# Ontology versus terminology, from the perspective of ontologists

Leila Zemmouchi Ghomari, Abdessamed Réda Ghomari

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## To cite this version:

Leila Zemmouchi Ghomari, Abdessamed Réda Ghomari. Ontology versus terminology, from the perspective of ontologists. International Journal of Web Science, 2013, 1 (4), pp.315. 10.1504/IJWS.2012.052531. hal-04565157

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Ontology versus terminology, from the perspective of
ontologists

Leila Zemmouchi-Ghomari and
Abdessamed Réda Ghomari*

LMCS Laboratory,
Ecole Nationale Supérieure d'Informatique (ESI) (ex. INI),
BP 68M, 16309, Algiers, Algeria
E-mail: l_zemmouchi@esi.dz
E-mail: l_ghomari@umbb.dz
E-mail: a_ghomari@esi.dz
*Corresponding author

**Abstract:** According to research teams' experiments in ontology engineering field, building ontologies is much more time-consuming and costly than building terminologies with regard to ontology complexity and formality, two major differences between these types of resources. In the meantime, there are already many consensual terminologies for numerous knowledge domains available on the web, consequently these knowledge representations can constitute a great contribution in saving time and money in ontology building process field since terminologies can be considered as preliminary attempts to model particular domains by their respective experts. Though before performing such a reuse, it is essential to distinguish clearly which type of resource we are dealing with and therefore decide for its suitability for our purposes or not. This paper will focus on two leading points: on the one hand, a comparison between ontology and terminology on the basis of several features such as: basic principles, methodological aspects and formality perspective and on the other hand, an overview of terminology reuse in ontology building process throughout some successful reuse cases.

**Keywords:** ontology versus terminology; terminology reuse; web science.

**Reference** to this paper should be made as follows: Zemmouchi-Ghomari, L. and Ghomari, A.R. (2012) ‘Ontology versus terminology, from the perspective of ontologists’, *Int. J. Web Science*, Vol. 1, No. 4, pp.315–331.

**Biographical notes:** Leila Zemmouchi-Ghomari is a Computer Science Engineer. She received her Research Master degree in Computer Science from ESI, Algiers, in 2009. She has been a PhD student since 2009, member of LMCS (the Laboratory of Systems Design Methodologies). Her research interests focus on ontology engineering, knowledge engineering and semantic web. She is currently a Lecturer at UMBB: M’hamed Bouguerra Boumerdes University.

Abdessamed Réda Ghomari received his PhD in Computer Science from ESI, Algiers, in 2008. During 2000–2012, he is ‘Information System Management’ Team Head at LMCS (the Laboratory of Systems Design Methodologies). His research interests focus on cooperative knowledge engineering, Enterprise 2.0 and collective intelligence. He is currently Associate Professor at ESI: National Superior School of Computer Science.
# Ontology versus terminology, from the perspective of ontologists

Leila Zemmouchi-Ghomari and
Abdessamed Réda Ghomari*

LMCS Laboratory,
Ecole Nationale Supérieure d'Informatique (ESI) (ex. INI),
BP 68M, 16309, Algiers, Algeria
E-mail: l_zemmouchi@esi.dz
E-mail: l_ghomari@umbb.dz
E-mail: a_ghomari@esi.dz
*Corresponding author

**Abstract:** According to research teams' experiments in ontology engineering field, building ontologies is much more time-consuming and costly than building terminologies with regard to ontology complexity and formality, two major differences between these types of resources. In the meantime, there are already many consensual terminologies for numerous knowledge domains available on the web, consequently these knowledge representations can constitute a great contribution in saving time and money in ontology building process field since terminologies can be considered as preliminary attempts to model particular domains by their respective experts. Though before performing such a reuse, it is essential to distinguish clearly which type of resource we are dealing with and therefore decide for its suitability for our purposes or not. This paper will focus on two leading points: on the one hand, a comparison between ontology and terminology on the basis of several features such as: basic principles, methodological aspects and formality perspective and on the other hand, an overview of terminology reuse in ontology building process throughout some successful reuse cases.

**Keywords:** ontology versus terminology; terminology reuse; web science.

**Reference** to this paper should be made as follows: Zemmouchi-Ghomari, L. and Ghomari, A.R. (2012) ‘Ontology versus terminology, from the perspective of ontologists’, *Int. J. Web Science*, Vol. 1, No. 4, pp.315–331.

**Biographical notes:** Leila Zemmouchi-Ghomari is a Computer Science Engineer. She received her Research Master degree in Computer Science from ESI, Algiers, in 2009. She has been a PhD student since 2009, member of LMCS (the Laboratory of Systems Design Methodologies). Her research interests focus on ontology engineering, knowledge engineering and semantic web. She is currently a Lecturer at UMBB: M'hamed Bouguerra Boumerdes University.

Abdessamed Réda Ghomari received his PhD in Computer Science from ESI, Algiers, in 2008. During 2000–2012, he is ‘Information System Management’ Team Head at LMCS (the Laboratory of Systems Design Methodologies). His research interests focus on cooperative knowledge engineering, Enterprise 2.0 and collective intelligence. He is currently Associate Professor at ESI: National Superior School of Computer Science.
# 1 Introduction

There is a clear consensus that both ontologies and terminologies allow researchers to process, store and share the increasing knowledge derived from their experiments (Jimeno-Yepes et al., 2009). This statement, among many others (Guarino, 2011; Happel and Seedorf, 2006; Gomez Perez and Benjamins, 1999; Uschold and Gruninger, 1996; Cabré, 1999; Wright and Budin, 2001; Van Elst and Abecker, 2002), underlines the crucial need of these two knowledge representation forms and explain why researchers are invested in building and reusing them to achieve the objectives mentioned above and even more.

Unfortunately, there is a lack of clarity when discussing the terms: ontology and terminology. Some reasons for this imprecision are:

* the large spectrum of functionality that gets grouped under the umbrella terms 'ontology' and 'terminology'

* abundance of the literature devoted to clarifying these two types of resources

* controversy about their respective definitions (Gillam et al., 2005; Bennett and Fellbaum, 2006; Guarino and Schneider, 2002; Ceusters, 2001; Sager, 1990; Roche et al., 2009).

This misunderstanding slows considerably the progression of research works concerned by ontologies and terminologies as claimed by Van Rees (2003). Resources found on the web, with regard to their multiplicity, are able, in almost all cases, to satisfy researchers' needs if they know how to recognise resources characteristics and hence avoid being in the situation of 'too much or too little knowledge'.

Nevertheless, if a 'missing' formal ontology is required, terminologies over their diversity, their number and their availability on the web, can be of reliable support in saving time and money in building the future ontology.

The purpose of this paper is to address primarily the issue of misperception between terminology and ontology and by what means these knowledge representations can benefit from each other's characteristics, explicitly, terminology reuse in ontology building process.

The paper has been organised in the following way; Section 2 will deal with a global comparison between ontologies and terminologies based on two perspectives: basic principles and methodological aspects. Section 3 will examine different levels of formality in ontologies and terminologies, plus some aspects having an impact on formality effectiveness. The following section will focus on some case studies of reusing terminologies in ontology building process and some guidelines on how to perform such a reuse. Finally, the last section will highlight our opinion on terminology and ontology roles in supporting researchers' works in general.
# 1 Introduction

There is a clear consensus that both ontologies and terminologies allow researchers to process, store and share the increasing knowledge derived from their experiments (Jimeno-Yepes et al., 2009). This statement, among many others (Guarino, 2011; Happel and Seedorf, 2006; Gomez Perez and Benjamins, 1999; Uschold and Gruninger, 1996; Cabré, 1999; Wright and Budin, 2001; Van Elst and Abecker, 2002), underlines the crucial need of these two knowledge representation forms and explain why researchers are invested in building and reusing them to achieve the objectives mentioned above and even more.

Unfortunately, there is a lack of clarity when discussing the terms: ontology and terminology. Some reasons for this imprecision are:

* the large spectrum of functionality that gets grouped under the umbrella terms 'ontology' and 'terminology'

* abundance of the literature devoted to clarifying these two types of resources

* controversy about their respective definitions (Gillam et al., 2005; Bennett and Fellbaum, 2006; Guarino and Schneider, 2002; Ceusters, 2001; Sager, 1990; Roche et al., 2009).

This misunderstanding slows considerably the progression of research works concerned by ontologies and terminologies as claimed by Van Rees (2003). Resources found on the web, with regard to their multiplicity, are able, in almost all cases, to satisfy researchers' needs if they know how to recognise resources characteristics and hence avoid being in the situation of 'too much or too little knowledge'.

Nevertheless, if a 'missing' formal ontology is required, terminologies over their diversity, their number and their availability on the web, can be of reliable support in saving time and money in building the future ontology.

The purpose of this paper is to address primarily the issue of misperception between terminology and ontology and by what means these knowledge representations can benefit from each other's characteristics, explicitly, terminology reuse in ontology building process.

The paper has been organised in the following way; Section 2 will deal with a global comparison between ontologies and terminologies based on two perspectives: basic principles and methodological aspects. Section 3 will examine different levels of formality in ontologies and terminologies, plus some aspects having an impact on formality effectiveness. The following section will focus on some case studies of reusing terminologies in ontology building process and some guidelines on how to perform such a reuse. Finally, the last section will highlight our opinion on terminology and ontology roles in supporting researchers' works in general.
## 2 Comparison between terminology and ontology

### 2.1 Ontology and terminology: basic principles

For the purpose of clarity, this subsection will be divided into five subsets; each subset will deal successively with: concept, ontology, term, terminology paradigms and relation types in both resources.

#### 2.1.1 Concept definition

'Concept' is the building block of ontology; it is defined as "unit of knowledge created by a unique combination of characteristics" (ISO 1087-1, 2000). One of the most shared concept definitions from the point of view of either ontologists or terminologists is illustrated by Odgen and Richards' (1923) semantic triangle (Figure 1):

* *Reference/thought/concept/signified/intension:* Meaning of a term or simply a definition. According to ISO 1087-1 (2000) "intension is a set of characteristics which makes up the concept".

* *Symbol/term/signifier/designation:* The sound-image of the concept, "representation of a concept by a sign which denotes it" (ISO 1087-1, 2000).

* *Referent/extension/object:* A concept is reflected by an object or a set of objects, an object is defined as "anything perceivable or conceivable" (ISO 1087-1, 2000).

