Resources represented using formal ontology languages 27

a domain [Gruber 1995]. It consists on defining domain concepts and re-lations between them. Ontologies are expressed using formalisms offering constructors for the definition of its entities [Wanget al. 2007].

A formal ontology is a knowledge resource that is explicitly represented using an ontology representation formalism. The representation formalism expresses meaningful statements of a specific context or domain using the resource’s entities. Formal constraints are applied for an ontology

repre-sentation formalism,which defines its semantic (Definition of a statement or a fact, type of entities involved in an assertion, etc.). Ontologies can be expressed using high-level languages in order to be understood by human (e.g., natural language, UML, conceptual graphs [Chein & Mugnier 1992]

and semantic networks [Sowa 2006] representations). These formalisms are not machine-readable unless they have a concrete syntax that is processed by computers. Thus, multiple formalisms have been defined for representing formal ontologies (see surveys [Nguyen 2011] [Stephan et al.2007]). These formalisms have different levels of expressiveness. For instance, represen-tation formalisms based on first-order logics are more expressive than for-malisms based on description logics [Baader et al.2005]. The higher the level of expressivity the more complete the knowledge representation be-comes. Consequently, reasoning on knowledge becomes more efficient and representative. The high level of expressivity requires better performances in terms of reasoning, more expertise in understanding logic and more spec-ification for representing knowledge. [Nguyen 2011] proposes the following classification of ontology representation formalisms:

1. Traditional Ontology Languages: Ontologies are represented using frame-based languages, which are based on frames and slots. A frame represents a concept and the frame’s slots represent its associated at-tributes:

• Frame Logic (F-Logic) [Kifer & Lausen 1989] is a declarative knowledge representation formalism that combines frame based languages with concept modeling. Frame languages give this for-malism a compact syntax. Its semantic is defined based on logics and a closed world assumption¹;

• Knowledge Interchange Format (KIF) [Geneserethet al. 1992] is a declarative frame-based language dedicated to interchanging knowledge between systems that supports non-monotonic reason-ing². KIF is a formal language that describes facts as objects, functions, relations and rules in first order logic;

• CycL: a declarative formal ontology representation language based on first-order logic and modal operators. This language was developed to represent the Cyc Knowledge Base [Lenat 1995]

using constants, functions, rules and generalization/specialization relations;

1A true statement is also known to be true and what is not currently known to be true, is false

2A consequence relation is not monotonic: adding a formula to a theory might reduce its set of consequences (revision of knowledge)

• Other formalisms such as LOOM [MacGregor & Bates 1987], which is a frame-based formalism where declarative knowl-edge is represented using definitions, rules, facts, and default rules, or OCML (Operational Conceptual Modelling Language) [Motta 1998], which “allows the specification and operationaliza-tion of funcoperationaliza-tions, relaoperationaliza-tions, classes, instances and rules.”³. 2. Web Ontology Languages: they are based on XML [Brayet al. 1998]

and RDF [Klyne & Carroll 2006] and are intended to be used for the interoperability of resources on the web. These languages are widely described and multiple surveys are available for a detailed description of each of them:

• Ontology Inference Layer (OIL) [Corchoet al. 2004] is an ontol-ogy representation formalism that is based on description logics and exchange standards such as RDF and RDFS. DAML+OIL [Fensel et al.2003] is a combination between DAML (DARPA Agent Markup Language)⁴ and OIL, which is more expressive than OIL and less based on frames representations;

• Other formalisms such as XML-based Ontology Exchange Lan-guage (XOL) [Karpet al. 1999] and Simple HTML Ontology Ex-tension (SHOE) [Heflin & Hendler 2000] are formalisms for ex-changing knowledge representation on the Web within HTML pages;

• Web Ontology Language (OWL) [McGuinnesset al. 2004] is a formalism and a standard for representing ontological resources within the context of the Semantic Web. This formalism is based on DAML+OIL and therefore close to description logics, frame-based representation and RDF. Three main languages are pro-posed for this formalism: (1) OWL-Lite is the least expressive language for OWL, which is adapted for hierarchical representa-tions and classification; (2) OWL-DL has a decidable inference procedure. This language is close to the description logics and (3) OWL-Full is the most expressive one and its semantics is close to first-order logic. The differences between the three languages for OWL are mainly due to the difference of definitions of owl:class and owl:ObjectPropertyType.

3source: http://projects.kmi.open.ac.uk/ibrow/toolset.htm

4http://www.daml.org

Figure 3.3: Semantic Web languages stack source by: http:

//bnode.org/blog/2009/07/08/the-semantic-web-not-a-piece-of-cake

Choosing a formalism for ontology representation depends on its appli-cation: knowledge exchange, referencing, automatic reasoning (logical in-ference) or knowledge structuring [Wacheet al. 2001]. For instance if the ontology is supposed to be generic and its goals are not precise then the frame-based representation languages are more adequate for its representa-tion.

