Emmanuelle Gaillard

Jean Lieber

Yannick Naudet

Emmanuel Nauer

Case-Based Reasoning on E-Community Knowledge

Keywords: case-based reasoning, meta-knowledge, reliability, filtering, ranking, personalization

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Introduction

The social Web is known to generate an enormous amount of information, constituting virtually a big knowledge base about almost any kind of subject. This knowledge base is mainly created by e-communities, consisting of people sharing common ideas, goals or interests, communicating over the Internet or any other technological communication network. The knowledge asset built by ecommunities on the Web has a high exploitation potential ranging from knowledge management to, e.g., data-mining applications for detecting interesting trends or tracking users for personalization purposes. Such knowledge is influenced by different human factors such as belief, confidence, or trust, which influence its reliability, not only from a human perspective but also for automated reasoning and decision making [START_REF] Naudet | Towards a novel approach for high-stake decision support system based on community contributed knowledge base[END_REF].

In this context, the work presented here focuses on the exploitation of partially reliable e-community knowledge for Case-Based Reasoning (CBR), in contrast to classical approaches that rely on consensual and validated expert knowledge. In order to preserve the quality of reasoning, we have designed a metaknowledge model, called MKM, to manage knowledge coming from (non-expert) users. MKM represents knowledge reliability using meta-knowledge such as belief, trust, reputation, quality, so that the CBR engine can take it into account in its reasoning process.

This paper is organized as follows. Section 2 gives the motivations for this research. We present the state of the art related to the use of meta-knowledge to characterize knowledge reliability and especially in CBR systems in Section 3. Section 4 details the global process for meta-knowledge management, and Section 5 shows how to extend a classical CBR system to handle partially reliable knowledge. Section 6 presents a use-case in the cooking domain and Section 7 concludes this paper.

Motivation

The research work presented here is motivated by the will to extend the traditional application field of CBR systems, and especially adapt the latter for processing web-originated data. Indeed, traditional CBR systems are used in a closed world where the manipulated knowledge base is fed with expert data, consensual and validated. Because of this, CBR systems can hardly be used when expert data is sparse and especially on the web, where knowledge reliability is not guaranteed and difficult to measure.

Knowing this, an important question is to evaluate if reasoning on not fully reliable knowledge coming from e-communities can be exploited in a CBR approach and if at least results of a similar quality than when reasoning on expert knowledge can be obtained.

Classical CBR systems are usually composed of four knowledge containers: the cases, the domain knowledge (i.e., ontology of the domain), the adaptation knowledge, and the similarity (i.e., retrieval) knowledge. Knowledge is validated by experts; this entails to reason with a limited amount of knowledge. Some studies, like the Kolflow project (http://kolflow.univ-nantes.fr/), have investigated ways to manage knowledge provided by an e-community. The Kolflow approach consists in improving the man-machine collaboration to ensure a collaborative knowledge construction. It allows to collect big amount of knowledge from users, and uses non-regression tests to ensure that new incoming knowledge does not affect the results of the reasoning process that exploits the knowledge [START_REF] Skaf-Molli | Knowledge Continuous Integration Process (K-CIP)[END_REF]. More particularly in the CBR domain, Richards [START_REF] Richards | A social software/web 2.0 approach to collaborative knowledge engineering[END_REF] has investigated decision support from a knowledge base constituted of experts and informal knowledge gathered from collective tools. She has proposed a collaborative knowledge engineering approach mixing CBR and rules, named Collaborative Multiple Classification Ripple Down Rules (C-MCRDR). C-MCRDR proved being efficient to build collaboratively a knowledge base. However, partial reliability of knowledge is handled implicitly in a manual human-driven review and negotiation process. Knowledge reliability related to truth, belief, trust or reputation, is not considered and not directly exploited in the reasoning process.

We propose to associate meta-knowledge about reliability to each knowledge unit (KU) in order to allow inferences on e-community originated knowledge, while ensuring the quality of results. Our solution consists in establishing a meta-knowledge model to describe e-communities knowledge. A new container, the meta-knowledge base, is thus added to the classical CBR architecture.

Fig. 1 shows the difference of architecture between a classical CBR system and a CBR system based on meta-knowledge. In the classical CBR system architecture, (1) the knowledge base is produced by experts of the domain. (2) A user queries the CBR engine which (3) uses the expert validated knowledge for (4) computing its answers. In a meta-knowledge based CBR system, (1) the knowledge base is produced by the e-community and (2) users and (3) knowledge are linked to meta-knowledge. When (4) a user queries the CBR engine, the latter [START_REF] Wang | A product perspective on total data quality management[END_REF] uses the e-community knowledge, (6) filtered by the meta-knowledge model. The final answers of the CBR engine (8) may be ranked (7) using also the meta-knowledge. Moreover, if the user who queries the system is a member of the e-community, the filtering and ranking operations produce personalized results since some meta-knowledge are user-specific.

3 State of the arts

Meta-knowledge about reliability

While philosophical studies (e.g, [START_REF] Hendricks | New waves in epistemology[END_REF]) associate knowledge reliability to safety and robustness, using reliable knowledge element in a knowledge-based system allows to infer knowledge with an acceptable level of trustworthiness. Knowledge reliability is influenced by several factors, sometimes interrelated, as discussed in [START_REF] Naudet | Towards a novel approach for high-stake decision support system based on community contributed knowledge base[END_REF], where a generic model for representing knowledge generated by e-communities is proposed. In an effort to provide a basis for exploiting partially reliable knowledge in a reasoning process, this work identifies the following dimensions for knowledge reliability: origin, context, truth, belief, value, quality, and trust. Origin (or provenance) is the source of a KU; context concerns validity conditions associated to knowledge use; truth is the knowledge validity in the considered world, while belief is the knowledge truth from the author, provider or community perspective; value stands for the importance of the KU for its consumer; quality accounts for characteristics of the KU (e.g., precision, completeness) and finally trust, impacting many relationships in the model and more particularly between knowledge, author/provider and community.

In order to assess at best the impact of these parameters on knowledge reliability, it is necessary to first work with a reduced set of meta-knowledge. In this work we focus on knowledge quality, belief, trust and reputation. Context, as a complex notion, is not considered for the moment, as well as value, since it can be considered that a knowledge of high quality is valuable for the community. Finally, truth is highly correlated with belief notion, and they can be considered as synonyms in a first approach.

Quality is considered by researchers as a complex notion, and it is perceived as a multi-dimensional concept [START_REF] Wang | A product perspective on total data quality management[END_REF]. The nature of data and measurement of its quality have an important impact on the success of decision making. So it is important to evaluate the quality of knowledge, when creating a knowledge base. Several criteria of quality are listed in [START_REF] Wang | A product perspective on total data quality management[END_REF], such as completeness, consistency, freshness, and accuracy. Quality assessment is obtained by using a set of formulas aggregating the different criteria. [START_REF] Even | Utility-driven assessment of data quality[END_REF] proposes a data quality measurement framework that takes into account the context of use and the utility of data. The quality measurement in MKM focuses on the satisfaction a user (measured thanks to a 5-star rating system) will have when using a KU.