<figure><img src="image_1.png" /><figcaption>**Figure 1** The semantic triangle (see online version for colours)</figcaption></figure>

Source: Odgen and Richards (1923)

However, Cointet and Chavalaris (2008) pointed out that "a concept can be polysomic" and therefore may be used by several scientific communities. This interpretation contrasts with that of some other terminologists such as Gillam et al. (2005) who argues that "a concept is an instantiation of a term related to a particular domain", in other words, a concept is defined when a signification (semantic) is given to a term to avoid terms polysemy. Other terminologists (Sager and Kageura, 1994) consider the concept much more broadly since "it refers to four categories: entities, activities, relations and characteristics".
## 2 Comparison between terminology and ontology

### 2.1 Ontology and terminology: basic principles

For the purpose of clarity, this subsection will be divided into five subsets; each subset will deal successively with: concept, ontology, term, terminology paradigms and relation types in both resources.

#### 2.1.1 Concept definition

'Concept' is the building block of ontology; it is defined as "unit of knowledge created by a unique combination of characteristics" (ISO 1087-1, 2000). One of the most shared concept definitions from the point of view of either ontologists or terminologists is illustrated by Odgen and Richards' (1923) semantic triangle (Figure 1):

* *Reference/thought/concept/signified/intension:* Meaning of a term or simply a definition. According to ISO 1087-1 (2000) "intension is a set of characteristics which makes up the concept".

* *Symbol/term/signifier/designation:* The sound-image of the concept, "representation of a concept by a sign which denotes it" (ISO 1087-1, 2000).

* *Referent/extension/object:* A concept is reflected by an object or a set of objects, an object is defined as "anything perceivable or conceivable" (ISO 1087-1, 2000).

<figure><img src="image_3.png" /><figcaption>**Figure 1** The semantic triangle (see online version for colours)</figcaption></figure>

Source: Odgen and Richards (1923)

However, Cointet and Chavalaris (2008) pointed out that "a concept can be polysomic" and therefore may be used by several scientific communities. This interpretation contrasts with that of some other terminologists such as Gillam et al. (2005) who argues that "a concept is an instantiation of a term related to a particular domain", in other words, a concept is defined when a signification (semantic) is given to a term to avoid terms polysemy. Other terminologists (Sager and Kageura, 1994) consider the concept much more broadly since "it refers to four categories: entities, activities, relations and characteristics".
From the above opinions on concept definitions, we can deduce that the debate is still open. However, in our opinion, there is no major difference between these points of views that is merely due to:

* Difference of perspective, whether we consider the concept pertaining to a particular domain or considering the concept in a global way.

* Difference of purpose use, for example in the context of wordnet¹, a concept is polysomic which is useful from the perspective of a user who wants to obtain all possible concept definitions however in the case of a medical thesaurus like Mesh² a concept definition must be much more precise and unambiguous by delivering a unique interpretation.

### 2.1.2 Ontology definition

From the perspective of ontologists, the most consensual ‘ontology’ definition is Gruber’s definition (Gruber, 1993): “an explicit specification of conceptualization” followed by a more detailed one from Borst and Akermans (1997): “a formal specification of a shared conceptualization” which underlines the formality and the consensual nature of the ontology.

Uschold (1998) provided minimal features that must be included in ontologies, namely: a vocabulary of terms and a specification of their meanings. This specification includes definitions and how concepts are interrelated; relations impose a structure on the field and limit the possible interpretations of the terms.

This point of view has been shared more recently by Berners-Lee et al. (2006) who agreed that ontology can be either formal or informal, obviously, the advantage of formality is what makes the ontology machine-readable, and consequently allows deeper reasoning over web resources.

The previous definitions show how wide the spectrum of ontology paradigm is, as confirmed by Green and Rosemann (2004), when he claimed that the understanding of ontology in terms of its scope, granularity and purpose varies significantly.

While a variety of ontology definitions have been suggested, this paper will use the definition proposed by Uschold (1998) who considers it a rigorously formal structure.

To our knowledge, ‘term’ and ‘terminology’ definitions are not as controversial as ‘concept’ and ‘ontology’ definitions, a rather logical consequence of the maturity of the terminological research field attested by ISO documents on terminology standards and terminology work (ISO 1087-1, 2000; ISO 704, 2000; ISO 704, 2009).

### 2.1.3 Term definition

According to ISO 1087-1 (2000), a ‘term’ is defined as “verbal designation of a general concept in a specific subject field”. There are several term categories, like: simple term (containing only one root), complex term (containing one or more roots), borrowed term (taken from another language or from another subject field) and neoterm (new term coined for a given concept).

Another categorisation of terms relies on term acceptability rating. The following ratings are common: preferred term (primary term for a given concept), admitted term (synonym of a preferred term), deprecated term (undesired) and obsolete term (no longer in use).
From the above opinions on concept definitions, we can deduce that the debate is still open. However, in our opinion, there is no major difference between these points of views that is merely due to:

* Difference of perspective, whether we consider the concept pertaining to a particular domain or considering the concept in a global way.

* Difference of purpose use, for example in the context of wordnet¹, a concept is polysomic which is useful from the perspective of a user who wants to obtain all possible concept definitions however in the case of a medical thesaurus like Mesh² a concept definition must be much more precise and unambiguous by delivering a unique interpretation.

### 2.1.2 Ontology definition

From the perspective of ontologists, the most consensual ‘ontology’ definition is Gruber’s definition (Gruber, 1993): “an explicit specification of conceptualization” followed by a more detailed one from Borst and Akermans (1997): “a formal specification of a shared conceptualization” which underlines the formality and the consensual nature of the ontology.

Uschold (1998) provided minimal features that must be included in ontologies, namely: a vocabulary of terms and a specification of their meanings. This specification includes definitions and how concepts are interrelated; relations impose a structure on the field and limit the possible interpretations of the terms.

This point of view has been shared more recently by Berners-Lee et al. (2006) who agreed that ontology can be either formal or informal, obviously, the advantage of formality is what makes the ontology machine-readable, and consequently allows deeper reasoning over web resources.

The previous definitions show how wide the spectrum of ontology paradigm is, as confirmed by Green and Rosemann (2004), when he claimed that the understanding of ontology in terms of its scope, granularity and purpose varies significantly.

While a variety of ontology definitions have been suggested, this paper will use the definition proposed by Uschold (1998) who considers it a rigorously formal structure.

To our knowledge, ‘term’ and ‘terminology’ definitions are not as controversial as ‘concept’ and ‘ontology’ definitions, a rather logical consequence of the maturity of the terminological research field attested by ISO documents on terminology standards and terminology work (ISO 1087-1, 2000; ISO 704, 2000; ISO 704, 2009).

### 2.1.3 Term definition

According to ISO 1087-1 (2000), a ‘term’ is defined as “verbal designation of a general concept in a specific subject field”. There are several term categories, like: simple term (containing only one root), complex term (containing one or more roots), borrowed term (taken from another language or from another subject field) and neoterm (new term coined for a given concept).

Another categorisation of terms relies on term acceptability rating. The following ratings are common: preferred term (primary term for a given concept), admitted term (synonym of a preferred term), deprecated term (undesired) and obsolete term (no longer in use).
### 2.1.4 Terminology definition

‘Terminology’ is “a set of designations or terms belonging to one special language” (ISO 1087-1, 2000). Terminology thus denotes a discipline, which systematically studies the designating of concepts particular to one or more subject fields through research and analysis of terms in context, for the purpose of documenting and promoting consistent usage.

Ceusters (2001) stated that terminology is different from lexicology in the sense that only the terms pertaining to a specific domain are considered.

### 2.1.5 Relation types in ontologies/terminologies

An ontology structure requires the included concepts to be connected in formal relations (Bennett and Fellbaum, 2006), namely: subsumption, part hood, identity, connection, dependence, constitution and instantiation:

* *Subsumption* leads to the basic hierarchy of the ontology, where all concepts directly or indirectly are linked to the top concept: ‘Thing’ and is defined as follows:
  $\forall x f(x) \rightarrow r(x)$, Every instance of the sub concept is necessarily an instance of the super concept (Guarino and Schneider, 2002).

* *Part hood* and temporary part hood could be used to indicate when an individual is a member of a collective. If an individual is only a member for a short time, for example an individual in a queue, the temporary part hood relation could be used to indicate that the relation exists only for a certain time period (Wood and Galton, 2008).

* Part hood can be regarded as partial *identity*: the entities on which something is dependent are part of its identity (Schneider, 2003).

* *Connection* is a symmetric and non-transitive relation that is reflexive in all cases it applies at all. Its underlying idea is that of immediate neighbourhood in space and time (Schneider, 2003).

* *Constitution* is a kind of non-extensional composition (Schneider, 2003).

* *Instantiation* is the relation between an individual and a universal that exists in this individual (Laarman and Kurtev, 2009).

Likewise, terminologies include several types of relations as reported in ‘ISO 1087’, which are:

* *Hierarchical relation* between two concepts which may be either a generic relation (one concept is more specific than the other), e.g., ‘car’ and ‘vehicle’ or a partitive (one concept is a part of the other concept) relation, e.g., ‘tyre’ and ‘car’.

* *Associative relation* is a pragmatic relation between two concepts having a non-hierarchical thematic connection based on experience, e.g., ‘teaching’ and ‘education’.
### 2.1.4 Terminology definition

‘Terminology’ is “a set of designations or terms belonging to one special language” (ISO 1087-1, 2000). Terminology thus denotes a discipline, which systematically studies the designating of concepts particular to one or more subject fields through research and analysis of terms in context, for the purpose of documenting and promoting consistent usage.

Ceusters (2001) stated that terminology is different from lexicology in the sense that only the terms pertaining to a specific domain are considered.

### 2.1.5 Relation types in ontologies/terminologies

An ontology structure requires the included concepts to be connected in formal relations (Bennett and Fellbaum, 2006), namely: subsumption, part hood, identity, connection, dependence, constitution and instantiation:

* *Subsumption* leads to the basic hierarchy of the ontology, where all concepts directly or indirectly are linked to the top concept: ‘Thing’ and is defined as follows:
  $\forall x f(x) \rightarrow r(x)$, Every instance of the sub concept is necessarily an instance of the super concept (Guarino and Schneider, 2002).

* *Part hood* and temporary part hood could be used to indicate when an individual is a member of a collective. If an individual is only a member for a short time, for example an individual in a queue, the temporary part hood relation could be used to indicate that the relation exists only for a certain time period (Wood and Galton, 2008).

* Part hood can be regarded as partial *identity*: the entities on which something is dependent are part of its identity (Schneider, 2003).

* *Connection* is a symmetric and non-transitive relation that is reflexive in all cases it applies at all. Its underlying idea is that of immediate neighbourhood in space and time (Schneider, 2003).

* *Constitution* is a kind of non-extensional composition (Schneider, 2003).

* *Instantiation* is the relation between an individual and a universal that exists in this individual (Laarman and Kurtev, 2009).

