B Chebel-Morello

M K Haouchine
email: karim.haouchine@ens2m.fr

N Zerhouni

Reutilization of diagnostic cases by adaptation of knowledge models

Keywords: case-based reasoning, adaptation, adaptation-guided retrieval, dependency relations, hierarchical model, context model, diagnostic

This paper deals with design of knowledge oriented diagnostic system. Two challenges are addressed. The first one concerns the elicitation of expert practice and the proposition of a methodology for developing four knowledge containers of case based reasoning system. The second one concerns the proposition of a general adaptation phase to reuse case solving diagnostic problems in a different context. In most cases, adaptation methods are application-specific and the challenge in this work is to make a general adaptation method for the field of industrial diagnostics applications. This paper is a contribution to fill this gap in the field of fault diagnostic and repair assistance of equipment. The proposed adaptation algorithm relies on hierarchy descriptors, an implied context model and dependencies between problems and solutions of the source cases. In addition, one can note that the first retrieved case is not necessarily the most adaptable case, and to take into account this report, an adaptation-guided retrieval step based on a similarity measure associated with an adaptation measure is realized on the diagnostic problem. These two measures allow selecting the most adaptable case among the retrieved cases. The two retrieval and adaptation phases are applied on real industrial system called Supervised industrial system of Transfer of pallets (SISTRE).

Introduction

This study is a part of our research work realized in the European project SMAC. The goal of this project was to develop a distributed cooperative knowledge oriented platform that proposes a set of maintenance services [START_REF] Karray | Towards a maintenance semantic architecture[END_REF]. These services offer support to the maintenance operators in their daily tasks. The platform is composed of several modules like the equipment model for instance, and a functional analysis and resource management unit that is interconnected by domain ontology [START_REF] Karray | A contextual semantic mediator for a distributed cooperative maintenance platform[END_REF] to ensure an intelligent maintenance policy within a company. The knowledge models are used in this platform to solve practical problems such as fault detection, fault diagnosis, etc. in industrial applications.

The paper aims at building an intelligent application based on a case based reasoning system that is dedicated to industrial diagnostic and repair in the context of maintenance services.

Althoff [START_REF] Althoff | Decision support for case based applicat Wirtschaftsinformatik[END_REF] assert that case based-reasoning (CBR) is the most appropriate technology to implement a knowledge based system. Moreover, CBR is frequently proposed as a methodology for knowledge management applications, in particular in experience based system technologies. It presents the expert knowledge as past and real experiences that are easily understandable by human users. It is a problem solving and learning method that uses similar past problems formalization to solve new ones by adapting them to the context. This method is suitable for diagnostic applications, because fault diagnostic is a domain based on the experience of human experts, where problems are recurrent and can be reused. In addition, diagnostic by case-based reasoning is one of the methods preferred by the industrials. Indeed, reasonin a signifi Therefo experts phase n knowled in organ Their st Fig. 1 sh and 45.9

Fig. 1 Pad'im, ystems, a struction plication he cases system. se based reasoning system in which they combine the CBR online cycle composed of 4 phases (elaborate, retrieve, reuse, retain) with knowledge container defined by Richter (Richter, 1995) (vocabulary; case base; similarity metrics; adaptation knowledge) making it possible to preserve and exploit the past experiences. In the same way our team [START_REF] Rasovska | A mix method of knowledge capitalization in maintenance[END_REF] combines the CBR reasoning "capitalize" and "actualize" phases with the knowledge "detection" and "preservation" in the knowledge capitalization cycle defined by Grundstein [START_REF] Grundstein | From capitalizing on Company Knowledge to Knowledge Management[END_REF]. The elicitation step is not defined by interviews with experts but by a methodology based on engineering safety tools, and reliability concepts such as FMECA and failure history [START_REF] Rasovska | A mix method of knowledge capitalization in maintenance[END_REF]. The creation of knowledge models is carried out by the analysis of the equipment and its decomposition (approach modeled on the practice ground of Cegelec company). The idea developed in this paper is similar to Rasovka's methodology, where a CBR system is built from knowledge models used by maintenance experts like functional and dysfunctional equipment models that are accessible on all equipments and easy to build when not available [START_REF] Rasovska | A mix method of knowledge capitalization in maintenance[END_REF]. This choice minimizes the cost of experts.

To elaborate an oriented knowledge CBR system, we must conceive first the different knowledge containers of CBR and secondly the CBR reasoning phase.

(i)

In the knowledge elicitation, our proposal is to build a. An appropriate case base (a case formalization based on the definition of the diagnostic taking into account the indicators used by the maintenance users). b. Similarity measures taking into account missing values and adaptation measures reflecting the adaptation cost (ii)

In the reasoning process, our proposal is to develop a reasoning process suitable for this model. The reasoning process is dedicated to retrieve the similar case and adapt it to solve problems in other contexts. This adaptation phase is complex and is usually designed for a specific application. a. The first challenge in this reasoning phase is to propose an algorithm based on the model and independent of the specific application. b. The second challenge is to minimize the adaptation cost in the retrieve and reuse phases by proposing an "adaptation-guided retrieval phase".