Trust, belief and reputation are closely related terms. Trust is largely studied in literature and multiple viewpoints exist, because trust is both a component of our everyday life and of each application domain [START_REF] Marsh | Formalising Trust as a Computational Concept[END_REF]. Trust is generally defined as a ternary relation, valid in a given context, between a truster, a trustee and an object, as originally proposed by Cook et al. [START_REF] Cook | Cooperation Without Trust? The Russell Sage Foundation Series on Trust[END_REF]. As for Grandison [START_REF] Grandison | Trust management tools for internet applications[END_REF], the trusted object is often related to an action performed by the trustee or its ability to do it. In other words, independently of whether the trustee is a person or not, the trustee is viewed as some entity that will actually perform the action the truster expects him to do. Trust is a social process, and evolves dynamically following the history of the relationship. In the human computer interaction domain, Golbeck [START_REF] Golbeck | Computing and applying trust in web-based social networks[END_REF] asserts that "A trusts B if A commits to an action based on the belief that B's future actions will lead to a good outcome." She used this definition in her recommending system for movies, where users can rate both movies and other users. For a given user, movie recommendation scores are computed by taking into account the community opinion: scores depends on movies ratings weighted by user reputation.

Several models have been proposed, both for conceptualizing and evaluating trust. The most common systems exploiting trust are based on reputation [START_REF] Castelfranchi | Principles of Trust for MAS: Cognitive Anatomy, Social Importance, and Quantification[END_REF][START_REF] Artz | A survey of trust in computer science and the Semantic Web[END_REF][START_REF] Jø | The beta reputation system[END_REF][START_REF] Abdul-Rahman | Supporting Trust in Virtual Communities[END_REF] and some of them take also belief into account. For example, the model proposed in [START_REF] Knap | Revealing beliefs influencing trust between members of the czech informatics community[END_REF] shows the relation between trust, belief and reputation in a social network. This model relates trust to a set of relevant beliefs on the evaluated user, for example if a user is an expert, or if a user is honest.

In [START_REF] Quijano-Sánchez | A casebased solution to the cold-start problem in group recommenders[END_REF], a movie is recommended to a user u if this movie has been well rated by users belonging to the trust network of u. Trust is computed in [START_REF] Quijano-Sánchez | A casebased solution to the cold-start problem in group recommenders[END_REF] from the proportion of common ratings users have put on movies they evaluated. The cold start problem is handled by finding in the available users one that is similar to a newcomer, where similarity is computed from demographic data. In our work, the MKM solution relies on predefined default values.

Trust and reputation management has been addressed in multiple domains like in multi-agent systems (MASs) (see, e.g., [START_REF] Huynh | An integrated trust and reputation model for open multi-agent systems[END_REF]), network security and Service Oriented Architectures [START_REF] Neisse | Trust management model and architecture for context-aware service platforms[END_REF]. Regarding e-communities, trust is used in social networks applications and addressed in the Web of Trust initiative.

Meta-knowledge in CBR systems

Usually, user feedback on proposed answers in CBR systems allows to judge the quality of the new cases and to repair a failed adaptation [START_REF] Cordier | Opportunistic acquisition of adaptation knowledge and cases -The IaKa Approach[END_REF]. However, some authors think that the feedback approach is insufficient (by observing missing or delayed feedback) and that meta-knowledge is more relevant to improve the reasoning results [START_REF] Leake | Case provenance: The value of remembering case sources[END_REF]. In [START_REF] Leake | Case provenance: The value of remembering case sources[END_REF], the authors propose to integrate the provenance of a case, as a meta-knowledge, in a CBR system to guide the case base maintenance and increase confidence in future results. For example, a repair is propagated through generated cases from the initial case and the quality of a case is measured by the length of the adaptation path. However, quality of initial cases is set to a same maximum value. The quality does not depend on external factors and additional meta-knowledge like, e.g., provenance of initial cases is not taken into account.

In [START_REF] Saaya | Recommending case bases: Applications in social web search[END_REF], authors integrate trust in addition to provenance in a CBR approach to propose a model of collaborative web search. During a user search, web pages are filtered and ranked using their relevance to the query and the reputation of users having already selected the pages. Reasoning is guided by preferences of users and not by reliability of cases. Besides, the indicator of quality acts only on the case base containers and on cases which have already been found.

In conclusion, the state of the art shows that meta-knowledge is introduced to provide explanations on CBR results or recommendations. To the best of our knowledge, during the reasoning of a CBR system, meta-knowledge like trust, belief and reputation are only used for cases created by the adaptation process or cases which have already been retrieved during a previous reasoning. Metaknowledge representing reliability of cases are not yet used for representing new external cases. Besides, reliability of knowledge of the other containers is not represented. The novelty of MKM is to represent reliability of KUs of all the containers: cases (not only learned cases), domain, adaptation and similarity KUs. Reliability is then used to filter KUs and to rank personalized answers returned by the CBR system. 4 Meta-knowledge management for a CBR system This section presents MKM, the meta-knowledge model, used to represent and to compute the reliability of knowledge coming from an e-community. The objective is to compute the reliability of each KU (of the four containers) for a given user, in order to improve the CBR system reasoning and personalize the answers returned to a user. The reliability is represented by a score that depends on several meta-knowledge elements presented in the following. Some meta-knowledge is inferred by the system while some other is entered in the system by the users who evaluate KUs or by other community members. These evaluations are the foundations of MKM. Fig. 2 introduces the links between users, KUs and metaknowledge:

-A user u may evaluate a KU ku of a knowledge container by a belief score which represents the belief u has in ku. -A user u may evaluate another user v by an a priori trust score which represents the trust u has towards v. -A trust score from a user u towards a user v, which represents how u trusts v, is inferred from the a priori trust score that u has assigned to v and from the belief scores that u has assigned to KUs produced by v. -A reputation score of a user u, which represents the reputation of u in the e-community, is inferred from all the trust scores about u. -A quality score of a KU ku, which represents the global quality of ku for the e-community, is inferred from all the belief scores about ku. -Finally, a reliability score of a KU ku for a user u, which represents the personalized reliability of ku for u, is inferred from quality, reputation and trust scores.

To summarize, the meta-knowledge represented on a white background in Fig. 2 (belief and a priori trust) are initially entered by the users. Sayya et al. [START_REF] Schafer | The adaptive web[END_REF] show that collecting such items works well when there is a small number of users who rate frequently, leading other users of the community to give feedback. The meta-knowledge on the light-grey background (trust, reputation and quality) are computed meta-knowledge, that change dynamically according to the community inputs. And finally, reliability (dark-grey background) is the metaknowledge that will be used by the CBR system to filter knowledge and to rank answers with respect to the users trusts and beliefs. in the following, we detail all these elements and the way they are computed.

User interactions and evaluations. Fig. 3 illustrates how users may interact with the system, by editing some KUs, by evaluating some KUs, and by evaluating other users. In the model, let User be the set of all the users of the system and let KU be the set of all KUs (of all the containers) used in the system.

The ku from function returns the KUs edited by a user, for u, v ∈ User, ku from(u) ∩ ku from(v) = ∅ (a ku ∈ KU has only one producer):

ku from : KU → 2 KU u → ku from(u)
Users may evaluate KUs or other users with, e.g., a numerical scale or a star system. In MKM, these evaluations are normalized in [0, 1].