Likewise, terminologies include several types of relations as reported in ‘ISO 1087’, which are:

* *Hierarchical relation* between two concepts which may be either a generic relation (one concept is more specific than the other), e.g., ‘car’ and ‘vehicle’ or a partitive (one concept is a part of the other concept) relation, e.g., ‘tyre’ and ‘car’.

* *Associative relation* is a pragmatic relation between two concepts having a non-hierarchical thematic connection based on experience, e.g., ‘teaching’ and ‘education’.
* *Sequential associative relation based on spatial or temporal proximity, e.g., 'production' and 'consumption'.*

* *Temporal sequential relation involving events in time e.g., 'spring' and 'summer'.*

* *Causal associative relation involving causal relation between concepts, e.g., 'murder' and 'death'.*

When considering both relation types' classifications, it seems obvious that there are a number of equivalences or at least overlaps between these categories, such as: subsumption = hierarchical and connection = sequential associative.

Further correspondences are summarised in Table 1 adapted from Examples of horizontal relations³ identified in ontologies (Hulden, 2007) and definitions of some related relations in terminologies (ISO 704, 2009):

<table><caption>**Table 1** Examples of relation types in ontologies/terminologies</caption><thead><tr><td>Relation types in ontologies</td><td>Equivalent relation types in terminologies</td><td>Examples of relations</td></tr></thead><tbody><tr><td>Partitive</td><td>Partitive (container-contained)</td><td>Has part/part of Has member/member of</td></tr><tr><td>Causative</td><td>Causative (cause-effect)</td><td>Causes/caused by</td></tr><tr><td>Temporal</td><td>Temporal sequential</td><td>Precedes/follows</td></tr><tr><td>Instrumental</td><td>Associative (activity-tool)</td><td>Is means for/performed by means of</td></tr><tr><td>Objective</td><td>Associative (producer-product)</td><td>Has author/is author of</td></tr></tbody></table>

*Source:* Adapted from Hulden (2007) and ISO 704 (2009)

From what precedes, we can attest that relation types in ontologies and terminologies are relatively similar and therefore cannot be considered as a distinctive feature between them.

This common aspect increases the confusion in the perception of either ontologies or terminologies. Because, semantic relations between terms reflect meaning relatedness, which is essentially conceptual and ontologies activate on the conceptual level as opposed to terminologies which operate on the linguistic level. Consequently, both notions appear to be very close to one another and are often wrongly used interchangeably (Huang et al., 2010).

## 2.2 Ontology and terminology: methodological aspects

We carry on our comparative study from another perspective by reporting differences and similarities identified in the literature with regard to the main features in building each type of resource (Table 2). Depth coverage of all aspects related to knowledge representation building methodologies is beyond the scope of this paper; nonetheless, we attempt to summarise a correct and concise comparison of this broad and complex research area.
* *Sequential associative relation based on spatial or temporal proximity, e.g., 'production' and 'consumption'.*

* *Temporal sequential relation involving events in time e.g., 'spring' and 'summer'.*

* *Causal associative relation involving causal relation between concepts, e.g., 'murder' and 'death'.*

When considering both relation types' classifications, it seems obvious that there are a number of equivalences or at least overlaps between these categories, such as: subsumption = hierarchical and connection = sequential associative.

Further correspondences are summarised in Table 1 adapted from Examples of horizontal relations³ identified in ontologies (Hulden, 2007) and definitions of some related relations in terminologies (ISO 704, 2009):

<table><caption>**Table 1** Examples of relation types in ontologies/terminologies</caption><thead><tr><td>Relation types in ontologies</td><td>Equivalent relation types in terminologies</td><td>Examples of relations</td></tr></thead><tbody><tr><td>Partitive</td><td>Partitive (container-contained)</td><td>Has part/part of Has member/member of</td></tr><tr><td>Causative</td><td>Causative (cause-effect)</td><td>Causes/caused by</td></tr><tr><td>Temporal</td><td>Temporal sequential</td><td>Precedes/follows</td></tr><tr><td>Instrumental</td><td>Associative (activity-tool)</td><td>Is means for/performed by means of</td></tr><tr><td>Objective</td><td>Associative (producer-product)</td><td>Has author/is author of</td></tr></tbody></table>

*Source: Adapted from Hulden (2007) and ISO 704 (2009)*

From what precedes, we can attest that relation types in ontologies and terminologies are relatively similar and therefore cannot be considered as a distinctive feature between them.

This common aspect increases the confusion in the perception of either ontologies or terminologies. Because, semantic relations between terms reflect meaning relatedness, which is essentially conceptual and ontologies activate on the conceptual level as opposed to terminologies which operate on the linguistic level. Consequently, both notions appear to be very close to one another and are often wrongly used interchangeably (Huang et al., 2010).

## 2.2 Ontology and terminology: methodological aspects

We carry on our comparative study from another perspective by reporting differences and similarities identified in the literature with regard to the main features in building each type of resource (Table 2). Depth coverage of all aspects related to knowledge representation building methodologies is beyond the scope of this paper; nonetheless, we attempt to summarise a correct and concise comparison of this broad and complex research area.
<table><caption>**Table 2** Comparison between terminology and ontology based on methodological aspects</caption><thead><tr><th>Terminology</th><th>Ontology</th></tr></thead><tbody><tr><td><strong>Intended users</strong><br>Terminologies are intended for human users, where domain experts constitute the major user group.<br><br><strong>Why do we need terminologies?</strong><br>To support at least these functions (Rector 1999):<ul><li>Information integration, indexing, retrieval, decision support, quality assurance</li><li>Messaging between software systems, linking information systems</li><li>Reporting, providing the official returns in which coding system is required.</li></ul><br><br><strong>Three dimensions need to be considered when developing terminologies:</strong><ul><li>the communicative dimension (the use or the purpose of the terminology)</li><li>the linguistic dimension (forms of the terms)</li><li>the cognitive dimension (meaning of terms) (Ceusters, 2001).</li></ul><br><br><strong>Necessary steps for developing terminologies (Peñas et al., 2001):</strong><ol><li>Terms extraction from texts and terms elicitation from experts</li><li>Terms weighting based on statistics</li><li>Terms selection (most terminologies have also adopted usage of what is called preferred terms however 'preferred terms' are merely an academic artefact rather than a reality) (Moerkerke and Ceusters 2000)</li><li>Terms linking.</li></ol></td><td><strong>Intended users</strong><br>Ontologies are mainly developed for knowledge sharing between (both human and artificial) agents.<br><br><strong>Why do we need ontologies?</strong><ul><li>To support retrieval and integration of information from different, distributed sources (Staab et al., 2000)</li><li>To facilitate communications among people and organisations to achieve interoperability</li><li>To save time and effort in building similar knowledge systems (sharing)</li><li>To provide background knowledge for query expansion or query rewriting (Bodner and Song, 1996)</li><li>To make domain assumptions explicit.</li></ul><br><br><strong>Five dimensions need to be considered when developing ontologies:</strong><ul><li>ontology purpose</li><li>ontology intended users</li><li>ontology intended uses (main scenarios in which the ontology will be used) (Gomez Perez and Benjamins, 1999)</li><li>ontology scope (ontology coverage and granularity)</li><li>ontology level of formality.</li></ul><br><br><strong>Necessary steps for developing ontologies (Noy and McGuinness, 2001):</strong><ol><li>Determine the domain and scope of the ontology</li><li>Consider reusing existing ontologies and non-ontological resources such as terminologies and documents</li><li>Extract and enumerate important terms (candidate terms) in the ontology</li><li>Deduce concepts from selected terms</li><li>Define the concepts and the concepts' hierarchy (top down approach, bottom-up approach, combination approach)</li><li>Define the properties of concepts, slots (intrinsic and extrinsic ones)</li><li>Define the facets of the slots (slot cardinality, slot-value type, domain and range of a slot)</li><li>Create instances.</li></ol></td></tr></tbody></table>
<table><caption>**Table 2** Comparison between terminology and ontology based on methodological aspects</caption><thead><tr><th>Terminology</th><th>Ontology</th></tr></thead><tbody><tr><td><strong>Intended users</strong><br>Terminologies are intended for human users, where domain experts constitute the major user group.<br><br><strong>Why do we need terminologies?</strong><br>To support at least these functions (Rector 1999):<ul><li>Information integration, indexing, retrieval, decision support, quality assurance</li><li>Messaging between software systems, linking information systems</li><li>Reporting, providing the official returns in which coding system is required.</li></ul><br><br><strong>Three dimensions need to be considered when developing terminologies:</strong><ul><li>the communicative dimension (the use or the purpose of the terminology)</li><li>the linguistic dimension (forms of the terms)</li><li>the cognitive dimension (meaning of terms) (Ceusters, 2001).</li></ul><br><br><strong>Necessary steps for developing terminologies (Peñas et al., 2001):</strong><ol><li>Terms extraction from texts and terms elicitation from experts</li><li>Terms weighting based on statistics</li><li>Terms selection (most terminologies have also adopted usage of what is called preferred terms however 'preferred terms' are merely an academic artefact rather than a reality) (Moerkerke and Ceusters 2000)</li><li>Terms linking.</li></ol></td><td><strong>Intended users</strong><br>Ontologies are mainly developed for knowledge sharing between (both human and artificial) agents.<br><br><strong>Why do we need ontologies?</strong><ul><li>To support retrieval and integration of information from different, distributed sources (Staab et al., 2000)</li><li>To facilitate communications among people and organisations to achieve interoperability</li><li>To save time and effort in building similar knowledge systems (sharing)</li><li>To provide background knowledge for query expansion or query rewriting (Bodner and Song, 1996)</li><li>To make domain assumptions explicit.</li></ul><br><br><strong>Five dimensions need to be considered when developing ontologies:</strong><ul><li>ontology purpose</li><li>ontology intended users</li><li>ontology intended uses (main scenarios in which the ontology will be used) (Gomez Perez and Benjamins, 1999)</li><li>ontology scope (ontology coverage and granularity)</li><li>ontology level of formality.</li></ul><br><br><strong>Necessary steps for developing ontologies (Noy and McGuinness, 2001):</strong><ol><li>Determine the domain and scope of the ontology</li><li>Consider reusing existing ontologies and non-ontological resources such as terminologies and documents</li><li>Extract and enumerate important terms (candidate terms) in the ontology</li><li>Deduce concepts from selected terms</li><li>Define the concepts and the concepts' hierarchy (top down approach, bottom-up approach, combination approach)</li><li>Define the properties of concepts, slots (intrinsic and extrinsic ones)</li><li>Define the facets of the slots (slot cardinality, slot-value type, domain and range of a slot)</li><li>Create instances.</li></ol></td></tr></tbody></table>
<table><caption>**Table 2** Comparison between terminology and ontology based on methodological aspects (continued)</caption><thead><tr><th>Terminology</th><th>Ontology</th></tr></thead><tbody><tr><td>Terms extraction is always done from texts, a step based on multiple techniques and strategies: linguistic, statistical and hybrid.</td><td>Concepts extraction is achieved by using several sources of knowledge such as knowledge experts and a broad pallet of resources regardless of being ontological or non-ontological ones.</td></tr><tr><td>Maintenance tools<br>Most terminologies maintenance tools provide limited or no means for an explicit representation of knowledge (Fisseha, 2003).</td><td>Maintenance tools<br>Ontology maintenance tools provide systems with powerful knowledge representation languages and inference mechanisms that allow formal consistency checks, inference of new knowledge, and a more user-friendly interaction (Fisseha, 2003).</td></tr><tr><td>Limitations of terminologies:<ul><li>They cannot take advantage of the knowledge implicitly available in the terms (Ceusters, 2001)</li><li>Finding specific terms requires a priori knowledge by the user on how the system is structured, reclassification of the terms, following different criteria, cannot be achieved (Ceusters, 2001)</li><li>Non-formal terminologies are designed to be used by humans: (Gruber 1993)</li></ul></td><td>Limitations of ontologies:<ul><li>The limitations of ontologies for representing the meaning due to language limitation expressivity</li><li>Computational efficiency is not always achieved, example with owl full which is not guaranteed to be decidable and consequently do not allow reasoning on these ontological structures</li><li>Top level ontologies, as complex and heavyweight ontologies are not suitable for real world applications</li><li>Ontologies are representations of particular view points of real world knowledge of their developers</li><li>As the size of the ontologies grows and applications developed become more complex, inconsistency becomes inevitable in the design and development of ontologies (Deng et al., 2007).</li></ul></td></tr><tr><td><ol><li>Little possibility of reuse due to inherent semantic ambiguity and lack of the explicitness of their semantics.</li><li>Difficulties in the diversity of their representational form (no common representational language)</li><li>Developed for human use. They lack expressive mechanisms to represent, maintain, and reason about complex knowledge in an explicit form-interpretation is left for humans.</li><li>Numerous terminologies have been found to suffer from major inconsistencies; examples being SNOMED CT (Ceusters et al., 2004), the Terminologia Anatomica (TA) (Rosse, 2001) and the NCI Thesaurus (Ceusters and Smith, 2005), Rector (1999) thinks that it is the result of a failure in separating language and concept representation.</li></ol></td><td></td></tr></tbody></table>
<table><caption>**Table 2** Comparison between terminology and ontology based on methodological aspects (continued)</caption><thead><tr><th>Terminology</th><th>Ontology</th></tr></thead><tbody><tr><td>Terms extraction is always done from texts, a step based on multiple techniques and strategies: linguistic, statistical and hybrid.</td><td>Concepts extraction is achieved by using several sources of knowledge such as knowledge experts and a broad pallet of resources regardless of being ontological or non-ontological ones.</td></tr><tr><td>Maintenance tools<br>Most terminologies maintenance tools provide limited or no means for an explicit representation of knowledge (Fisseha, 2003).</td><td>Maintenance tools<br>Ontology maintenance tools provide systems with powerful knowledge representation languages and inference mechanisms that allow formal consistency checks, inference of new knowledge, and a more user-friendly interaction (Fisseha, 2003).</td></tr><tr><td>Limitations of terminologies:<ul><li>They cannot take advantage of the knowledge implicitly available in the terms (Ceusters, 2001)</li><li>Finding specific terms requires a priori knowledge by the user on how the system is structured, reclassification of the terms, following different criteria, cannot be achieved (Ceusters, 2001)</li><li>Non-formal terminologies are designed to be used by humans: (Gruber 1993)</li></ul></td><td>Limitations of ontologies:<ul><li>The limitations of ontologies for representing the meaning due to language limitation expressivity</li><li>Computational efficiency is not always achieved, example with owl full which is not guaranteed to be decidable and consequently do not allow reasoning on these ontological structures</li><li>Top level ontologies, as complex and heavyweight ontologies are not suitable for real world applications</li><li>Ontologies are representations of particular view points of real world knowledge of their developers</li><li>As the size of the ontologies grows and applications developed become more complex, inconsistency becomes inevitable in the design and development of ontologies (Deng et al., 2007).</li></ul></td></tr><tr><td><ol><li>Little possibility of reuse due to inherent semantic ambiguity and lack of the explicitness of their semantics.</li><li>Difficulties in the diversity of their representational form (no common representational language)</li><li>Developed for human use. They lack expressive mechanisms to represent, maintain, and reason about complex knowledge in an explicit form-interpretation is left for humans.</li><li>Numerous terminologies have been found to suffer from major inconsistencies; examples being SNOMED CT (Ceusters et al., 2004), the Terminologia Anatomica (TA) (Rosse, 2001) and the NCI Thesaurus (Ceusters and Smith, 2005), Rector (1999) thinks that it is the result of a failure in separating language and concept representation.</li></ol></td><td></td></tr></tbody></table>
<table><caption>**Table 2** Comparison between terminology and ontology based on methodological aspects (continued)</caption><thead><tr><th>Terminology</th><th>Ontology</th></tr></thead><tbody><tr><td colspan="2"><strong>Main similarities:</strong></td></tr><tr><td><ul><li>Both serve the same purpose, namely to provide a shared conceptualisation about a specific part of the world to different users in order to facilitate an efficient communication of complex knowledge.</li><li>Both are concerned about covering a broad range of terminology used in a particular domain, and in understanding the relationships among these terms.</li><li>Both utilise a hierarchical organisation to group terms into categories and subcategories.</li><li>Both use text-based extraction terms techniques.</li></ul></td><td></td></tr></tbody></table>

