Structuring the Cultural Domain with an Upper Ontology of Culture

Structuring the Cultural Domain with an Upper Ontology of Culture

Emmanuel G. Blanchard (McGill University, Canada), Riichiro Mizoguchi (Osaka University, Japan) and Susanne P. Lajoie (McGill University, Canada)
DOI: 10.4018/978-1-61520-883-8.ch009
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Abstract

Study of cultural similarities and differences is an important research topic for many disciplines such as psychology, sociology, anthropology, archaeology, museology, communication, management and business. This presents many potential opportunities for Information Technology specialists to develop culturally-aware technology, but it also raises the risk of inconsistent approaches of the cultural domain. In this chapter, the authors present the fundamental concepts of the Upper Ontology of Culture (UOC), a formal conceptualization of the cultural domain they developed by identifying the common backbone of culture-related disciplines and activities. As a neutral, theory-driven, and interdisciplinary conceptualization, the UOC shall provide guidelines for the development of culturally-aware applications, for the consistent computerization of cultural data and their interoperability, as well as for the development of culture-driven automatic reasoning processes.
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Introduction

Study of cultural similarities and differences is an important research topic for many disciplines such as psychology, sociology, anthropology, archaeology, museology, communication, management and business to cite but a few. This presents many potential opportunities for IT specialists to develop culturally-aware technology.

However the various coexisting and competing discipline-specific approaches and methods that have been developed, the genuine fuzziness of folk language that people use to discuss cultural matters and the ill-defined nature of cultural questioning lead us to the statement that cultural awareness is particularly difficult to address consistently in information technology.

As mentioned by Lane and his colleagues, “ill-defined domains, in contrast to those that are well defined, are characterized by problems that tend to lack consistent, unambiguous, and generalizable solutions” (Lane et al., 2007). Indeed, culture is an easy-to-use concept in everyday discussions. But it becomes much more difficult to deal with it when the time comes to give it a proper and consensual definition, to determine its constituents or to describe its specifics: in other words to consider it in a scientific manner.

Even when this is the case, cultural notions and terminologies sometimes differ from one discipline to another. The research focus of disciplines may also vary. For instance, some disciplines such as anthropology may be interested in discussing cultural artefacts, whereas others such as psychology may not be. Moreover, a huge amount of data is annually produced by research that could nurture the development of culturally-aware systems. But the computerization of such cultural data as well as the interoperability and centralization of resulting collections are currently limited. Finally, mastering the various research initiatives on culture is a difficult and highly time-consuming task, and the process of knowledge acquisition may frequently be limited by commercial realities and constraints such as deadlines. This could potentially result in ill-designed systems that would extensively rely on ethnocentric views of their development team, implying cultural misconceptions and stereotypes. This may thus affect the credibility of the resulting application, potentially increasing users’ misconceptions about a targeted culture, or reducing the efficiency of human-computer interactions.

Using formal ontology engineering techniques, the Upper Ontology of Culture (UOC) project aims to develop a generic conceptualization of the cultural domain, neutral and interdisciplinary, by identifying the cultural backbone common to culture-related disciplines and activities. Such theory-driven conceptualization has many interests for the development of research on artificial cultural awareness:

  • 1.

    To allow development teams to consider cultures in a scientifically-sound and cross-disciplinary way, i.e. to propose appropriate guidelines on what development teams should focus on when addressing a specific cultural issue,

  • 2.

    To propose ways of appropriately computerizing cultural aspects of a given problem by suggesting templates for theory-driven data structures and data management processes,

  • 3.

    To promote interoperability by enforcing the consistency of cultural data modelling between systems, thus facilitating reuse of computerized cultural data,

  • 4.

    To promote cultural automatic reasoning, thus allowing systems to take culturally-informed decisions that may impact on their internal processing as well as on human-computer interaction.

Key Terms in this Chapter

Cultural Group: In this chapter, a cultural group is seen as a coherent population of individual agents that share a common culture

Culturally-Aware System: A culturally-aware system refers to any system in which culture-related information had/has some impact on its design, runtime or internal processes, structures, and objectives. Cultural awareness may have many interpretations and can lead to several different approaches.

Upper Ontology: “An upper ontology is limited to concepts that are meta, generic, abstract and philosophical, and therefore are general enough to address (at a high level) a broad range of domain areas. Concepts specific to given domains will not be included; however, this standard will provide a structure and a set of general concepts upon which domain ontologies (e.g. medical, financial, engineering, etc.) could be constructed” (SUOWG2009). Smith (2003) further mentions that an upper ontology aims at serving “as a common neutral backbone, which would be supplemented by the work of ontologists working in more specialized domains”

Ontology: “An ontology is similar to a dictionary or glossary, but with greater detail and structure that enables computers to process its content. An ontology consists of a set of concepts, axioms, and relationships that describe a domain of interest” (SUOWG, 2009). An ontology can also be read by humans, who do not necessarily need to be computer specialists to interpret its meaning.

Formal (Heavyweight) Ontology: According to Mizoguchi (2003), formal (heavyweight) ontology “includes ontologies developed with much attention paid to rigorous meaning of each concept, organizing principles developed in philosophy, semantically rigorous relations between concepts, etc. Instance models are usually built based on those ontologies to model a target world, which requires careful conceptualization of the world to guarantee of the consistency and fidelity of the model.”

Cultural Element: In this chapter, cultural elements are either the direct production of a cultural group or elements that it has borrowed, integrated and potentially adapted from another cultural group through group-level interactions such as conquests for instance. Cultural elements are to be considered at group level and are not necessarily homogeneously known/accepted/endorsed by members of their related cultural group. Four kinds of cultural elements are defined in this chapter: core cultural ideas, tangible cultural elements, cultural practices, and ideational cultural elements.

Culture: In this chapter, a culture is seen as an accumulation of elements produced, or integrated and possibly adapted by a cultural group

Lightweight Ontology: According to Mizoguchi (2003), lightweight ontology “includes ontologies for web search engines like Yahoo ontology which consists of a topic hierarchy with little consideration of rigorous definition of a concept, principle of concept organization, distinction between word and concept, etc. The main purpose of such a hierarchy is to power up the search engine and hence it is very use-dependent.”

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