-When a user u evaluates a ku ∈ KU, u assigns a belief score to the KU ku.

This belief score represents the degree of acceptance, for u, that ku may be true, according to his/her own knowledge.

belief :

User × KU → [0, 1] ∪ {?} (u, ku) → belief(u, ku)
where ? stands for the unknown value (belief(u, ku) = ? means that u has not evaluated ku). -When a user u evaluates another user v, u assigns an a priori trust score for v. This a priori trust score represents the degree of acceptance that v is a trustworthy user for u according to subjective information independently from the KUs produced by v. The higher this a priori trust score is, the higher u expects that a KU ku produced by v is true.

a priori trust : User × User → [0, 1] ∪ {?} (u, v) → a priori trust(u, v)
Belief scores and a priori trust scores are directly given by users when evaluating KUs or other users. The other meta-knowledge is inferred from these two scores. Trust. The trust of user u (the truster) regarding a user v (the trustee) represents the degree of expectation that the knowledge brought by v to the community is true for u. The trust score depends on the a priori trust score assigned by u to v, and on all the belief scores assigned by u to the knowledge produced by v (see Fig. 4).

The multi-set of belief scores that u assigns to the knowledge produced by v, is returned by the function user belief scores(u, v). This multi-set is inferred thanks to the function ku from(v) and belief(u, ku) where ku is the KU that v has produced:

user belief scores : User × User → 2 [0,1] (u, v) → {belief(u, ku) | ku ∈ ku from(v)} \ {?} trust(u, v
) is a measure of the trust the user u has towards the user v.

trust :

User × User → [0, 1] ∪ {?}
Trust is computed as follows (for u, v ∈ User):

-If u has never evaluated any KUs produced by v (i.e., user belief scores(u

, v) = ∅) then, trust(u, v) = a priori trust(u, v) ∈ [0, 1] ∪ {?} -Else, let n = |user belief scores(u, v)|, n = 0.
• If u has not assigned an a priori trust score to v (i.e., a priori trust(u, v) = ?) then, trust is the average of the belief scores that u assigned to KUs produced by v (i.e., user belief scores(u, v)).

trust(u, v) = 1 n s∈user belief scores(u,v) s (1)
Fig. 5. Reputation of a user v is inferred from all trust scores assigned to v.

• Else, the trust is computed by a combination of the a priori trust and the belief scores given by u:

trust(u, v) = α n a priori trust(u, v) + (1 -α n ) 1 n s∈user belief scores(u,v) s ( 2
)
where

α n = 1 n + 1
The more scores have been assigned by u to the KUs provided by v, the less is the influence of the a priori trust score (since lim n→∞ α n = 0): asymptotically, the expressions ( 1) and ( 2) are equivalent.

Reputation. The reputation of a user v is the perception all the users in the community have of v, based on their previous experience with v. Reputation provides an indicator of the truth of knowledge produced by v. It depends on the inferred trust scores of the community towards v (see Fig. 5).

The multi-set of trust scores inferred for the community towards v, is returned by the function community trust scores(v):

community trust scores : User → 2 [0,1] v → {trust(u, v) | u ∈ User} \ {?} reputation(v)
is an estimation of the measure of the trust that has all the community towards v.

reputation : u → [0, 1] ∪ {?}
For v ∈ User, reputation is computed as follows:

-

If |community trust scores(v)| < τ then reputation(v) = default reputation, -Else reputation(v) = u∈User,u =v trust(u, v)
|community trust scores(v)| .

Fig. 6. Quality of a knowledge computed from its belief scores.

The reputation score of v is the average of the set of trust(u, v). For users that have obtained a number of evaluation less than a given threshold τ , the reputation score is assigned to a default value between 0 and 1 (for example, 0.5) and is denoted by default reputation.

Quality. The quality score of a KU ku is the computed community quality of ku. This score is independent from the KU's producer and represents the estimation of the degree of satisfaction of users after the use of ku. The quality score of ku depends on all the belief scores assigned to ku (see Fig. 6).

The multi-set of belief scores of a KU ku, denoted by ku belief scores(ku), represents all the evaluations the community has assigned to ku:

ku belief scores : KU → 2 [0,1] ku → {belief(u, ku) | u ∈ User} \ {?}
The quality knowledge of ku, denoted by community quality(ku), is the average of ku belief scores(ku):

community quality : KU → [0, 1] ∪ ? ku → s∈ku belief scores(ku) s |ku belief scores(ku)|
Reliability. The reliability corresponds to how much a user u can rely on a KU ku, or how ku is actually useful for him/her. This reliability score will be used by the CBR system for filtering knowledge, as well as for ranking.

For a user u (see Fig. 7), the reliability of a KU ku produced by a user v, depends of the reputation of v, the trust score of u towards v (if it exists), and the quality of ku (if it exists). Because reliability is based on the trust score of u towards v, the reliability of knowledge is a personalized score (the trust score varies from one user to another).

reliability : User × KU → [0, 1] (u, ku) → w reputation reputation(v) + w trust trust(u, v) + w quality community quality(ku)
where w reputation + w trust + w quality = 1

This function assumes that trust(u, v) = ? and community quality(ku) = ?.

If trust(u, v) = ? and/or community quality(ku) = ? then they are not taken into account. 5 Plugging the meta-model into a CBR system

This section presents how a classical CBR system can be modified in order to take into account the reliability of knowledge (see Fig. 1). A filter function is used to select the more reliable set of knowledge according to the query and to the user who queries the system. A ranking function is used to order the set of answers according to the meta-knowledge associated to the KUs involved in the computation of the results.

Filtering

According to MKM, the knowledge which is not sufficiently reliable for a user is filtered according to the reliability score. The filtering function, to be filtered(u, ku), depends on reliability(u, ku). It returns true iff the reliability score ku for u is higher than a given threshold β ∈ [0, 1].

to be filtered : User × KU → {true, false} (u, ku) → (reliability(u, ku) ≥ β)

If to be filtered(u, ku), then ku is used in inferences by the CBR engine, else it is not considered.

Ranking

The answers computed by the CBR engine will be ranked according to MKM. The idea is to associate to a user u and an inference, a score, called the inferred reliability. Let {ku 1 , . . . , ku n } ⊢ ku denote an inference performed by the CBR engine: the ku i 's are the KUs taken from the knowledge containers and ku is an inferred knowledge unit. For example, if a case c is adapted thanks to an adaptation rule a into a case c ′ , {c, a} ⊢ c ′ denotes this adaptation inference. The inference reliability is computed thanks to an aggregation function ⊛ applied to the reliability of the ku i 's: inferred reliability(u, {ku 1 , . . . , ku n } ⊢ ku) → ⊛ 1≤i≤n reliability(u, ku i ) There are several possible aggregation functions ⊛: it can be an average aggregation function (corresponding to a probabilistic approach) or a minimum (corresponding to a necessity measure of a possibilistic approach).