Clearly, ontologies and terminologies are relatively analogous with regard to development aspects even if ontology building is more complex than terminology construction confirmed by the numerous steps necessary to achieve it.

In addition to previously mentioned methodological facets, cost issues in the development cycle of any structure have to be taken into account in order to consider which type of resource is more suitable for a given application.

Simperl et al. (2009) found that: “structures which have similar complexity and which are closely related in the manner in which they are developed will show more common behaviour with regard to effort”. Indeed, developers of ONTOCOM⁴ which is a cost estimation model for the area of Ontology Engineering (Paslaru et al., 2006), considered ontologies from the entire range of the ontology spectrum (Figure 2) and revealed, from the data collected and developer interviews, that the structure type and the development method play a significant role in explaining the tremendous effort of building ontologies.

<figure><img src="image_2.png" /><figcaption>**Figure 2** Ontology spectrum according to ontology formalisation and complexity</figcaption></figure>
<table><caption>**Table 2** Comparison between terminology and ontology based on methodological aspects (continued)</caption><thead><tr><th>Terminology</th><th>Ontology</th></tr></thead><tbody><tr><td colspan="2"><strong>Main similarities:</strong></td></tr><tr><td colspan="2"><ul><li>Both serve the same purpose, namely to provide a shared conceptualisation about a specific part of the world to different users in order to facilitate an efficient communication of complex knowledge.</li><li>Both are concerned about covering a broad range of terminology used in a particular domain, and in understanding the relationships among these terms.</li><li>Both utilise a hierarchical organisation to group terms into categories and subcategories.</li><li>Both use text-based extraction terms techniques.</li></ul></td></tr></tbody></table>

Clearly, ontologies and terminologies are relatively analogous with regard to development aspects even if ontology building is more complex than terminology construction confirmed by the numerous steps necessary to achieve it.

In addition to previously mentioned methodological facets, cost issues in the development cycle of any structure have to be taken into account in order to consider which type of resource is more suitable for a given application.

Simperl et al. (2009) found that: “structures which have similar complexity and which are closely related in the manner in which they are developed will show more common behaviour with regard to effort”. Indeed, developers of ONTOCOM⁴ which is a cost estimation model for the area of Ontology Engineering (Paslaru et al., 2006), considered ontologies from the entire range of the ontology spectrum (Figure 2) and revealed, from the data collected and developer interviews, that the structure type and the development method play a significant role in explaining the tremendous effort of building ontologies.

<figure><img src="image_4.png" /><figcaption>**Figure 2** Ontology spectrum according to ontology formalisation and complexity</figcaption></figure>
In other terms, Ontocom model, considers the complexity of the structure as an important cost driver for the calculation of the ontology building cost effort as illustrated by Table 3.

<table><caption>**Table 3** Ratings for complexity of conceptualisation</caption><thead><tr><td>Rating</td><td>Rating scale</td></tr></thead><tbody><tr><td>Very low</td><td>List of concepts</td></tr><tr><td>Low</td><td>Taxonomy, high number of patterns, no constraints</td></tr><tr><td>Nominal</td><td>Properties, general pattern available, some constraints</td></tr><tr><td>High</td><td>Axioms, few modelling patterns, considerable number of constraints</td></tr><tr><td>Very high</td><td>Instances, no patterns, considerable number of constraints</td></tr></tbody></table>

Source: Simperl et al. (2009)

## 3 Formality in ontology versus formality in terminology

Formal information is meant to be machine-readable and machine-interpretable (Studer et al., 1998). Schleno et al. (1999) defined ontology formality by the degree of usage of mathematical logic in ontologies. Logic enables making valid inferences and allows computational tools to manage complexity even if it leads sometimes to undecidability.

Ontologies are distinguished along a spectrum of formality (Figure 2), so an ontology can be totally informal, as a natural language description of the world, or can be entirely formal, as a mathematical theory (Berners-Lee et al., 2006), but between these two possibilities there are different levels of formality (Ojo and Janowski, 2005):

* *Strongly informal:* an informal ontology contains a list of concepts that are defined only by statements in natural language.

* *Semi-informal:* the ontology is expressed using restricted and structured form of natural language in order to reduce ambiguity and to improve the clarity, example: *yahoo hierarchy*.

* *Semi-formal:* the ontology is implemented in an artificial and well defined language, this category of ontologies includes elements with more structure in the bottom database schemas and metadata schemes on the top conceptual models, e.g., the *ontolinguा⁵ version of the enterprise ontology⁶*.

* *Rigorously formal:* the ontology is implemented using a semantically defined language that is able to represent logical properties of world elements and of their relationships. Formal ontology must include axioms or axiomatic theories using first order logic to constrain the possible interpretations of the ontology components.

However, it is not trivial to select the appropriate level of formality in either ontologies or terminologies, Van Elst and Abecker (2002) have identified two aspects as having an impact on formality, namely: sharing scope and stability as shown in Figure 3:
In other terms, Ontocom model, considers the complexity of the structure as an important cost driver for the calculation of the ontology building cost effort as illustrated by Table 3.