3.1.3 Terminological, Lexical and semantic resources

Researchers use different names refer to these resources; they qualify them as ontologies or light weighed ontologies. Their entities are generally interpreted differently than ontological entities. Some researchers qual-ify hese resources as termino-ontological resources[Reymonetet al. 2007, Aussenac-Gilles et al.2006, Badraet al. 2011], others use the term lexi-cal ontologies [Hirst 2009, McCrae et al.2011, Nédellecet al. 2010] or “non-ontological” resources [García-Silva et al.2008, Gangemi & Presutti 2009, Matusovet al. 2013]. In table 3.1 below we present an excerpt of some re-search works that have considered and categorized these types of resources.

These classifications are not standard and each of them depends on the scope

of the research survey and some criteria that may be relevant for one study and non relevant for another. Besides relevance, the categorization criteria, models representing elements of this category were not taken into consider-ation.

Authors Classification

[Gangemi et al.1998]

• Catalogue of normalized terms

• Glossed Catalogue

• Taxonomy [Maedche & Staab 2001]

• Text

• Dictionary

• Knowledge base

• Relational schemata [Sabou et al. 2007a]

• Unstructured

• Semi-structured

• Structured [García-Silva et al.2008]

• Glossaries

• Lexicons

• Classification schemes

• Thesauri

• Folksonomies

Table 3.1: Classification of “non-ontological” resources in the literature These classifications are based on the structure of the re-sources [Sabou et al. 2007a] or the entities within these resources [Gangemi et al.1998]. [García-Silvaet al.2008] defined an approach for categorizing “non-ontological” resources. His methodology is based on three different features: (1) the type of a “non-ontological” resource, which refers to the type of knowledge encoded by the resource; (2) the data model used to represent this knowledge; and (3) the resource’s implementation.

The methodology of [Villazón-Terrazaset al. 2010b] aimed to build guide-lines and patterns to transform “non-ontological” resources into ontological resources. The model for representing these resources according to the authors will be described in the section 3.2.

[Hodge 2000] defines a methodology for classifying Knowledge resources that are not clearly defined as ontologies based on criteria such as structure and complexity. Thus, he classifies “non-ontological” resources under three categories. We describe each item of this classification in order to identify

their entities and the difference between them:

• Term Lists: resources that contain terminological entities linked to each other using terminological relations.

– Terminologies: resources that represent concepts of a par-ticular domain and associates them to terms and label [Wright & Budin 1997, Tudhopeet al. 2006]. Each domain, sub-domain or specialty has a specific terminology to identify the terms to use;

– Gazetteers: or geographical dictionaries, are resources represent-ing information about a specific type of entities (places or loca-tions) [Toral & Munoz 2006, Souzaet al.2005];

– Glossaries: (referred to as a vocabulary) is a resource that rep-resents a list of terms that describe a domain’s concepts by asso-ciating definitions to them [Kohavi & Provost 1998]. The entities of a glossary are concepts (labeled by terms), definitions and the associations between them within a monolingual or multilingual context;

• Classifications and categories: resources that contain conceptual enti-ties linked to each other using hierarchical relations.

– Categorization schemes: these resources describe the representa-tion of objects in a detailed manner in order to define principles for identifying and understanding the link between an object and a category (or a class) [Rosch 1999];

– Subject headings: (index terms, subject terms, or descriptor) are resources representing a set of terms associated to concepts that represent another resource’s content. For instance, the usage of these resources enhances information retrieval. Two typical ex-amples of these resources are MeSH (Medical Subject Headings) [Lowe & Barnett 1994] and LCSH (Library of Congress Subject Headings) [Chan 1995];

– Classification schemes: these resources represent a hierarchical structure of kinds of things (or classes) associated to some descrip-tions. Objects having common characteristics are often grouped under a specific class and individuals having different criteria are divided into different groups [Hafellner 1988].

– Taxonomies: are resource that represents a hierarchical classifica-tion of a domain’s concepts (labelled by terms). The hierarchical relation between entries of a taxonomy are multiple such as whole-part, genus-species and type-instance, etc.;

– Folksonomies: [Peters & Stock 2007] these resource are collec-tions of tags used to organize and categorize content. These tags are proposed by multiple users as a result of a collaborative task [Lambiotte & Ausloos 2005] (social tagging).

• Relationship lists

– Semantic networks: are resources that represent knowledge as a graph of concepts linked by semantic relations. These resources are used to support reasoning about knowledge [Sowa 2006];

– Thesauri: terminological resources in, which terms are organized according to a limited number of semantic relationships (hierar-chical, equivalence and associative) [Foskett 1980];

To this list we add documents, which are a valuable support of informa-tion. This type of resources is a particle popular subject for several studies and is an essential artifact of knowledge engineering. Documents can be translated or associated to specific domains and annotated or indexed using thesauri or ontologies;

Some research works consider all these structured resources as ontologies and others classify them differently. In this contribution, our concern is not about representing these heterogeneous resources under one unique formal representation, we intend to keep the aspects of their entities.