By abuse of notation, inferred reliability(u, {ku 1 , . . . , ku n } ⊢ ku) is denoted by inferred reliability(u, ku). Thus, given several inferred knowledge units ku 1 , . . . , ku p and a user u, the ku j 's can be ranked according to decreasing inferred reliability(u, ku j ).

Prerequisites

To apply the filter and ranking functions, a classical CBR system that would be adapted to reason on an e-community knowledge, must:

provide the possibility for users to manage KUs of the different containers in order to assign them a belief score; -be able to filter KUs it will use according to their reliability; -return the set of KUs that are involved in the computation of each answers, in order to rank these answers.

Use case: adapting cooking recipes

This section presents a use case, in the framework of Taaable (http:// taaable.fr/). Taaable is a CBR system which retrieves and creates cooking recipes by adaptation [START_REF] Badra | Taaable: Text Mining, Ontology Engineering, and Hierarchical Classification for Textual Case-Based Cooking[END_REF].

Taaable is based on the four classical knowledge containers. The domain knowledge container contains an ontology of the cooking domain that is used to retrieve the source cases that are the most similar to a target case (i.e. the query). This ontology includes several hierarchies (about food, dish types, etc.). A generalization cost is associated to each edge in the hierarchy, and is used to compute similarity between cases and the query. For example, cost associated on the edge connecting GreenOnion and Onion is 0.3.

The case base is composed of recipes. Each recipe R is transformed into idx(R), the index of the recipe R which is a conjunction of concepts of the domain ontology. Four recipes are given in example in Table 1. idx(R 1 ) is a formal and abstracted representation of the recipe R 1 which is a gratin dish and whose ingredients are green onion, leek, béchamel sauce, and ham (and nothing else).

According to a query entered in the system by a user, and also represented by a conjunction of concepts, the system searches in the case base for some cases (recipes) satisfying the query. For example, Q = GratinDish ∧ Leak ∧ Ham ∧ Bechamel represents the query "I would like a recipe of gratin dish with leak, ham and béchamel sauce." Recipes matching exactly the query, if they exist, are returned to the user and are ranked at the first place (for example R 1 matches exactly Q). The system searches also similar recipes, using the hierarchies to generalize the user query. Recipes that have a similarity measure higher than a given threshold are retrieved and ranked by similarity. Adaptation consists in substituting some ingredients of the source cases by the ones required by the user, and is encoded by a substitution σ = A B, meaning that "A has to be substituted by B". An adaptation has a cost, denoted by cost(A B). The lesser the cost of adaptation of R 1 is, the higher is the similarity of R 1 with Q. The final result of Taaable for Q, ordered by increasing similarity, is R 1 (no adaptation), R 2 with the adaptation GreenOnion Leek, R 3 with the adaptation Endive Leek, R 2 is retrieved before R 3 because GreenOnion is closer to Leek than Endive (i,e. cost(GreenOnion Leek) < cost(Endive Leek)). R 4 is not retrieved because the cost of substitution of Salmon by Leek is too high to adapt R 4 to Q.

Taaable uses also adaptation knowledge (AK) of the form (context, σ) [START_REF] Gaillard | Adaptation knowledge discovery for cooking using closed itemset extraction[END_REF]: context is the recipe or the class of recipes on which the substitution σ can be applied. For example, the AK where Salmon could be replaced with Leek in R 4 is represented by (R 4 , Salmon Leek). Using this AK, R 4 which was not proposed by the query generalization process is now proposed.

Example of reasoning with the meta-knowledge approach

A meta-knowledge container is added to take into account reliability. It will be used to modify the list of answers returned by Taaable. Table 2 shows the reliability of recipes and reliability of adaptations for a specific user: Tom.

Using MKM, unreliable KUs (e.g. which are lower than a given reliability threshold) will be filtered and will not participate to the reasoning process. For example, with a threshold fixed to 0.6, the recipe R 1 and the adaptations A 1 are eliminated, for the user Tom.

The answers can be ranked according to the reliability of the KUs that are involved, for example, when using the average aggregation function, in the following order: R 4 with A 3 , R 3 with A 2 . Indeed, with the average aggregation function, the ranking score of R 4 with A 3 , which is 0.8, is higher than the ranking score of R 3 with A 2 , which is 0.75. R 2 with A 1 is not proposed because A 1 cannot be applied anymore. These ranked answers are specific to Tom, because the reliability of a knowledge for Tom is influenced by the trust score he has towards users of the system who produced the knowledge.

Conclusion

In this paper, we presented an approach for reasoning on partially reliable ecommunity knowledge by contrast to consensual and validated knowledge in classical CBR systems. The approach proposes to associate meta-knowledge about reliability of knowledge, based on user evaluations, and a model has been presented.

We have illustrated the interest of the approach with an example in the community cooking domain. Ongoing work consists in implementing the use case presented in section 6 in the framework of the Taaable project. A collaborative web space which allows to manage cases, AK, and users, and in which users may evaluate these KUs and other users has already been developed. The functions for computing the different meta-knowledge scores must yet be integrated in this collaborative work space. Experiments with the Taaable e-community will be driven at short term in order to evaluate if using MKM and knowledge from an e-community provides similar or better results than those obtained using the classical architecture exploiting consensual knowledge.

Fig. 1 .

 1 Fig. 1. CBR system classical architecture vs. architecture using meta-knowledge.

Fig. 2 .

 2 Fig. 2. Dependencies between users, knowledge units and meta-knowledge.

Fig. 3 .

 3 Fig. 3. A priori Trust and Belief: Evaluation of knowledge and users.

Fig. 4 .

 4 Fig. 4. Trust of a user u towards a user v, computed from the a priori trust score of u towards v, and belief scores of u towards KUs produced by v.

Fig. 7 .

 7 Fig. 7. Reliability of a KU ku for a user u is computed from the quality score of ku, the reputation score of v, the producer of ku, and the trust of u towards v.

Table 1 .

 1 Four examples of recipes and their indexes. Leek ∧ P otato ∧ Bechamel ∧ Ham R2 Carrots gratin GratinDish ∧ GreenOnion ∧ Carrot ∧ Bechamel ∧ Ham R3 Endives gratin GratinDish ∧ Endive ∧ Lemon ∧ Bechamel ∧ Ham R4 Salmon paste GratinDish ∧ P asta ∧ Salmone ∧ Bechamel ∧ Ham

	Id	T itle	idx(Ri)
	R1 Leeks gratin	GratinDish ∧

Table 2 .

 2 Reliability inferred for Tom, for recipes and adaptations.

	Id R 1 Leeks gratin T itle R 2 Carrots gratin R 3 Endives gratin R 4 Salmon paste	Reliability 0.4 0.6 0.9 0.8	Id A 1 GreenOnion σ A 2 Endive A 3 Salmon	Leek Leek Leek	Reliability 0.5 0.6 0.8


Hidekazu Itoh

Market Area Analysis of Ports in Japan: An Application of Fuzzy Clustering

Keywords: port market area, port use propensity, hinterland/foreland, fuzzy clustering, Japan Market Area

This study reviews port cargo flow structure on hinterland/foreland (i.e. shippers' port use propensity) in Japan to examine port policy. Port service areas are analysed by conducting fuzzy clustering for 47 prefectures in Japan. Container cargo flow survey data from 1988 to 2008 at five-year intervals are used. Clusters of shippers' use of ports are discussed; that is, shippers' groups are determined using export/import handling cargo data on the basis of weight, cross section, and time series. The share changes of major Japanese ports for handling international container cargo indicated that only the Kobe port experienced significant volume reduction. However, port market areas have greatly changed in the last 20 years. Major ports lost shares of neighbouring market areas and gained small shares of remote areas (i.e. from regional ports). In contrast, regional ports groups expanded their market area beyond their regional areas. These structural changes to port market area differ between export and import cargo. For example, the Kyushu ports group lost significant market area in the Kyushu region on import cargo, but expanded them on export cargo in some prefectures in the Kyushu region.