<table><caption>**Table 3** Ratings for complexity of conceptualisation</caption><thead><tr><td>Rating</td><td>Rating scale</td></tr></thead><tbody><tr><td>Very low</td><td>List of concepts</td></tr><tr><td>Low</td><td>Taxonomy, high number of patterns, no constraints</td></tr><tr><td>Nominal</td><td>Properties, general pattern available, some constraints</td></tr><tr><td>High</td><td>Axioms, few modelling patterns, considerable number of constraints</td></tr><tr><td>Very high</td><td>Instances, no patterns, considerable number of constraints</td></tr></tbody></table>

Source: Simperl et al. (2009)

## 3 Formality in ontology versus formality in terminology

Formal information is meant to be machine-readable and machine-interpretable (Studer et al., 1998). Schleno et al. (1999) defined ontology formality by the degree of usage of mathematical logic in ontologies. Logic enables making valid inferences and allows computational tools to manage complexity even if it leads sometimes to undecidability.

Ontologies are distinguished along a spectrum of formality (Figure 2), so an ontology can be totally informal, as a natural language description of the world, or can be entirely formal, as a mathematical theory (Berners-Lee et al., 2006), but between these two possibilities there are different levels of formality (Ojo and Janowski, 2005):

* *Strongly informal:* an informal ontology contains a list of concepts that are defined only by statements in natural language.

* *Semi-informal:* the ontology is expressed using restricted and structured form of natural language in order to reduce ambiguity and to improve the clarity, example: *yahoo hierarchy*.

* *Semi-formal:* the ontology is implemented in an artificial and well defined language, this category of ontologies includes elements with more structure in the bottom database schemas and metadata schemes on the top conceptual models, e.g., the *ontolingu*^5 version of the enterprise ontology^6.

* *Rigorously formal:* the ontology is implemented using a semantically defined language that is able to represent logical properties of world elements and of their relationships. Formal ontology must include axioms or axiomatic theories using first order logic to constrain the possible interpretations of the ontology components.

However, it is not trivial to select the appropriate level of formality in either ontologies or terminologies, Van Elst and Abecker (2002) have identified two aspects as having an impact on formality, namely: sharing scope and stability as shown in Figure 3:
<figure><img src="image_3.png" /><figcaption>**Figure 3** Sharing scope, stability, and formality of information (see online version for colours)</figcaption></figure>

Note: adapted from Van Elst and Abecker (2002)

* *Formality versus stability:* the substantial time period needed to process formalisation of the planned structure to build have an indubitable impact on the stability of the ontology domain and because of that, the ontology must be updated which leads to a new process of formalisation. This means that if the ontology is describing an inconstant reality then a low level of formality will be easier to manage.

* *Formality versus sharing scope:* being as explicit as possible when specifying a conceptualisation is necessary for sharing knowledge between several agents and misunderstanding due to different interpretations of this conceptualisation is avoided with formalisation. However, to achieve the objective of sharing scope via formalisation, understanding formal languages in which are written ontologies is required.

The point here is: although formality is unavoidable to address computational issue of any terminological resource; it has been demonstrated (Van Elst and Abecker, 2002) that it is important to select the appropriate degree of formality in order to avoid influencing negatively sharing scope and stability, two important features that must be taken into account when building knowledge resources.

Formality in terminologies is sometimes mandatory, specifically in tremendously large domains such as healthcare, as claimed by Ceusters (2001). Indeed, it is extremely hard to retrieve a concept or to place a new one in the concept system of a very large terminology without assuming formality in terminology management, which allows automated processing of the terminology.

# 4 Terminology as a step in ontology building process

From the previous section, we know that formal ontologies are needed to process large amounts of information.

Methodologies for building ontologies such as the well-known NeOn methodology, Gomez Perez and Suarez-Figueroa (2008) propose different approaches to facilitate this tremendous effort. Actually, NeOn designers suggest nine different scenarios for building ontology networks. One of these scenarios is achieved by reusing and reengineering
<figure><img src="image_5.png" /><figcaption>**Figure 3** Sharing scope, stability, and formality of information (see online version for colours)</figcaption></figure>

Note: adapted from Van Elst and Abecker (2002)

* *Formality versus stability:* the substantial time period needed to process formalisation of the planned structure to build have an indubitable impact on the stability of the ontology domain and because of that, the ontology must be updated which leads to a new process of formalisation. This means that if the ontology is describing an inconstant reality then a low level of formality will be easier to manage.

* *Formality versus sharing scope:* being as explicit as possible when specifying a conceptualisation is necessary for sharing knowledge between several agents and misunderstanding due to different interpretations of this conceptualisation is avoided with formalisation. However, to achieve the objective of sharing scope via formalisation, understanding formal languages in which are written ontologies is required.

The point here is: although formality is unavoidable to address computational issue of any terminological resource; it has been demonstrated (Van Elst and Abecker, 2002) that it is important to select the appropriate degree of formality in order to avoid influencing negatively sharing scope and stability, two important features that must be taken into account when building knowledge resources.

Formality in terminologies is sometimes mandatory, specifically in tremendously large domains such as healthcare, as claimed by Ceusters (2001). Indeed, it is extremely hard to retrieve a concept or to place a new one in the concept system of a very large terminology without assuming formality in terminology management, which allows automated processing of the terminology.

# 4 Terminology as a step in ontology building process

From the previous section, we know that formal ontologies are needed to process large amounts of information.

Methodologies for building ontologies such as the well-known NeOn methodology, Gomez Perez and Suarez-Figueroa (2008) propose different approaches to facilitate this tremendous effort. Actually, NeOn designers suggest nine different scenarios for building ontology networks. One of these scenarios is achieved by reusing and reengineering
‘non-ontological resources’, defined by Suárez-Figueroa (2010) as “existing knowledge resources whose semantics have not been formalised yet by means of an ontology”.

It is clear that reusing existing and largely consensual terminology allows saving time and money in the ontology development process (Suárez-Figueroa et al., 2008).

This point of view has been shared by many other researchers such as Sowa (2000) who considered that parts of the terminology can be used as starting points for formalisation since “most fields of science, engineering, business, and law have evolved systems of terminology or nomenclature for naming, classifying, and standardizing their concepts”.

In the same current of thought, Prieto-Diaz (2002) declared that a terminology is first developed providing a controlled vocabulary for a domain of interest, then it is organised into taxonomy where key concepts are identified, and finally these concepts are defined and related to create ontology.

Some concrete projects that perform reuse and reengineering of terminologies in building ontologies are:

1 the Neon Project⁷, in which Fisheries Ontologies were developed for use within the Fish Stock Depletion Assessment System (FSDAS) (Caracciolo and Gangemi, 2007), by reusing resources available for the fisheries domain, e.g., the FIGIS database⁸

2 the SEEMP⁹ project in which a Reference Ontology has been built by reusing human resources management standards

3 SKOS¹⁰, the most used metamodel to reengineering thesauri, which is being used by several organisations to export their existing vocabularies in order to support their reuse by other communities, i.e., they are converting the vocabularies from a proprietary format into a consensuated format.

There are many other case studies in reusing terminologies in the ontology life cycle, like the Open Biomedical Ontologies¹¹ (OBO) and the Foundational Model of Anatomy (FMA)¹² with the Terminologia Anatomica (TA) (Rosse, 2001).

Actually all OBO ontologies have the terminology included as a part of the concept specification (Jimeno-Yepes et al., 2009) and similarly in many other medical ontologies like in Dhombres et al. (2010).

Most approaches, dedicated to reuse and reengineering of non-ontological resources, focus on the transformation process of these resources into ontologies. These works concentrate on the reengineering process of terminologies and of their implementation (Suárez-Figueroa et al., 2008).

In fact, two approaches have been identified for transforming terminologies into ontologies; the first approach (Hepp, 2006; Hepp and De Brujin, 2007) transforms resource content into an ontology schema by following these phases:

1. Pre-process and create a formal representation of the reused terminology: the input is an informal specification of a hierarchical categorisation schema and a notion of the context in which the ontology should be used, the output is a set of categories.

2. Derive meaningful, generic ontology classes from the categories in the original terminology by narrowing them down to their meaning in one particular context: derive two ontology classes for each category: one generic concept in a given context and one broader taxonomic concept which allows preserving the original hierarchy.
‘non-ontological resources’, defined by Suárez-Figueroa (2010) as “existing knowledge resources whose semantics have not been formalised yet by means of an ontology”.

It is clear that reusing existing and largely consensual terminology allows saving time and money in the ontology development process (Suárez-Figueroa et al., 2008).

This point of view has been shared by many other researchers such as Sowa (2000) who considered that parts of the terminology can be used as starting points for formalisation since “most fields of science, engineering, business, and law have evolved systems of terminology or nomenclature for naming, classifying, and standardizing their concepts”.

In the same current of thought, Prieto-Diaz (2002) declared that a terminology is first developed providing a controlled vocabulary for a domain of interest, then it is organised into taxonomy where key concepts are identified, and finally these concepts are defined and related to create ontology.

Some concrete projects that perform reuse and reengineering of terminologies in building ontologies are:

1 the Neon Project⁷, in which Fisheries Ontologies were developed for use within the Fish Stock Depletion Assessment System (FSDAS) (Caracciolo and Gangemi, 2007), by reusing resources available for the fisheries domain, e.g., the FIGIS database⁸

2 the SEEMP⁹ project in which a Reference Ontology has been built by reusing human resources management standards

3 SKOS¹⁰, the most used metamodel to reengineering thesauri, which is being used by several organisations to export their existing vocabularies in order to support their reuse by other communities, i.e., they are converting the vocabularies from a proprietary format into a consensuated format.

There are many other case studies in reusing terminologies in the ontology life cycle, like the Open Biomedical Ontologies¹¹ (OBO) and the Foundational Model of Anatomy (FMA)¹² with the Terminologia Anatomica (TA) (Rosse, 2001).

Actually all OBO ontologies have the terminology included as a part of the concept specification (Jimeno-Yepes et al., 2009) and similarly in many other medical ontologies like in Dhombres et al. (2010).

Most approaches, dedicated to reuse and reengineering of non-ontological resources, focus on the transformation process of these resources into ontologies. These works concentrate on the reengineering process of terminologies and of their implementation (Suárez-Figueroa et al., 2008).

In fact, two approaches have been identified for transforming terminologies into ontologies; the first approach (Hepp, 2006; Hepp and De Brujin, 2007) transforms resource content into an ontology schema by following these phases:

1. Pre-process and create a formal representation of the reused terminology: the input is an informal specification of a hierarchical categorisation schema and a notion of the context in which the ontology should be used, the output is a set of categories.

