Roussey et al. It is important to distinguish these different forms of ontologies to clarify their content, their use and their goal.
It is also needed to define precisely the vocabulary derived from the word ontology. For example what is the difference between a core ontology and a domain ontology? First, we introduce and define the different types of ontologies. Second, we present some methodologies to build ontologies. Each of them focused on different dimensions in which ontologies can be classified. This section focuses on two of these classifications: the first one classifies ontologies according to the expressivity and formality of the languages used: natural language, formal language, etc.
Figure 2. For example, if we focus on concepts, which are one of the main components of ontologies, the UML class diagram of Fig.
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A concept can also be defined by the set of instances that belong to it. This last definition is called the extensional definition of a concept and the three former definitions are called intensional definitions of a concept. Symbols are terms that humans can rapidly understand roughly by reading them. And finally all these ontology components are connected through relations. Semantic relations link only concepts together: for example the location relationship indicates that city concept is localized in a country concept.
Instance relations connect only instances and instance relations are often instances of semantic relations, although it is not always the case. Some relations between instances can be contextual and cannot be generalized to all instances of their concept. An example of instance rela- tion is that the city instance named Paris is localized in the country instance named France. All cities are localized in a country. In each section we explain which type of language is normally used to define the ontology and we provide some examples for illustra- tion purposes.
The classification starts using the less formal languages to the more formal one. These ontologies are only used by humans. As shown in Fig. Their goal is to propose an overview of a current project in order to express the state of this project. The grey color of the property elements means that properties are not always well defined by information ontologies. Information ontologies are normally described by means of visual languages, so that they can be easily understood by humans. A Mind Map is a good example of this type of visual language. They notice that Brain Storming is a good method to quickly and intuitively start a project.
Their Mind Map plug-in is a support for discussion about ontology structure. Mind Map descriptions will be followed by three examples of information ontologies: one example will be taken from urban planning project, another one come from archi- tectural design and the latter is used in a construction project.
Mind Map provides information about a topic that is structured in a tree see Fig. Mind maps are used to generate, visualize, structure, and classify ideas, and as an aid in study, organization, problem solving, decision making, and writing. Their information ontology is composed of actors, objects, activities and documents.
All these components are in relation during the cooperative process of design building. Thus it is preferable to follow the decisions taken by each actor to understand the project development, to save time and to avoid errors. Their information ontology presents the state of architectural design components by following the decision process of each actor about this component.
Ontologies in Urban Development Projects
These ontologies are implemented in information system in order to compute some 3D representations of the building called mock up. These mock-ups synthesize the evolution of the project. This work is still in development, Bouattour et al. Example: Information Ontology of Urban Planning Kaza and Hopkins presents a set of concepts to formalize information ontologies used during urban planning process.
Their information ontologies show the different alternatives of a decision in a plan. Plans could present effective decisions, alterna- tive decisions and realizations in order to facilitate the communication between several actors. Moreover this type of plans can help stakeholders during their deci- sion process in order to have a general overview of the city evolution. All these concepts decisions, alternative, actors, etc. In this example the information ontology does not look like a Mind Map but it still uses a visual language similar to that used in a plan.
This type of information ontology focuses on the location of the concept instance not on their internal structure description. These information ontologies represent some general patterns that have to be modified in order to resolve the specific problem of the construction project. The first stage of problem solving is to understand the language convention of each actor group based on the ontology element. Then negotiation and collaborative works can begin to find the appropriate solution of the construction problem.
This type of ontology has to be heavy adaptable and modifiable. Unfortunately, terms are ambiguous. The roles of linguistic ontologies are twofold: The first one is to present and define the vocabulary used. This is achieved by a dictionary for example which list all the terms actually used in language. This agreement defines which term is used to represent a concept in order to avoid ambiguity.
This process is called vocabulary normaliza- tion. When a concept could be described by two synonym terms, the normalization process selects one of those to be the preferred label of the concept. It means that in Fig. Taxonomy and thesaurus organized their normalized vocabulary so that the a priori relationships between concepts are made explicit. That is the reasons why in Fig. Unfortunately the distinction between concepts and their instances are not taken in account: Instances are considered like concepts.