Introduction

Containerization started during the 1970s. The expansion in container handling volumes resulted from export-led economic development in Japan and the newly industrializing economies (NIEs) in the Asian region. For example, the port of Kobe, which had been the largest container handling in Asia until 1981, was the fourth largest port in Asia in 1990 after the ports of Singapore, Hong Kong, and Kaohsiung. After the Great Hanshin Earthquake in 1995, the port of Kobe began dropping gradually in ranking. In the most recent rankings conducted in 2010, the port's position was 49th in the world. The rankings of other Japanese ports have also fallen: After 2006, the rankings of all Japanese ports fell below the 20th position. In contrast, the ports of Hong Kong, Singapore, and Busan, which ranked among the top 10 ports in 1975, have maintained their positions. In the 2010 rankings, these ports were container cargo flow survey data from 1988 to 2008 at five-year intervals is used. Clusters of shippers' use of ports (i.e. shippers' groups based on export/import handling cargo data on the basis of weight, cross section, and time series) are discussed.

The remainder of this paper is structured as follows: Section 2 provides a background on port hinterland analysis and Japanese port cargo handling in the past 20 years. Section 3 explains the methodology and the database used for the numerical analysis. Section 4 shows the calculated results by using a fuzzy clustering and maps the changes in the port market area over the past 20 years. Finally, Section 5 summarizes the research and indicates future challenges.

Research significance of port market analysis

Port hinterland/foreland

Inland cargo flow is becoming a critical dimension of the globalisation/maritime transportation/freight distribution paradigm. Port users select the port for shipping in/out based on parameters such as the costs for transporting cargo and inventory. Inland accessibility is a crucial factor of port competition. Therefore, port hinterland analysis serves as the most important and universal research topic in the field of transport geography (specifically, maritime cargo transport). 'Port hinterland/foreland' refers to the market area of the port handling the cargo (i.e. the place/area of origin or destination). [START_REF] Notteboom | Port regionalization: Toward a new phase in port development[END_REF] discussed new patterns on the spatial and functional evolution of ports and port systems by introducing a port regionalisation phase that adds to the existing literature [START_REF] Bird | The Major Seaports of the United Kingdom[END_REF][START_REF] Hoyle | The port-city interface: trends, problems, and examples[END_REF]. Doi (2003) mentioned that container ports, in collaboration with their own regional economies-including the location of inland facilities and transport networks on the hinterland/foreland-compete with each other; this is especially the case in Asia because of geographical factors and a higher concentration of people in large coastal areas. Moreover, [START_REF] Lee | A tale of Asia's world ports: the spatial evolution in global hub port cities[END_REF] showed that different geographical areas exhibit varied hinterland spatial patterns in Western Europe, North America, South Asia, and East Asia. Furthermore, [START_REF] Rodrigue | Comparative North American and European gateway logistics: the regionalism of freight distribution[END_REF] analysed regionalism in freight transport systems by providing a comparative analysis of gateway logistics practices (i.e. inland distribution/warehouse networks) in North America and Europe. However, empirical research on maritime freight remains far less developed because of the lack of data on hinterland/foreland freight flows that enable the delineation of regions containing most port users [START_REF] Darnton | We can have hinterland data[END_REF][START_REF] Mccalla | Dealing with globalisation at the regional and local levels: The case of contemporary containerization[END_REF][START_REF] Guerrero | Les aires d'influence des ports de la France entre réseau et gravitation[END_REF].

Port and shipping line selection behaviours of exporters and importers have been evaluated separately in various studies. [START_REF] Itoh | A Comparative Analysis of Port Cargo Flow Structures: A Focus on Japan and China[END_REF] compared the port cargo flow structures between Japan and China by applying the estimated discrete choice functions used in early studies [START_REF] Itoh | An Analysis of Cargo Transportation Behaviour in Kita Kanto (Japan)[END_REF][START_REF] Tiwari | Shippers' Port and Carrier Selection Behaviour in China: A Discrete Choice Analysis[END_REF]. [START_REF] Tongzon | Port choice and freight forwarders[END_REF] evaluated the main factors influencing a forwarder's port choice in Southeast Asia. Furthermore, [START_REF] Ducruet | Port-city relationships in Europe and Asia[END_REF] conducted an empirical analysis on commodity composition, characteristics of port handling cargo, and economic characteristics of the surrounding area (i.e. the port hinterland/foreland). Specifically, [START_REF] Ducruet | Port-city relationships in Europe and Asia[END_REF] discussed the relationship between port-related variables and socioeconomic variables. Ducruet et al. (2012aDucruet et al. ( , 2012b) ) identified the mutual influences between the specialization of traffic passing through seaports and the socioeconomic characteristics of their surroundings, and proposed a typology of port regions to map the distribution of port regions and to zoom in on specific local conditions.

Based on the survey of the hinterland/foreland of container cargo flow in Japan, the current paper discusses the market area of Japanese ports. Moreover, the structural changes of port service area (i.e. hinterland/foreland) are shown because of time-series data on a matching basis. For example, [START_REF] Osman | Port Choice Selection Based on Cargo Physical Distribution (Containerization) for Export Promotion[END_REF] discussed the origin and destination of container cargo by counting data from the same survey. However, early studies only focused on simple aggregation for the database. In addition, simple counting does not enable a discussion on the characteristics of port hinterland/foreland with numerous port systems for analysis (Figures 4,5,6,and 7).

