Metaphor

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2.3 Metaphor

Metaphors might be thought of as “understanding and experiencing one kind of things in terms of another (Lakoff & Johnson 1980).” For example, we understand abstract ideas of friendship in terms of our experience in taking care of flowers. The metaphor,

“Friendship (i.e., target) is a flower (i.e., vehicle),” entails the ideas that friendship is beautiful and vulnerable and it can grow and bloom. These attributes of flower can be applied to the target “friendship”. The ideas from the attributes of flower influence the way “friendship” is understood. Therefore, metaphors are broadly considered to be a conceptual mapping of properties between two knowledge domains, the target domain and the source domain (Lakoff & Johnson 1980; Mason 2004). The target domain provides dimensions for attribution, whereas the source domain (or vehicles) offers properties that may be applicable to the target (McGlone & Manfredi 2001).

A metaphor, as a result, is an effective manner for defining abstract concepts on more concrete level. The abstract nature of friendship is made clearer by defining more concrete characteristics of flower like beauty and vulnerability. Destination image, in essence, is an abstract concept, which is suitable to be described in the form of metaphor. By analyzing metaphors, the characteristics of abstract concepts are able to be embodied.

Metaphors have been widely applied to many areas, such as computer science, psychology, the corporate world, and one of the most interesting applications is in design problem solving due to its potential on enhancing creative and innovative thinking (Casakin 2007; Lubart & Getz 1997; Weick 2003). When designers want to develop an innovative solution to a specific problem, the critical first step is to perceive the world in an unorthodox and unconventional way. Morgan once stated

“metaphors provide some different ways of thinking about things (Morgan 2006).”

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That means metaphors can help us uncover the complex and paradoxical characteristics of things, we then are able to manage and design the solutions that we may have not thought possible before.

These ideas have appeared in the vast literature. For instance, in architectural domain, one of the most impressive metaphors ‘less is more’ makes reference to the engineering idea of reducing architectural design to its minimal and basic nature (Casakin 2007). In product design, metaphors are used to explore the possibilities of product design solutions, and the product designers make their design to reflect the characteristics of metaphors to products on visual level, action level and image level (Wang & Liao 2009). In business administration, metaphors are regarded as a tool to describe “visions” or organization mission and strategy to gain novel concepts for innovation (Hill & Levenhagen 1995). In our study, due to the capabilities of metaphors in creative thinking, we believe it is promising to apply metaphors to design partner configurations for attractive and unique destination images building.

In the previous examples, most of the metaphors were interpreted and created by human being. In most cases, people can understand the metaphor, but they are usually unable to speak out all the specific meanings behind the metaphors (Zhou et al. 2007).

Additionally, we often need to come up with an insightful metaphor before we really enjoy the advantages of metaphors. These are two important tasks we have to tackle when we are using the metaphors.

As a result, a number of studies have investigated on how to support the above tasks in an automatic way, that is, computing metaphor. These studies can be generally classified into two categories. One is metaphor comprehension and the other is metaphor generation. Both tasks are designed to be done automatically by the aid of information technology.

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Metaphor comprehension is defined as a process of mapping between the target and vehicle concepts in order to identify some similarities for metaphor interpretation (Slack 1980; Zhou et al. 2007). There are two general ways that dominate the metaphor comprehension task. One is the rule-based approach and the other is statistics-based approach (Zhou et al. 2007). Approaches based on rules are usually involving hand-coded rules or knowledge base (D'Harris 2002; Martin 1990; Weiner 1984). The level of applicability in these system is limited to the predefine knowledge base. Notice that it is rather difficult to define all the rules or knowledge by human beings. Statistic-based approaches, alternatively, could be implemented by dynamically mining documents or corpus on the fly to understand the metaphor components (Mason 2004; Veale & Hao 2007). The corpus from web serves as a plentiful knowledge source that implicitly represents a different perspective in the world.

Similar to metaphor comprehension, metaphor generation process involves identifying the vehicles associated with shared common attributes (Abe & Nakagawa 2006). The approaches for metaphor generation are relatively few because generating a novel metaphor is more complex. However, there were still some approaches, e.g.

Sardonicus (Veale & Hao 2007) and transparently-motivated (T-M) (Jones 1992).

These approaches also can be classified into two categories, statistic-based approaches and rule-based approaches. Statistic approaches are normally developed based on leveraging statistic method in mining the corpus to establish a probabilistic model or identify the concept patterns (Abe & Nakagawa 2006;Veale & Hao 2007). Rule-based approaches can be implemented as building knowledge base through framing grammatical or hierarchical structure relationships between target and source domains (Baumer et. al. 2009; Jones 1992).

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Although a substantial body of research studies are now available to shed light on automatically understanding or generating metaphors, the application of computing metaphors is still at early stage. We can observe metaphors have been used in vast domain like mentioned above. We also can notice that, in natural language processing domain, there are paramount studies on processing metaphor automatically (Abe &

Nakagawa 2006; Baumer et. al. 2009; D'Harris 2002; Jones 1992; Mason 2004;

Martin 1990; Slack 1980; Veale & Hao 2007; Weiner 1984; Zhou et al. 2007).

Nevertheless, scant research deals with integrating computing metaphor approaches to solve the problem in specific industries. In this study, we aim to take advantages of metaphors to explore innovative solutions for alliance partner selection in a heuristic and automatic manner in the regional tourism industry.

This chapter has reviewed literature from several issues – alliance partner selection, destination image and metaphor – relevant to the goals of this dissertation.

This review included discussions on traditional way of partner selection, influential accounts of destination image building and the utility of metaphors. We can then summarize three points as follows.

(1) For partner selection, there is growing need on a systematic and automatic approach to uncover partner compositions with market niche potentials.

(2) For SMEs in tourism industry, it is beneficial for them to make successful alliances by building attractive and unique images.

(3) For image building, metaphor is a great meaning carrier and its power on creativity contributes to innovative solutions discovery. Computing metaphor theory here forms a sound basis for analyzing metaphor automatically.

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Through connecting these concepts and theory, we are able to develop a methodology to solve our research problem in an unconventional way.

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