2. Derive meaningful, generic ontology classes from the categories in the original terminology by narrowing them down to their meaning in one particular context: derive two ontology classes for each category: one generic concept in a given context and one broader taxonomic concept which allows preserving the original hierarchy.
3 Form a consistent taxonomy where the edges in the schema are interpreted as a subsumption hierarchy.

4 Generate the ontology in an ontology language.

The second approach (Mochol and Paslaru, 2006) consists of the following steps:

1. Identify and evaluate manually the potentially useful resources: this task is associated to considerable efforts and is dependent on the level of expertise of the engineering team.

2. Extract and integrate the relevant fragments of the selected resources to the ontology: the integration step requires means to translate heterogeneous formats of selected fragments and their customisation with regard to ontology domain.

In addition to the above recommendations about terminology reuse, we suggest that terms' definitions enclosed in terminologies should be used as concepts' annotations in formal ontologies to enhance human understanding of the ontology.

Regarding the inverse process, explicitly, the reuse of ontologies in terminology building process, Baud et al. (2007) stated that terminologies should be developed in such a way that their nodes and relations are modelled on an underlying formal ontology. This approach will provide the consistency of scientific theories to terminologies.

Nevertheless, if either terminologies or ontologies are built for specific purposes, their potential of reusability for other requirements is reduced significantly.

## 5 Conclusions

The purpose of the current study was to determine without ambiguity whether a given structure is a terminology or a formal ontology. We have founded this comparison on several features related to elementary principles, methodological facets and formality view which is in our opinion the foremost criterion to distinguish undoubtedly between these two types of resources.

Ontology formality is crucial since it allows:

* the use of reasoners which infer new knowledge and check out the ontology consistency throughout the satisfaction of ontology constraints

* the use of ontology query languages (e.g., SPARQL) which allows information return on relationships via predicates, inherent graph structure and triples.

Despite these benefits, the formality level has to be selected judiciously to avoid influencing negatively the added value of the future structure to build.

Ontologies are competing in growth and popularity with terminologies due to the emerging technologies related with ontologies, particularly regarding with the semantic web (Martinez-Cruz et al., 2011), on the other hand, terminologies allow saving time and money in the ontology development process, as they are based on an organised set of key terms related to a particular domain, built by domain experts (probable end-users) and obtained after many steps such as: extracting terms, selecting terms, aggregating terms in key concepts, defining terms and organising them in a hierarchical structure.
3 Form a consistent taxonomy where the edges in the schema are interpreted as a subsumption hierarchy.

4 Generate the ontology in an ontology language.

The second approach (Mochol and Paslaru, 2006) consists of the following steps:

1. Identify and evaluate manually the potentially useful resources: this task is associated to considerable efforts and is dependent on the level of expertise of the engineering team.

2. Extract and integrate the relevant fragments of the selected resources to the ontology: the integration step requires means to translate heterogeneous formats of selected fragments and their customisation with regard to ontology domain.

In addition to the above recommendations about terminology reuse, we suggest that terms' definitions enclosed in terminologies should be used as concepts' annotations in formal ontologies to enhance human understanding of the ontology.

Regarding the inverse process, explicitly, the reuse of ontologies in terminology building process, Baud et al. (2007) stated that terminologies should be developed in such a way that their nodes and relations are modelled on an underlying formal ontology. This approach will provide the consistency of scientific theories to terminologies.

Nevertheless, if either terminologies or ontologies are built for specific purposes, their potential of reusability for other requirements is reduced significantly.

## 5 Conclusions

The purpose of the current study was to determine without ambiguity whether a given structure is a terminology or a formal ontology. We have founded this comparison on several features related to elementary principles, methodological facets and formality view which is in our opinion the foremost criterion to distinguish undoubtedly between these two types of resources.

Ontology formality is crucial since it allows:

* the use of reasoners which infer new knowledge and check out the ontology consistency throughout the satisfaction of ontology constraints

* the use of ontology query languages (e.g., SPARQL) which allows information return on relationships via predicates, inherent graph structure and triples.

Despite these benefits, the formality level has to be selected judiciously to avoid influencing negatively the added value of the future structure to build.

Ontologies are competing in growth and popularity with terminologies due to the emerging technologies related with ontologies, particularly regarding with the semantic web (Martinez-Cruz et al., 2011), on the other hand, terminologies allow saving time and money in the ontology development process, as they are based on an organised set of key terms related to a particular domain, built by domain experts (probable end-users) and obtained after many steps such as: extracting terms, selecting terms, aggregating terms in key concepts, defining terms and organising them in a hierarchical structure.
This set has to be enriched and formalised, to the appropriate level of formality, in order to create the intended ontology, even if this transformation process is not as straightforward as it appears.

## References

Baud, R., Ceusters, W., Ruch, P., Rassinoux, A.M., Lovis, C. and Geissbühler, A. (2007) ‘Reconciliation of ontology and terminology to cope with linguistics’, in Kuhn, K., Warren, J. and Leong, T. (Eds.): *Proceedings of MEDINFO*, pp.796–801, Ios Press, Brisbane, Australia.

Bennett, B. and Fellbaum. C. (2006) (Eds.) ‘Formal ontology in information systems’, *Proceedings of the Fourth International Conference FOIS 2006*, Amsterdam, IOS Press.

Berners-Lee, T., Hall, W., Hendler, J.A., O’Hara, K., Shadbolt, N. and Weitzner, D.J.A. (2006) ‘Framework for web science’, *Foundations and Trends in Web Science*, Vol. 1, No. 1, pp.1–130.

Bodner, R.C. and Song, F. (1996) ‘Knowledge-based approaches to query expansion in information retrieval’, *Canadian Conference on AI*, pp.146–158.

Borst, W.N. and Akermans, J.M. (1997) ‘Engineering ontologies’, *International Journal of Human-Computer Studies*, Vol. 46, Nos. 2/3, pp.365–406.

Cabré, M.T. (1999) *Terminology: Theory, Methods and Applications*, John Benjamins, Amsterdam.

Caracciolo, C. and Gangemi, A. (2007) *NeOn Deliverable D7.2.2. Revised and Enhanced Fisheries Ontologies* [online] http://www.neon-project.org/ (accessed July 2011).

Ceusters, W. (2001) ‘Formal terminology management for language based knowledge systems: resistance is futile’, in Temmerman, R. and Lutjeharms (Eds.): *Proceedings of the International Colloquium ‘Trends in Special Language Technology’*, Brussels, Belgium, pp.135–153.

Ceusters, W. and Smith, B. (2005) ‘Terminological and ontological analysis of the NCI thesaurus’, *Methods of Information in Medecine*, Vol. 44, No. 4, pp.498–507.

Ceusters, W., Smith, B., Kumar, A. and Dhaen, C. (2004) ‘Ontology-based error detection’, in Fieshi, M. et al. (Eds.): *SNOMED-CT*, IOS Press, Amsterdam.

Cointet, J.P. and Chavalaris, D. (2008) ‘Multi-level science mapping with asymmetrical paradigmatic proximity in networks and heterogeneous media (NHM)’, *American Institute of Mathematical Sciences*, Vol. 3, No. 2, pp.267–276.

Deng, X., Haarslev, V. and Shiri, N. (2007) ‘Measuring inconsistency in ontologies’, *Proceedings of the 4th European conference on The Semantic Web: Research and Applications*, Innsbruck, Austria, ESpringer-Verlag, Berlin, Heidelberg, pp.326–340.

Dhombres, F., Charlet, J., Jouannic, J.M., Mazuel, L. and Jaulent, M.C. (2010) ‘Re-use of terminological and ontological resources for the construction of domain ontologies in medicine: a description of two experimental approaches’, *Proceedings of the EKAW 2010 Workshop 6: ‘Reuse and Adaptation of Ontologies and Terminologies’*, Lisbon, Portugal, 15 October.

Fisseha, F. (2003) ‘The basics of ontologies – exploratory project’, *Fourth Nordic Agricultural Ontology Service (AOS) Workshop*, Royal Veterinary and Agricultural University, Copenhagen, Denmark.

Gillam, L., Tariq, M. and Ahmad, K. (2005) ‘Terminology and the construction of ontology’, *Terminology*, Vol. 11, No. 1, pp.55–81.

Gomez Perez, A. and Suarez-Figueroa, M.C. (2008) ‘NeOn methodology: scenarios for building networks of ontologies’, *16th International Conference on Knowledge Engineering and Knowledge Management Knowledge Patterns (EKAW 2008)*, Italy.

Gomez Perez, A. and Benjamins, V.R. (1999) ‘Applications of ontologies and problem-solving methods’, *AI-Magazine*, Vol. 20, No. 1, pp.119–122.
This set has to be enriched and formalised, to the appropriate level of formality, in order to create the intended ontology, even if this transformation process is not as straightforward as it appears.

## References

Baud, R., Ceusters, W., Ruch, P., Rassinoux, A.M., Lovis, C. and Geissbühler, A. (2007) ‘Reconciliation of ontology and terminology to cope with linguistics’, in Kuhn, K., Warren, J. and Leong, T. (Eds.): *Proceedings of MEDINFO*, pp.796–801, Ios Press, Brisbane, Australia.

Bennett, B. and Fellbaum. C. (2006) (Eds.) ‘Formal ontology in information systems’, *Proceedings of the Fourth International Conference FOIS 2006*, Amsterdam, IOS Press.

Berners-Lee, T., Hall, W., Hendler, J.A., O’Hara, K., Shadbolt, N. and Weitzner, D.J.A. (2006) ‘Framework for web science’, *Foundations and Trends in Web Science*, Vol. 1, No. 1, pp.1–130.

Bodner, R.C. and Song, F. (1996) ‘Knowledge-based approaches to query expansion in information retrieval’, *Canadian Conference on AI*, pp.146–158.

Borst, W.N. and Akermans, J.M. (1997) ‘Engineering ontologies’, *International Journal of Human-Computer Studies*, Vol. 46, Nos. 2/3, pp.365–406.

Cabré, M.T. (1999) *Terminology: Theory, Methods and Applications*, John Benjamins, Amsterdam.

Caracciolo, C. and Gangemi, A. (2007) *NeOn Deliverable D7.2.2. Revised and Enhanced Fisheries Ontologies* [online] http://www.neon-project.org/ (accessed July 2011).

Ceusters, W. (2001) ‘Formal terminology management for language based knowledge systems: resistance is futile’, in Temmerman, R. and Lutjeharms (Eds.): *Proceedings of the International Colloquium ‘Trends in Special Language Technology’*, Brussels, Belgium, pp.135–153.