Japanese port influence

Figure 1 shows the container handling volume trends in terms of TEU (i.e. 20-footer equivalent units) sorted by export and import for the eight major container ports and an aggregation of all other ports in Japan. Although Kobe handled most containers during the first half of 1990s for both export and import, the Great Hanshin earthquake in January 1995 cut the handling volume to less than half of the pre-earthquake levels. The handling volume has partially recovered, but has not reached the pre-earthquake levels yet. Moreover, the port of Kobe lost its transhipment functions (transhipment share declined from 31.6% in 1994 to 4% in 2007) because budgets were insufficient for both reconstruction and modernisation after the earthquake. The foregone demand of Kobe shifted to Osaka, which is geographically close to Kobe, and to smaller extents to Yokohama and Nagoya, especially for imports. Although both export and import handling volumes at Tokyo port have been increasing gradually since 1993, Yokohama did not experience consistent import demand growth and was caught up to by Tokyo in 1996. Since the late 1960s, the port of Tokyo has continuously upgraded its facilities and has maintained efficient port operations [START_REF] Itoh | Efficiency Changes of Major Container Ports in Japan: A Window Application of Data Envelopment Analysis[END_REF]; in addition, the Tokyo Port Terminal Corporation modernized a number of deepwater berths between 1996 and 2004 (MLIT). The handling volumes at Shimizu, Kitakyushu, and Hakata are smaller than those at the major five ports, but are growing because of the increasing Asian route demand. The aggregate of the regional ports has been gradually increasing port handling volumes both in exports and imports. Figure 2 illustrates the changes of port handling shares on each major Japanese port based on Figure 1. Although Kobe port has lost much of its port handling shares in 1995, these figures do not show the drastic changes on the other five major Japanese ports. In contrast, the share of the other ports (shown on the top of each figure) has increased in the past 20 years, and was over 10% in 2010. The shares of Kitakyushu and Hakata, which are in the Kyushu region, are also increasing. The share of import cargo in Keihin port, which consists of the ports of Tokyo and Yokohama, has not changed; however, the share of the port of Yokohama has decreased and the share of the port of Tokyo has increased after 1996. This research focuses on the port market area only for container cargo movements, and it does not cover bulky cargo that comprises most maritime port cargo. However, for example, the study of the bulk cargo flow survey (MLIT, 2009) notes that 96% of the place of origin/destination of bulky cargo is from/to a municipality with the port locates in Japan. That is, the impact of bulky cargo on broad area cargo movements is limited. Moreover, [START_REF] Debrie | Re)spatialiser la question portuaire: pour une lecture géographique des arrière-pays européens[END_REF] noted that wide differences exist among commodities passing through ports in terms of travelled distance, and showed that manufactured goods, such as container cargo, travel longer distances than liquid and dry bulks do in French ports.

Analytical method and research motivation

The purpose of this research is to analyse port market area as the aggregated results of shipper's port choice behaviours. Dynamic fuzzy clustering analysis is applied on three-way data: time, individual (i.e. shippers at the prefecture), and index (the port handling share of each port at each prefecture). Clustering analysis is used to classify each prefecture (individual) based on the usage per cent of each port for export and import on each prefecture (index): how much per cent of some port does a shipper use for shipping out/in from/to their place. We define the typical port use propensity of each ports group by using the cluster centroid (i.e. the gravity) of each cluster on the classified ports group, where each cluster is classified with the minimum distance between the gravity of the cluster and each individual included in the cluster. Therefore, prefectures (shippers) with similar port use propensity are classified within the same ports group. By discussing the port use propensity (i.e. the port handling share of abbreviated/classified shippers), from the gravity of each cluster, we interpret the characteristics of each ports group. In contrast, we identify the port market area from the prefectures' combination included in that cluster.

In general, hard clustering models (i.e. non-hierarchy clustering, like k-means models; [START_REF] Anderberg | Cluster Analysis for Applications[END_REF][START_REF] Hartigan | Clustering Algorithms[END_REF] can be used to classify each prefecture in groups (i.e. clusters) with similar port use propensity according to their port handling share at each port. By contrast, from the viewpoint of a single port cluster, each prefecture may or may not be included within this cluster (i.e. alternative choice). However, even though individual shippers can use some ports as their items, lot size, and the place of origin/destination. In addition, various shippers in one region (i.e. prefecture) can use some ports in combination. Because hard clustering models cannot analyse a shipper's real-life behaviour regarding multi-port clusters, a fuzzy clustering model is preferred to be able to classify a single prefecture (i.e. region) to partially define port markets (i.e. clusters) by port use propensity [START_REF] Itoh | Market Area Analysis of Port in Japan[END_REF].

By applying fuzzy clustering on three-way data in this paper, we analysed how much each prefecture was attributed to each ports group and observed the time-series changes of the extent of attribution for each port cluster. The extent of attribution for each cluster at each prefecture is defined by the degree of membership (i.e. the 'membership function'). Moreover, the sum of the degree of membership in each cluster must be 1.0. Similarly, for hard clustering, the classification mechanism used in fuzzy clustering is that which minimizes the distance between the centroid of the cluster and the individual (i.e. prefecture); the distance is weighted by the degree of membership on the membership function [START_REF] Dunn | A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters[END_REF][START_REF] Bezdek | Pattern Recognition with Fuzzy Objective Function Algorithms[END_REF][START_REF] Bezdek | Fuzzy Models and Algorithms for Pattern Recognition and Image Processing[END_REF]. Moreover, similar to hard clustering, we can characterize the port market area of each ports group constructed using several ports based on the centroid of each cluster as virtual port use propensity indexes. Simultaneously, we can analyse to what extent each prefecture should be attributed to a single port group, and to what extent the membership of each prefecture to a single port group shifts (i.e. how much the port market area changes).

Fuzzy clustering and database

Fuzzy clustering and fuzzy analysis (FANNY) algorithm

Hard clustering (k-means model)

To explain clustering analysis, we used the k-means model, which is a widespread nonhierarchal clustering model. The clustering evaluation criterion on the k-means model is the sum of the squares of the average deviation (AD). That is, the total variation of the data is the sum of the deviation within the cluster and the deviation between clusters.

The set of n observations

n x x , , 1  is expressed as follows:     ip i i n x x x x x S , , , , , 1 1     (1)
The purpose of the k-means model is to classify the set of n observations, S to k clusters, and

n C C , , 1  .
We can denote (1) as follows.



p a n i x x x ia n , , 1 , , , 1 , 1                   ( 2
)
where  is a matrix with n lines and p columns. The purpose of the k-means model is to minimize the following on the evaluation criterion:

     K kG X k i k i v x d J 1 2 , (3)
where k v expresses the cluster centroid of k C :

  K k v v v kp k k , , 1 , , , 1     In matrix notation,   p a K k v v v V ka K , , 1 , , , 1 , 1                  where   2 , k i v x d
is a square value of Euclidean distance between the centroid of the k-th cluster and observation i.

Fuzzy clustering (FANNY algorithm)

In a fuzzy clustering, each observation is 'spread out' over various clusters. Because n is the number of data observations and K is the number of clusters, the purpose of this fuzzy clustering model is to minimize the following objective function:

 , U J  V           m v x d u n i K k k i m ik 1 , 11 2 (4)
where k v : the centroid of the k-th cluster, i x : specification vector of database,

  k i v x d , : dissimilarity between i x and k v ,   ik u U  : ik u is membership function of i x to k-th cluster,
and

                n i ik K k ik ik u u u 1 1 0 , 1 , 1 , 0 .
Every membership function ( ik u ) must be between 0 and 1, every cluster must not be empty, and the sum of the membership function of each observation is equal to 1

.0.   2 , k i v x d
is a square value of the Euclidean distance between the centroid of the k-th cluster and observation i. Moreover, m is a parameter for the degree of membership, where

   m 1 . The parameter defines the extent of fuzziness. If 1  m
, the model is a hard clustering model.