A thesaurus has three basic relationships among terms: equivalence, hierarchical and associative. Let us point out that the last two relations hide several semantic relations. Associative relation between two terms means that there exists a semantic link between concepts labeled by these terms but no information is given on this semantic link.
Now we describe two languages that can be used to describe this type of ontolo- gies: SKOS is used to define thesaurii and RDF is used the defined web metadata. Next we present four different thesaurii belonging to different domains: urban plan- ning, environmental domain and cultural heritage; followed by a taxonomy used in architectural design. RDF is composed of Triples: 1 the subject the web page , 2 a property or predicate an attribute name and 3 an object the actual value of the attribute for the web page. The subject is a resource. The property is a resource that has a name.
The object can be a URI, a literal a string of character representing a number, a date, a noun etc. Since the creation of the data bank in , the hierarchical organization of all these topics gave place to the construction of thesaurus URBAMET. The thesau- rus is accessible in French, Spanish and English. A study of this thesaurus is pre- sented in Chap.
It has been developed as an indexing, retrieval and control tool for the EEA. AGROVOC is a multilingual thesaurus designed to cover the terminology of all subject fields in agriculture, forestry, fisheries, food and several other environmental domains environmental quality, pollution, etc. It is an excellent example of linguistic ontology resulting of a terminology agreement between a community.
This thesaurus is described in Chap. Example: DesignScape Project The research developed in the project DesignScape focuses on the modeling of the different steps of the architectural design Kim and Kim The works formal- ize the typical building design process by a linguistic ontology. More precisely, the ontology is a taxonomy describing the relationships between different activities related to architectural design. The main basis activities modeled are: pre-design, site analysis, schematic design, space zoning, site zoning, objectives definition, analysis, synthesis, evaluation.
Numerous concepts around the architectural design activity are represented in the considered ontology. These relations are also associated to constraints. At execution time, data are stored in the properties of object, that is to say an instance of a concept. Thus, data could be processed in various treatments called methods. Software ontologies are normally defined with conceptual modeling languages used in software and database engineering. These languages are used during soft- ware design procedure: for example Entity-Relationship Model language or Object Model Language. The next section presents the most well known one called UML.
UML presentation will be followed by one example of software ontology1 used in building construction. UML is a graphical language for visualizing, specifying and constructing any parts of software components. Thus, UML is not sufficient to represent all the details required by complex reasoning processes Cranefield like: deducing new knowledge, compute the logical correctness of a formal ontology, etc.
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There have been several releases of the model that have been implemented. The IFC is a response to interop- erability requirements within building construction by a significantly large group of industry practitioners including government and other statutory bodies, clients, con- sultants and contractors together with a substantial number of software vendors. The primary target of the IFC Model is the interoperability among software applica- tions within the building and construction market sector Ferreira da Silva and Cutting-Decelle IFC classes are therefore defined according to the scope and the abstraction level of software systems dealing with building and construction specific content.
Such a model has been primarily developed to enable the exchange and sharing of Building Information Models BIM to increase the productiveness of design, construction, and maintenance operations within the life cycle of buildings. The IFC model therefore describes an object model with concepts classes , relations as direct associations or objectified relationships , and properties or attributes.
They are now widely accepted by industry and major Computer Aided Design software systems support IFC classes for file based exchanges with plan- ning tools and cost evaluation applications. The IFC standard is studied in several chapters of this book, especially in Chap.
This is obtained by using formal logic usually first order logic or Description Logic where the meaning of the concept is guaranteed by formal semantics Borgo As you can see in Fig. For example, Knowledge Bases KB are formal systems that capture the mean- ing of the adopted vocabulary via logical definitions.
In such a language, an ontology designer can arbitrarily define new con- cepts that do not correspond to any term in an existing language. So why do we need to consider natural languages when building ontologies? There are multiple answers to this question, some of which are highly practical while others have a more theoretical background. Most of the lexical forms, in particular nouns, designate a family of individuals that form a concept e.