Ceusters, W. and Smith, B. (2005) ‘Terminological and ontological analysis of the NCI thesaurus’, *Methods of Information in Medicine*, Vol. 44, No. 4, pp.498–507.

Ceusters, W., Smith, B., Kumar, A. and Dhaen, C. (2004) ‘Ontology-based error detection’, in Fieshi, M. et al. (Eds.): *SNOMED-CT*, IOS Press, Amsterdam.

Cointet, J.P. and Chavalaris, D. (2008) ‘Multi-level science mapping with asymmetrical paradigmatic proximity in networks and heterogeneous media (NHM)’, *American Institute of Mathematical Sciences*, Vol. 3, No. 2, pp.267–276.

Deng, X., Haarslev, V. and Shiri, N. (2007) ‘Measuring inconsistency in ontologies’, *Proceedings of the 4th European conference on The Semantic Web: Research and Applications*, Innsbruck, Austria, ESpringer-Verlag, Berlin, Heidelberg, pp.326–340.

Dhombres, F., Charlet, J., Jouannic, J.M., Mazuel, L. and Jaulent, M.C. (2010) ‘Re-use of terminological and ontological resources for the construction of domain ontologies in medicine: a description of two experimental approaches’, *Proceedings of the EKAW 2010 Workshop 6: ‘Reuse and Adaptation of Ontologies and Terminologies’*, Lisbon, Portugal, 15 October.

Fisseha, F. (2003) ‘The basics of ontologies – exploratory project’, *Fourth Nordic Agricultural Ontology Service (AOS) Workshop*, Royal Veterinary and Agricultural University, Copenhagen, Denmark.

Gillam, L., Tariq, M. and Ahmad, K. (2005) ‘Terminology and the construction of ontology’, *Terminology*, Vol. 11, No. 1, pp.55–81.

Gomez Perez, A. and Suarez-Figueroa, M.C. (2008) ‘NeOn methodology: scenarios for building networks of ontologies’, *16th International Conference on Knowledge Engineering and Knowledge Management Knowledge Patterns (EKAW 2008)*, Italy.

Gomez Perez, A. and Benjamins, V.R. (1999) ‘Applications of ontologies and problem-solving methods’, *AI-Magazine*, Vol. 20, No. 1, pp.119–122.
Green, P. and Rosemann, M. (2004) ‘Applying ontologies to business and systems modeling techniques and perspectives: lessons learned’, *Journal of Database Management*, Vol. 15, No. 2, pp.105–117.

Gruber, T.R. (1993) ‘A translation approach to portable ontology specifications’, *Knowledge Acquisition*, Vol. 5, No. 2, pp.199–220.

Guarino, N. (2011) *Ontology and Terminology: How Can Formal Ontology Help Concept Modeling and Terminology?*, Laboratory for Applied Ontology (LOA), Institute for Cognitive Sciences and Technology, National Research Council Trento, Italy [online] http://www.eaft-aet.net/fileadmin/files/VAKKI/nicola_guarino.pdf (accessed 1 September 2012).

Guarino, N. and Schneider, L. (2002) ‘Ontology-driven conceptual modelling’, *Lecture Notes in Computer Science*, 2503, in *Proceedings of the 21st International Conference on Conceptual Modeling*.

Happel, H.J. and Seedorf, S. (2006) ‘Applications of ontologies in software engineering’, in *Proceedings of International Workshop on Semantic Web Enabled Software Engineering*.

Hepp, M. (2006) ‘A methodology for deriving OWL ontologies from industrial categorization standards’, *IJSWIS, International Journal on Semantic Web & Information Systems*, Vol. 2, No. 1, pp.72–99.

Hepp, M. and De Brujin, J. (2007) ‘GenTax: a generic methodology for deriving OWL and RDF-S ontologies from hierarchical classifications, thesauri, and inconsistent taxonomies’, *Proceedings of the 4th European Semantic Web Conference (ESWC2007)*, Springer-Verlag, pp.129–144.

Huang, C., Calzolari, N., Gangemi, A., Lenci, A., Oltramari, A. and Prévot, L. (Eds.) (2010) *Ontology and the Lexicon. A Natural Language Processing Perspective*, Cambridge University Press, Cambridge, UK.

Hulden, M. (2007) *A Practical Approach on Creating a Restricted Ontology for Crop Wild Relatives*, Agricultural Information and Knowledge Management Papers, Project Report.

ISO 1087-1 (2000) *Terminology Work – Vocabulary – Part 1: Theory and Application*, ISO, Switzerland.

ISO 704 (2000) *Terminology Work – Principles and Methods*, ISO, Switzerland.

ISO 704 (2009) *Terminology Work – Principles and Methods*, ISO, Switzerland.

Jimeno-Yepes, A., Jimenez-Ruiz, E., Berlanga, R. and Redholz-Schuhmann, D. (2009) ‘Reuse of terminological resources for efficient ontological engineering in Life Sciences’, *BMC Bioinformatics*, Vol. 10, No. S4 [online] http://www.biomedcentral.com/1471-2105/10/S10/S4 (accessed 10 September 2012).

Laarman, A. and Kurtev, I. (2009) ‘Ontological metamodeling with explicit instantiation’, *Conference on Software Languages Engineering*, SLE 2009, LNCS, Springer.

Martinez-Cruz, C., Blanco, I, and Vilain, M. (2011) ‘Ontologies versus relational databases: are they so different? A comparison’, *Artificial Intelligence Review*, DOI: 10.1007/s10462-011-9251-9, pp.1–20.

Mochol, M. and Paslaru, E. (2006) ‘Practical guidelines for building semantic eRecruitment applications’, *International Conference on Knowledge Management* (iKnow’06), Special Track: Advanced Semantic Technologies.

Moerkerke, C. and Ceusters, W. (2000) ‘The myth of preferred terms in medical sublanguage and its impact on natural language understanding applications: an empirical study’, *Proceedings of 18th MIC Conference*, pp.55–62.

Noy, N.F. and McGuinness, D.L. (2001) *Ontology Development 101: A Guide to Creating your First Ontology*, Technical Report SMI-2001-0880, Stanford Medical Informatics.

Ogden, C.K. and Richards, I.A. (1923) *The Meaning of Meaning: A Study of the Influence of Language Upon Thought and of the Science of Symbolism*, Routledge & Kegan Paul, London.

Ojo, A. and Janowski, T. (2005) *Ontology, Semantic Web and Electronic Government*, UNU-IIST, Macao.
Green, P. and Rosemann, M. (2004) ‘Applying ontologies to business and systems modeling techniques and perspectives: lessons learned’, *Journal of Database Management*, Vol. 15, No. 2, pp.105–117.

Gruber, T.R. (1993) ‘A translation approach to portable ontology specifications’, *Knowledge Acquisition*, Vol. 5, No. 2, pp.199–220.

Guarino, N. (2011) *Ontology and Terminology: How Can Formal Ontology Help Concept Modeling and Terminology?*, Laboratory for Applied Ontology (LOA), Institute for Cognitive Sciences and Technology, National Research Council Trento, Italy [online] http://www.eaft-aet.net/fileadmin/files/VAKKI/nicola_guarino.pdf (accessed 1 September 2012).

Guarino, N. and Schneider, L. (2002) ‘Ontology-driven conceptual modelling’, *Lecture Notes in Computer Science*, 2503, in *Proceedings of the 21st International Conference on Conceptual Modeling*.

Happel, H.J. and Seedorf, S. (2006) ‘Applications of ontologies in software engineering’, in *Proceedings of International Workshop on Semantic Web Enabled Software Engineering*.

Hepp, M. (2006) ‘A methodology for deriving OWL ontologies from industrial categorization standards’, *IJSWIS, International Journal on Semantic Web & Information Systems*, Vol. 2, No. 1, pp.72–99.

Hepp, M. and De Brujin, J. (2007) ‘GenTax: a generic methodology for deriving OWL and RDF-S ontologies from hierarchical classifications, thesauri, and inconsistent taxonomies’, *Proceedings of the 4th European Semantic Web Conference (ESWC2007)*, Springer-Verlag, pp.129–144.

Huang, C., Calzolari, N., Gangemi, A., Lenci, A., Oltramari, A. and Prévot, L. (Eds.) (2010) *Ontology and the Lexicon. A Natural Language Processing Perspective*, Cambridge University Press, Cambridge, UK.

Hulden, M. (2007) *A Practical Approach on Creating a Restricted Ontology for Crop Wild Relatives*, Agricultural Information and Knowledge Management Papers, Project Report.

ISO 1087-1 (2000) *Terminology Work – Vocabulary – Part 1: Theory and Application*, ISO, Switzerland.

ISO 704 (2000) *Terminology Work – Principles and Methods*, ISO, Switzerland.

ISO 704 (2009) *Terminology Work – Principles and Methods*, ISO, Switzerland.

Jimeno-Yepes, A., Jimenez-Ruiz, E., Berlanga, R. and Redholz-Schuhmann, D. (2009) ‘Reuse of terminological resources for efficient ontological engineering in Life Sciences’, *BMC Bioinformatics*, Vol. 10, No. S4 [online] http://www.biomedcentral.com/1471-2105/10/S10/S4 (accessed 10 September 2012).

Laarman, A. and Kurtev, I. (2009) ‘Ontological metamodeling with explicit instantiation’, *Conference on Software Languages Engineering*, SLE 2009, LNCS, Springer.

Martinez-Cruz, C., Blanco, I, and Vilain, M. (2011) ‘Ontologies versus relational databases: are they so different? A comparison’, *Artificial Intelligence Review*, DOI: 10.1007/s10462-011-9251-9, pp.1–20.

Mochol, M. and Paslaru, E. (2006) ‘Practical guidelines for building semantic eRecruitment applications’, *International Conference on Knowledge Management* (iKnow’06), Special Track: Advanced Semantic Technologies.

Moerkerke, C. and Ceusters, W. (2000) ‘The myth of preferred terms in medical sublanguage and its impact on natural language understanding applications: an empirical study’, *Proceedings of 18th MIC Conference*, pp.55–62.

Noy, N.F. and McGuinness, D.L. (2001) *Ontology Development 101: A Guide to Creating your First Ontology*, Technical Report SMI-2001-0880, Stanford Medical Informatics.

Ogden, C.K. and Richards, I.A. (1923) *The Meaning of Meaning: A Study of the Influence of Language Upon Thought and of the Science of Symbolism*, Routledge & Kegan Paul, London.

Ojo, A. and Janowski, T. (2005) *Ontology, Semantic Web and Electronic Government*, UNU-IIST, Macao.
Paslaru, E., Simperl, B., Tempich, C. and Sure, Y. (2006) ‘ONTOCOM: a cost estimation model for ontology engineering’, *Proceedings of 5th Int’l Semantic Web Conference (ISWC 2006)*, LNCS 4273, Springer, pp.625–639.