The following equations are derived from Equation (4):

                                                n i K k k k k k k k k i k i k i m ik n i K k k k k i k k k i m ik n i K k k i k i m ik n i K k k i m ik v h v h v h h x h x h x u v h h x v h h x u v x v x u v x d u V U J 11 11 11 2 11 , 2 , , , , , (5)
Here,    , shows the inner product.

       n i m ik n i i m ik k u x u h 1 1 (6)
Assuming Equation ( 6) holds, the minimization of Equation ( 4) is equivalent to the following equation.

                              K k n i n j n l m ik ij m jk m ik u d u u V U J 11 1 1 2 , ( 7
)
and

      p a ja ia ij x x d 1 2 .
The fuzzy analysis (fanny) algorithm estimates the membership function ( ik u ) required to minimize Equation ( 7) [START_REF] Kaufman | Finding Groups in Data[END_REF]. Compared to other fuzzy clustering models, the fanny method has the following advantages: (i) it accepts a dissimilarity matrix, (ii) it is more robust for the spherical cluster assumption, and (iii) it provides a novel graphical display-the silhouette plot (see Statistical Data Analysis Software R web site manual).

Database for fuzzy clustering

The database for this research details container handling volumes by domestic export ports and domestic import ports in each prefecture from the annual MLIT container cargo flow survey conducted between 1988 and 2008 at five-year intervals. The survey period covered in this study is just one month, October 1st to 31st, in each year. Moreover, the database includes container cargo declared at national customs, and acquired container handling tonnage volumes for export and import separately in each municipality by using a questionnaire survey. The database constructed for this numerical analysis converted the original handling volumes to the port handling shares at the export and import ports in 47 prefectures because of the reduction of scale impacts. In this numerical analysis, by using a three-way database [START_REF] Sato | Fuzzy Clustering Models and Applications[END_REF], that is, (1) individual (prefecture), ( 2) index (port handling shares at each prefecture to/from each port by export and import), and (3) time (from 1988 to 2008 at five-year intervals), we can discuss the changes of port market area (i.e. port service area) in Japan. The database includes port handling shares for the 11 major container ports and the aggregate share of all other regional ports in Japan. The 11 major container ports in Japan covered in this analysis are the Ports of Tokyo, Yokohama, Niigata, Shimizu, Nagoya, Yokkaichi, Osaka, Kobe, Shimonoseki, Kitakyushu, and Hakata.

Before showing the calculated results, the remainder of this section shows the port handling shares of major container ports in Japan based on the database for numerical analysis to confirm data stability and consistency (Figures 3(a) and 3(b)). Although this study period includes just one month each year, the database is comparatively stable compared to that based on data in Figures 2(a) and 2(b), which were retrieved from the statistics yearbook. For example, the survey database indicated such trends as import cargo share increase at Tokyo, cargo share reduction of both exports and imports at Kobe, and cargo share increase at local ports (i.e. the aggregate of the other ports). To discuss the hinterland/foreland changes from the viewpoint of ports, Figures 4, 5, 6, and7 show the hinterland/foreland of the ports of Tokyo and Kobe on export and import cargos using a simple aggregation for the database used for numerical analysis as a reference. These figures show the hinterland/foreland shares of container cargo at each prefecture for the two ports. The (a) and (b) sections of each figure show the shares after applying a weighted moving average method between 1988 and 1998 and between 1998 and 2008, respectively. Although the port of Tokyo expanded its import cargo handling shares in the past 20 years (e.g. Tokyo increased its share of cargo from the North Kanto area (i.e. the Gunma and Tochigi prefectures); see Figure 2(b)), the relevant figure does not show clear foreland changes. By contrast, the port of Kobe has depended recently on increased local cargos (e.g. from the Hyogo prefecture) for import and export because of the reduction in their port handling share in the past 20 years. However, simple counting does not enable a significant analysis of the characteristics of port hinterland/foreland with numerous ports systems from the viewpoint of ports, or port service provides. By applying fuzzy clustering, this empirical analysis discusses the port market area changes from a viewpoint of shippers, or port users.

Structural changes of port market area

Calculated results using fuzzy clustering

This research performed fuzzy clustering with a fanny algorithm for the database discussed in the previous section. We achieved reasonable and meaningful results of clusters by adjusting the parameter m, which represents the degree of membership, from 1.0 to 2.0, and the number of clusters k. Figures 8,9,10,11,and 12 show the calculated results, or the membership function of each prefecture, based on five clusters and m = 1.5, which is the middle fuzziness. Other options, such as including seven clusters with m = 1.5, produce results with two Keihin ports clusters that are not suitable for discussion because of high complexity and fuzziness. Kitakyushu and Hakata;and (5) the local (regional) ports group (white in figures), which includes the cluster centroids. These first four clusters are constructed using the 'central international ports' which were designated as high standard ports in 1995, having North American and European routes. Each circle graph on the prefecture in the figures shows the degree of membership ( ik u ) and the size of each circle shows the economic level of prefecture, or the gross regional products (GRP) at constant prices in 2000 (Note: these are not port traffic sizes on the database, because the survey only spans 1 month). The most noticeable point of this numerical analysis is that the database provides three-way data. Therefore, we can compare the changes of the degree of membership for each cluster over time, or how these differences show the changes in port market area.

Generally, these circle graphs become more complex as time passes, and the white segment (i.e. the local ports cluster) increases in remote regions after 2003, especially in the Tohoku region. For example, the Hanshin ports group maintained some market area in the Kyushu region for both export and import in 1988, which it lost after 1993. In contrast, the Kyushu ports group gained market area in the Chugoku region after 2003.