Falquet cui. Falquet et al. Falquet and J. Guyot This designation can of course be ambiguous in presence of polysemous forms like bank or table. They are also of great help for many practical applications like synonym removal, word sense disambigu- ation, query expansion in information retrieval, etc. Another theoretical connection between ontologies and natural languages origi- nates in the non-circularity of definitions. It is usually desirable to avoid circular definitions in formal ontologies.
But the only way to avoid circularity is to admit that some concepts, called primitive or basic, are not defined within the ontology. Then, the only way to know what these concepts are is either to name them accord- ing to a well-known natural language term or to describe them with words. For instance, the CityGML model, in its Water Bodies sub-model refers to water body classes such as lake, river, ditch, bayou, etc.
This is acceptable because the purpose of this ontology is to describe urban objects and these descriptions do not require extremely precise definitions for concepts that are on the border of the domain.
In this case, the linguistic form, like sea, is associated to a consensual meaning that is considered as sufficient. Finally, linguistic forms are the only way to anchor an ontology in a real domain. An ontology whose concepts and relations identifiers are purely arbitrary strings of characters C, C, icl, pof, … would hardly be considered a conceptualiza- tion of some domain. This is where linguistic forms play an important role. These documents, except for pictures, are expressed in some natural language.
Moreover, in every specialized domain of human activity, a specific terminology has emerged to easily and unambiguously designate the frequently used concepts. Because specialists of the domain have learned to work with these concepts, it is quite clear that any usable ontology should be consistent with this terminology and the conceptual- ization it induces. Similarly, from the ontology designer point of view it is certainly more conve- nient to work with concept names that exist in the natural language, even if the concept meaning in the ontology differs from its usual sense in everyday language.
At some point the designer may also be led to create new concepts, acting as a terminologist, here again it is often suitable to name these concepts with combina- tions of existing linguistic forms. This may occurs is several circumstances, for instance —— An ontology may serve as a common reference for an international community of users.
In such a situation users generally prefer to accessing the ontology in their own language; they also need to find equivalent terms in other languages, e. We will first study the representation issues how to take into account multiple languages when building ontologies , then, we will show how ontologies, connected to multilingual lexicons, can enhance information indexing and retrieval in a multilingual context. For instance, it is well known that domain specialists have developed specific vocabularies to exchange information in a precise and non-ambiguous way.
As a consequence, when a human activity spans several domains, the involved actors may experience communication problems due to this diversity of vocabularies. This can typically occur in urbanism related activi- ties, such as urban planning, where urban engineers, architects, politicians, transporta- tion engineers, or citizen organizations participate in decision processes. In fact, we are confronted with a situation that is similar to multilingualism or multiculturalism. Guyot 5. In other words, concepts are universal while their linguistic representation is culture-specific. The OWL ontology language proposes a basic mechanism to handle linguistic information in the form of annotation properties.
An annotation property is a kind of meta-data attached to a concept. Its value is a string together with a language tag. In OWL knowledge bases the rdfs:label property is typically used to provide the linguistic form of a concept in different languages. Many existing ontologies are based on this approach. For instance, the Unified Medical Language System UMLS National Library of Medicine is com- prised of a set of concept identifiers over one million associated to terms originat- ing from sources vocabularies from 18 different languages.
The concept-centric approach is well suited for normative terminologies, e. In a sense, these ontologies are similar to multilingual thesauri, the aim of which is mostly to define a controlled vocabulary. The main disadvantages of this approach are 1. The European Union EU Community Strategic Guidelines — place particular emphasis on the specific needs of certain zones, such as urban and rural areas.
I gratefully acknowledge the cooperation of officials from departments of the Apulia Region Departments for having participated in the OUR experiment and for having offered their invaluable suggestions, especially with regards to user needs. It should be added that the views expressed in this paper do not necessarily reflect those of the scientific group working with the EU COST Action C21 which sustained the project.
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