Peñas, A., Verdejo, F. and Gonzalo, J. (2001) ‘Corpus-based terminology extraction applied to information access’, *Proceedings of Corpus Linguistics*, Lancaster University.

Prieto-Diaz, R.A. (2002) ‘Faceted approach to building ontologies’, *21st International Conference on Conceptual Modeling*, Tampere.

Rector, A.L. (1999) ‘Clinical terminology: why is it so hard?’, *Methods of Information in Medicine*, Vol. 38, Nos. 4–5, pp.147–157.

Roche, C., Calberg-Challot, M., Damas, L. and Rouard, P. (2009) ‘Ontoterminology: a new paradigm for terminology’, *International Conference on Knowledge Engineering and Ontology Development*, pp.5–8, October, Madeira, Portugal.

Rosse, C. (2001) ‘Terminologia Anatomica: considered from the perspective of next-generation knowledge sources’, *Clinical Anatomy*, Vol. 14, No. 2, pp.120–133.

Sager, J.C. (1990) *A Practical Course in Terminology*, John Benjamins Publishing Company, Amsterdam.

Sager, J.C. and Kageura, K. (1994) ‘Concept classes and conceptual structures: their role and necessity in terminology’, *ALFA*, Vols. 7/8, pp.191–216.

Schleno, C., Ivester, R., Libes, D. and Denno, P. (1999) *An Analysis of Existing Ontological Systems for Applications in Manufacturing and Healthcare*, NISTIR 6301, National Institute of Standards and Technology, Gaithersburg, MD.

Schneider, L. (2003) ‘Designing foundational ontologies. The object-centered highlevel reference ontology OCHRE as a case study’, in *ER 2003, 22nd International Conference on Conceptual Modeling*.

Simperl, E., Popov, I. and Burger, T. (2009) ‘ONTOCOM revisited: towards accurate cost predictions for ontology development projects’, *Proceedings of the International Semantic Web Conference*.

Sowa, J.F. (2000) *Knowledge Representation: Logical, Philosophical, and Computational Foundations*, Brooks Cole Publishing Co., Pacific Grove, CA.

Staab, S., Angele, J., Decker, S., Erdmann, M., Hotho, A., Maedche, A., Schnurr, H.P., Studer, R. and Sure, Y. (2000) ‘AI for the web. Ontology-based community web portals’, *9th International World Wide Web Conference (WWW9)*, Amsterdam, The Netherlands, pp.1034–1039.

Studer, R., Benjamins, V.R. and Fensel, D. (1998) ‘Knowledge engineering: principles and methods’, *Data and Knowledge Engineering*, Vol. 25, Nos. 1–2, pp.161–197.

Suárez-Figueroa, M.C. (2010) *NeOn Methodology for Building Ontology Networks: Specification, Scheduling and Reuse*, PhD thesis, Universidad Politécnica de Madrid [online] http://oa.upm.es/3879/ (accessed 3 October 2012).

Suárez-Figueroa, M.C., Dellschaft, K., Montiel-Ponsoda, E., Villazon-Terrazas, B., Yufei, Z., Aguado de Cea, G., García, A., Fernández-López, M., Gómez-Pérez, A., Espinoza, M. and Sabou, M. (2008) ‘NeOn D5.4.1. NeOn methodology for building contextualized ontology networks. NeOn project’, *Terminologia Anatomica*, Federative Committee on Anatomical Terminology (FCAT), Thieme, Stuttgart [online] http://www.neon-project.org (accessed 23 July 2012).

Uschold, M. (1998) ‘Knowledge level modelling: concepts and terminology’, *Knowledge Engineering Review*, Vol. 13, No. 1, pp.5–29.

Uschold, M. and Gruninger, M. (1996) ‘Ontologies: principles, methods and applications’, *The Knowledge Engineering Review*, Vol. 11, No. 2, pp.93–136.

Van Elst, L. and Abecker, A. (2002) ‘Ontologies for information management: balancing formality, stability, and sharing scope’, *Expert Systems with Applications*, Vol. 23, No. 4, pp.357–366.

Van Rees, R. (2003) ‘Clarity in the usage of the terms ontology, taxonomy and classification’, in the Construction Informatics Digital Library [online] http://itc.scix.net/ (accessed July 2011).
Paslaru, E., Simperl, B., Tempich, C. and Sure, Y. (2006) ‘ONTOCOM: a cost estimation model for ontology engineering’, *Proceedings of 5th Int’l Semantic Web Conference (ISWC 2006)*, LNCS 4273, Springer, pp.625–639.

Peñas, A., Verdejo, F. and Gonzalo, J. (2001) ‘Corpus-based terminology extraction applied to information access’, *Proceedings of Corpus Linguistics*, Lancaster University.

Prieto-Diaz, R.A. (2002) ‘Faceted approach to building ontologies’, *21st International Conference on Conceptual Modeling*, Tampere.

Rector, A.L. (1999) ‘Clinical terminology: why is it so hard?’, *Methods of Information in Medicine*, Vol. 38, Nos. 4–5, pp.147–157.

Roche, C., Calberg-Challot, M., Damas, L. and Rouard, P. (2009) ‘Ontoterminology: a new paradigm for terminology’, *International Conference on Knowledge Engineering and Ontology Development*, pp.5–8, October, Madeira, Portugal.

Rosse, C. (2001) ‘Terminologia Anatomica: considered from the perspective of next-generation knowledge sources’, *Clinical Anatomy*, Vol. 14, No. 2, pp.120–133.

Sager, J.C. (1990) *A Practical Course in Terminology*, John Benjamins Publishing Company, Amsterdam.

Sager, J.C. and Kageura, K. (1994) ‘Concept classes and conceptual structures: their role and necessity in terminology’, *ALFA*, Vols. 7/8, pp.191–216.

Schleno, C., Ivester, R., Libes, D. and Denno, P. (1999) *An Analysis of Existing Ontological Systems for Applications in Manufacturing and Healthcare*, NISTIR 6301, National Institute of Standards and Technology, Gaithersburg, MD.

Schneider, L. (2003) ‘Designing foundational ontologies. The object-centered highlevel reference ontology OCHRE as a case study’, in *ER 2003, 22nd International Conference on Conceptual Modeling*.

Simperl, E., Popov, I. and Burger, T. (2009) ‘ONTOCOM revisited: towards accurate cost predictions for ontology development projects’, *Proceedings of the International Semantic Web Conference*.

Sowa, J.F. (2000) *Knowledge Representation: Logical, Philosophical, and Computational Foundations*, Brooks Cole Publishing Co., Pacific Grove, CA.

Staab, S., Angele, J., Decker, S., Erdmann, M., Hotho, A., Maedche, A., Schnurr, H.P., Studer, R. and Sure, Y. (2000) ‘AI for the web. Ontology-based community web portals’, *9th International World Wide Web Conference (WWW9)*, Amsterdam, The Netherlands, pp.1034–1039.

Studer, R., Benjamins, V.R. and Fensel, D. (1998) ‘Knowledge engineering: principles and methods’, *Data and Knowledge Engineering*, Vol. 25, Nos. 1–2, pp.161–197.

Suárez-Figueroa, M.C. (2010) *NeOn Methodology for Building Ontology Networks: Specification, Scheduling and Reuse*, PhD thesis, Universidad Politécnica de Madrid [online] http://oa.upm.es/3879/ (accessed 3 October 2012).

Suárez-Figueroa, M.C., Dellschaft, K., Montiel-Ponsoda, E., Villazon-Terrazas, B., Yufei, Z., Aguado de Cea, G., García, A., Fernández-López, M., Gómez-Pérez, A., Espinoza, M. and Sabou, M. (2008) ‘NeOn D5.4.1. NeOn methodology for building contextualized ontology networks. NeOn project’, *Terminologia Anatomica*, Federative Committee on Anatomical Terminology (FCAT), Thieme, Stuttgart [online] http://www.neon-project.org (accessed 23 July 2012).

Uschold, M. (1998) ‘Knowledge level modelling: concepts and terminology’, *Knowledge Engineering Review*, Vol. 13, No. 1, pp.5–29.

Uschold, M. and Gruninger, M. (1996) ‘Ontologies: principles, methods and applications’, *The Knowledge Engineering Review*, Vol. 11, No. 2, pp.93–136.

Van Elst, L. and Abecker, A. (2002) ‘Ontologies for information management: balancing formality, stability, and sharing scope’, *Expert Systems with Applications*, Vol. 23, No. 4, pp.357–366.

Van Rees, R. (2003) ‘Clarity in the usage of the terms ontology, taxonomy and classification’, in the Construction Informatics Digital Library [online] http://itc.scix.net/ (accessed July 2011).
Wood, Z. and Galton, A. (2008) ‘A new classification of collectives, formal ontology in information systems’, *Proceedings of the Fifth International Conference*.

Wright, S.E. and Budin, G. (2001) *Handbook of Terminology Management. Vol. 2: Application-Oriented Terminology Management*, John Benjamins, Amsterdam.

Notes

1 http://wordnet.princeton.edu/

2 http://www.ncbi.nlm.nih.gov/mesh

3 Non-hierarchical relations

4 http://ontocom.sti-innsbruck.at/

5 http://www.ksl.stanford.edu/software/ontolinguat

6 http://www.aiai.ed.ac.uk/project/enterprise/enterprise/ontology.html

7 http://www.neon-project.org

8 http://www.fao.org/fishery/statistics/global-aquaculture-production/query/en

9 http://www.seemp.org

10 http://www.w3.org/2004/02/skos/

11 http://www.obofoundry.org/

12 http://fma.biostr.washington.edu
Wood, Z. and Galton, A. (2008) ‘A new classification of collectives, formal ontology in information systems’, *Proceedings of the Fifth International Conference*.

Wright, S.E. and Budin, G. (2001) *Handbook of Terminology Management. Vol. 2: Application-Oriented Terminology Management*, John Benjamins, Amsterdam.

Notes

1 http://wordnet.princeton.edu/

2 http://www.ncbi.nlm.nih.gov/mesh

3 Non-hierarchical relations

4 http://ontocom.sti-innsbruck.at/

5 http://www.ksl.stanford.edu/software/ontolinguat

6 http://www.aiai.ed.ac.uk/project/enterprise/enterprise/ontology.html

7 http://www.neon-project.org

8 http://www.fao.org/fishery/statistics/global-aquaculture-production/query/en

9 http://www.seemp.org

10 http://www.w3.org/2004/02/skos/

11 http://www.obofoundry.org/

12 http://fma.biostr.washington.edu