instruction in a domain should consider ability in that domain:

X shouldConsider Y

IF X <isa (type of)> instruction AND X <hasDomain> W

AND Y <isa> ability AND Y <hasDomain> W

yields: : The designer of reading instruction should also consider reading ability.

Rule 2

X shouldConsider Z
IF X <
shouldConsider> Y
> Z

yields: The designer of reading instruction should also consider perception.



Table 2: AGROVOC relationships compared with more differentiated relationships of a hypothetical ontology (narrower term (NT), broader term (BT))

(from Soergel et al, 2004)


Hypothetical Ontology

Undifferentiated hierarchical relationships in AGROVOC


NT cow milk

NT milk fat


NT cow milk 

Cheddar cheese

BT cow milk

Differentiated relationships in an ontology



<includesSpecific> cow milk

<containsSubstance> milk fat 


<hasComponent> cow milk*

Cheddar cheese

<<madeFrom> cow milk


Rule 1

Part X <mayContainSubstance> Substance Y

IF Animal W <hasComponent> Part X

AND Animal W <ingests> Substance Y


Rule 2

Food Z <containsSubstance> Substance Y

IF Food Z <madeFrom> Part X

AND Part X <containsSubstance> Substance Y 




Soergel et al. (2004) also state what is, in their opinion, the limitations of existing KOS and the potential benefits of future generation KOSs:



"The limitations of existing KOS can be summarized as follows:
  • Lack of conceptual abstraction: thesauri and other traditional KOSs are collections of terms (generic or domain-specific), ordered in a polyhierarchic lattice structure or a monohierarchic tree structure and interlinked with some very broad and basic relationships. The distinction between a concept (meaning) and its lexicalizations (words) is not made consistently, if at all, in such a system, and as such it does not reflect the ways humans understand the world in terms of meaning and language.
  • Limited semantic coverage: most thesauri do not differentiate concepts into types (such as living organism, substance, or process) and have a very limited set of relationships between concepts, distinguishing only between hierarchical relationships, i.e. NT/BT, and associative relationships, i.e. RT. These very rudimentary relationships are not powerful enough to guide a user in meaningful information discovery on the Web or to support inference. They do not reflect the conceptual relationships that people know and that can be used by a system to suggest concepts for expanding the query or making it more specific. Examples:
    • The relation between cow and its part cow milk is expressed as NT rather than the more semantically expressive relation <hasComponent>, so a user who wants to expand the query hierarchically (search for all concepts narrower than cow as well) could not distinguish between searching for all cow parts or searching for all varieties of cow;
    • the relation between mad cow disease and the animal it afflicts, cow, is expressed using RT instead of the more semantically precise relation <afflicts>, so the user could not easily assemble a list of all cow diseases and search for recent occurrences;
    • mad cow disease and one of its symptoms anorexia would also be related using RT rather than the more semantically expressive relation <hasSymptom>.
    The concept relations provided by most thesauri force all relations into the two broad categories, hierarchical and associative. Too often the semantic relationships captured in this way are ambiguous and poorly defined. The generalization/specialization relations defined in most thesauri are not adequately developed to be of use for semantic description and discovery of Web resources. Thus there is a need for a richer and more powerful set of relationships.
  • Lack of consistency: since the relationships in thesauri lack precise semantics, they are applied inconsistently, both creating ambiguity in the interpretation of the relationships and resulting in an overall internal semantic structure that is irregular and unpredictable. Many of the NT/BT hierarchical relationships could, for example, be resolved to the non-hierarchical RT relationship, and vice versa.
  • Limited automated processing: traditionally thesauri were designed for indexing and query formulation by people and not for automated processing. The ambiguous semantics that characterizes many thesauri makes them unsuitable for automated processing. "  (Soergel et al., 2004).



"Potential benefits of future generation KOSs

For emerging KOSs to satisfy user needs, they must improve both information organization and retrieval in a way that was not possible with traditional KOSs. The following potential benefits are expected from such systems:
  • Unique identifiers and formal semantics: the explicit definition of concepts and relations in an ontology allows a unique identifier to be assigned to each concept. As each concept and relation is explicitly defined as a unique entity, the ontology lends itself to semantic formalization.
  • Internal consistency: another benefit of explicit semantics is the achievement of internal structural consistency in the expression of knowledge due to the possibility of applying integrity constraints.
  • Interoperability: clear semantics enables interoperability among different KOSs since corresponding concepts within different KOSs would have the same unique identifier, irrespective of the actual lexicalizations used to express those concepts. Semantic interoperability promotes sharing and reuse of knowledge.
  • Greater information integration: interoperability among different KOSs makes it possible for machines to recognize and analyze intended meaning of terms from disparate vocabularies. This is possible by using structured meta-information and formal knowledge description such as agreed-upon metadata schemas, controlled domain vocabularies, and taxonomies. The ability to integrate terminologies from different sources maximizes the value of investment made in the ontology.
  • Inferencing capability: new KOSs have the potential for expressing knowledge beyond what is present in the structure of the system. Unlike traditional KOSs where both concepts and relations are underspecified and very few, if any, axiomatic rules exist, the facts (concepts and relations) and rules that can be derived from an ontology have the expressive capabilities that allow for reasoning.
  • Automated information processing: new KOSs create improved potential to discover relevant information from different sources by exploring patterns and filtering information using conceptual connections represented in the ontology. This enables question-answering from one or more databases or, using natural language processing (see next bullet), from text.
  • Natural language processing (NLP) support: offers the possibility of providing a direct reply to a search question that is expressed in natural language, using the enhanced relationships and semantics in an ontology, instead of only returning a list of relevant documents.
  • Search query understanding: using NLP and semantic processing, a system can understand a query posed in natural language, determine the concepts involved and, where useful, create a Boolean query.
  • Concept-based search: an ontology can provide context-aware search capabilities specific to the area of interest.
  • Integrated information search/browse support: text mining on the Web (Web mining) through meaning-oriented access, dynamic organization of information with the possibility for cross-domain links are feasible with emerging KOSs.
  • Search query expansion: the enhancement, extension, and disambiguation of user query terms become possible with the addition of enriched domain- and context-specific information. " (Soergel et al., 2004).






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See also: Ontology & metaphysics (Epistemological lifeboat); Semantic web; Topic Maps




Birger Hjørland

Last edited: 24-05-2007





  1. An ontology has been defined as “a specification of a representational vocabulary for a shared domain of discourse - definitions of classes, relations, functions, and other objects” and as a specification of a conceptualization. Discuss whether these two definitions also apply to other kinds of semantic tools?

  2. What is (if anything) the principal difference between an ontology and other kinds of semantic tools (such as taxonomies and thesauri).