Port market area in Japan

To see visual changes in port market area for the five port clusters, we calculated the differences of the degree of membership for each cluster at each prefecture between 1993 and 2003 by applying a weighted moving average method (Figures 13,14,15,16,and 17). A deeper red colour on the map for the prefecture indicates how much the ports cluster expanded its market area on the prefecture. By contrast, a deeper blue colour on the map for the prefecture shows how much the ports cluster lost market area in the prefecture (Note: a white colour for the prefecture indicates that the port market area expanded by 0-10 points). In total, the major Japanese ports groups lost imports and exports in Eastern and Western Japan to regional ports. For example, the Keihin port and the Hanshin port lost much of the Eastern Japan region (especially the Tohoku region and the Hokkaido prefecture) and the Western Japan region, respectively, because of regional port development and foreign transhipment expansion. The Hanshin ports cluster claimed a portion of the port market area in the Kyushu region in 1988, especially for export cargo, but gradually lost that share (Figure 15) because the budget was insufficient for both reconstruction and modernisation after the 1995 earthquake at the port of Kobe. By contrast, these port groups comparatively gained market area in regions that require more travel distance. The Ise Bay ports group expanded their port market area in the Kansai region and the Nagano prefecture for importing cargo, but lost its share of both exports and imports in the Mie prefecture, which is near the port of Nagoya. The port of Nagoya, which is the major port in the Ise Bay ports group, has road links with major highways leading to/from the Kanto (i.e. Tokyo) and Kansai (i.e. Osaka and Hyogo) areas, as well as new road improvements that connect the inland hinterland/foreland (i.e. the Nagano prefecture). The Kyushu ports group gained market area in the Kumamoto and Nagasaki prefectures for export cargo, but lost most of its market share in the Kyushu region for import cargo. The new container-dedicated terminal developments at the port of Hakata, which is part of the Kyushu ports group, support the handling of export cargo (e.g. plastics, rubber tires, tubes, auto parts) for Asian emerging markets. Finally, the local ports group gained significant market area in remote regions in Japan, as well as in the Eastern Japan, Shikoku, Chugoku, and South Kyushu regions. After the second half of the 1990s, Japanese regional port handling increased because of new port investments. For example, the ports of Hiroshima (in the Hiroshima prefecture) and Sendai (in the Miyagi prefecture) increased their port handling because of port investments by the national works budget after being designated as 'special major ports' in 1992 and 2001, respectively. The port of Sendai connected domestic feeder containers on Pacific routes with North America and provided new rail cargo services to complement the Tokyo Bay ports (i.e. the Keihin ports), since 1998 (MLIT, Figure 13). Moreover, the prefectures of Aomori, Iwate, Kochi, and Miyazaki created tight-positive connections with local ports by carrying basic commodities in 2008 (Ducruet et al., 2012a), indicating how the local ports' market area increased on import cargo (Figure 17). Although the handling shares of major Japanese ports for international container cargo (excluding the port of Kobe) did not experience a major drop in shares, their market areas had greatly changed in the past 20 years. In particular, major ports lost their market areas to their neighbouring areas, only having gained slight market area in remote areas (Note: these are the prefectures coloured white in the figures). This was caused by the increase of foreign transhipment cargo on the main routes to/from Western countries from/to Japanese regional ports. For example, the number of ship calls along the main routes to/from North America and Europe on major Japanese ports has been decreasing (Section 1). Therefore, contrary to our expectations, the market area of each major Japanese port(s) user group has been expanding thinly and widely: the port handling shares at regional ports, including the ports of Hakata and Kitakyushu, have increased. Some early empirical studies [START_REF] Le | Evolution Dynamics of Container Port Systems with a Geo-Economic Concentration Index: A Comparison of Japan, China and Korea[END_REF][START_REF] Itoh | Container Handling Concentration for Export and Import in Japan's Port Systems: Decomposition Analysis of the Gini Coefficient[END_REF][START_REF] Itoh | Structural Changes in Port Cargo Flow Distribution in Asian Container Port Systems[END_REF] noted that the concentration levels of container handling at Japanese ports has been stable, slightly decreasing, or balanced with regional ports.

The Japanese government started a new port policy in 2011, and have invested in highstandard container terminals in two major ports groups, the Keihin and Hanshin ports, to support Japanese manufacturing companies and logistics service providers. However, these ports have gradually lost their domestic market areas in the past 20 years. By contrast, the geographical extent of the hinterland/foreland for these ports has expanded. For example, the Keihin ports group (i.e. the ports of Tokyo and Yokohama) and the Hanshin ports group (i.e. the ports of Osaka and Kobe) have started integrated operations as a business alliance based on the Act on Port Regulations and the Customs Act of the second half of the 2000s. The effects on port selection behaviour of shippers are expected to be seen in the near future. New port logistics companies started feeder services to the Hanshin port from Western regional ports in Japan in 2011, and the city of Kobe started the promotion program for domestic feeder services by taking foreign transhipment cargo via East Asian countries ports in 2012 (Kobe city, press release).

Finally, the ports of Tokyo and Yokohama will merge to become the 'Keihin port' and the ports of Osaka and Kobe will merge to become the 'Hanshin port' in 2014 and 2015, respectively. These 'business mergers' must support both operational efficiency at ports and domestic manufacturing companies' competitiveness against other Asian emerging economic regions through inland transportation network improvements and new container terminal improvements in place since 2005 [START_REF] Shinohara | Port competition paradigms and Japanese port clusters[END_REF]. Specifically, the Hanshin port is enriching their port service level through the development of an inland container depot for their cargo. For example, they started a new contract for an inland container depot in Shiga prefecture that is near the Osaka port with Shandong International Transportation Corporation (SITC) and Orient Overseas Container Line (OOCL) in February 2013 (Osaka Port Corporation, press release). This is because the Hanshin ports group lost their port users (i.e. market area) in the Shiga prefecture. The port users changed their preferences to the Ise Bay ports group, according to the results of this study. Inland warehouse location is also an important factor for port selection in Japan, especially in major ports groups [START_REF] Itoh | Shipper's Distribution Facility Selection Behaviour in Maritime Cargo Transport: The Case of Kita Kanto Region in Japan[END_REF]. Many port and logistics activities also shifted inland around dry ports during the so-called regionalisation phase to expand hinterland control and relieve the seaport city from congestion and lack of space [START_REF] Notteboom | Port regionalization: Toward a new phase in port development[END_REF].

Conclusion

The purpose of this analysis is to review the structural changes of port market areas in Japan by applying fuzzy clustering to time-series data. The applications of fuzzy clustering enable users to turn each port service area into a typology with visual mapping. The study period is 20 years, between 1988 and 2008, and the data is presented at five-year intervals. The results of the numerical analysis showed that there were various port user groups based on their port use propensities.

Although the handling shares for international container cargo of major Japanese ports (except the port of Kobe) did not have experience significant drops in shares, their market areas have greatly changed in the past 20 years. In particular, major ports have lost their market areas to their neighbouring areas, and have only gained slight market area in remote areas (i.e. regions). In contrast, the regional ports groups expanded their market areas in rural areas. Moreover, these structural changes of port market area vary between export and import cargo. For example, the Kyushu ports group lost much of their market area in the Kyushu region on import cargo, even though they had expanded them in some prefectures in the Kyushu region on export cargo because of the expanding Asian markets.

This study classified shippers (prefecture-wise) into port user groups in terms of their port use propensity based on the port handling volumes. However, this paper does not discuss the factors influencing the structural changes by using empirical testing. For further research, factor analysis explaining these changes is needed.

  changes of Japanese container port handlings (Source: port statistics yearbook)

  share changes on Japanese container port handlings (Source: port statistics yearbook)

  share changes on Japanese container port handlings (Source: container cargo flow survey)

  (a) the first decade (1988-1998) (b) the second decade (1998-2008) Figure 4 -Tokyo port's hinterland on export cargo (Source: container cargo flow survey) (a) the first decade (1988-1998) (b) the second decade (1998-2008) Figure 5 -Tokyo port foreland on import cargo (Source: container cargo flow survey) port foreland on import cargo (Source: container cargo flow survey)

  market area (membership function) in 2008 (Source: Devised by the author.) The results indicate that five clusters are constructed: (1) the Keihin ports group (yellow in figures): the ports of Tokyo and Yokohama; (2) the Ise Bay ports group (green in figures): the ports of Nagoya and Yokkaichi; (3) the Hanshin ports group (red in figures): the ports of Osaka and Kobe; (4) the Kyushu ports group (blue in figures): the ports of

  Figure 16 -Kyushu ports group's market area changes (Source: Devised by the author.)

Acknowledgements

The database for this research was provided by the Planning Division, Ports and Harbours Bureau, Ministry of Land, Infrastructure, Transport and Tourism (MLIT), Japan. Moreover, the research was supported by JSPS KAKENHI Grant Number 24730259, Japan.