Our objective in this work is to minimize the cost of designing the knowledge diagnostic system. To evaluate the design cost of our diagnostic method we define in section 6.4.2 three indicators; the adaptability of the method, the reuse cost and the creation effort.

The paper is organized as follows. We first describe the principle of the elicitation approach which defines the knowledge containers of CBR system. In section 3, we focus on the retrieve step of the reasoning phase. One might think that the most similar case is always the best candidate. However, the literature reviews of the retrieve phase show that this is not always the case (Smyth et Keane,1993, 1995[START_REF] Smyth | Adaptation-guided retrieval: Questioning the similarity assumption in reasoning[END_REF][START_REF] Cordier | Engineering and Learning of Adapta Knowledge in Case-Based Reasoning[END_REF]. Consequently, we propose an adaptation-guided retrieval method applied to the industrial diagnostic based on two measures: the first one is similarity and the second one is adaptation. Section 4 deals with the adaptation phase, which is applied to the best adaptable retrieved case.

To define a general adaptation method on symbolic data in the field of industrial diagnostic, we are interested in the adaptation approaches in all applications of CBR.

Some studies of the "memory-based reasoning" [START_REF] Kasif | Towards a Framework for Memory-Based R[END_REF] avoid this step because the wealth of the case-base can compensate for the adaptation phase (Standfill, 1996). Introduction Our ambition is to construct a diagnostic system based on the European standard definition that specifies diagnostic as "they are actions carried out to detect breakdowns, localize them and identify the cause. (Maintenance terminology, 2001):NF EN 13306 of diagnostic. In order to build this system of help of diagnostic, we decompose the equipment into zones that include components. A zone will be characterized by the descriptors d 1 d 2 d 3 (these descriptors are necessary to define the existing zones).

Within each zone, each component has a specific function. If this function is not fulfilled, it will induce dysfunctions that are revealed by some symptoms and characterized by the state of the component and its functioning mode. Fig. 9 gives you the internal functional analysis of an assembly station in SISTRE.

To define the functional mode FM i d , the observed state is compared with the expected state in functional context and identified to the normal (nor) or abnormal (abn) mode.

Those components are characterized by symptoms descriptors. Three parameters are defined by: name, measure defining its state and functional mode

We describe the failure that might occur using the problem descriptors that include zone's descriptors, descriptors in relation to the functions and/or dysfunctions associated to the zone where the failure is taking place.

 

,, value state FM iiii dddd  These components will be classified into different families of components in a hierarchy of components (family of sensors, pneumatic actuators). This will be used by the reasoning phases, mainly, the phase of identification of the failing component.

The localization of the failed zone, when a fault is detected, is done thank at a conceptual graph (see Fig. 3a and Fig. 3b) where the node is the value of the zone descriptor and the solution is the failed zone. Failed zones are composed of the components potentially failing.

A case will describe the experience of how a diagnostic problem is solved. A retrieval phase will find the cases the most similar to the case to be solved by using similarity measures. An adaptation measure will select the most easily adaptable case among the most similar cases. An adaptation method will be applied if no identical cases are found in the case base. The method is based on the relations of dependence between the variations of the problem and the variation of the solution. It includes 3 classes of adaptation that will identify the cause of the failure in function of the context of the breakdown.

Diagnostic case representation

The case base reflects the experiment of solving a fault by linking the dysfunctional mode of component to the cause of this fault, and the repair action. Indeed, we propose the following representation.

A case is composed of a Problem and a Solution part:

Case= (d 1 , d 2 , .d i …., D 1 , D 2 , D 3 , D 4 ) (i)
The problem space of the case contains descriptors relative to both the localization and the functional part of the equipment. (ii)

In the solution space of case, descriptors retain information about the detection of failure and the identification of the cause. A case is formalized by two kinds of problem descriptors: the localization descriptor and the symptom descriptor, and by 4 solution descriptors: D 1 : the Class detected, D 2 : the cause of failure, D 3 : the Repair action, D 4 : the zone of failure.

The existing class is defined in the components taxonomy model.

To simplify the notation of the source case descriptors we note and the target case descriptors

The solution part is composed of four descriptors, the first one is relative to the class of the fault component, the second is dedicated to the component causing the failure, the next one describes the repair actions, and the last one defines the zone of the failure.

The notion defined in this paper will be d by the application SISTRE: For example: let us consider the puller, a specific component in equipment. This component can have two states linked to its position: [front; back] and can have two functional modes [nor, abn]. The localization of this equipment is the entry of internal Ring (ds 1 and ds 2 ) and so on. The solution is composed of a class of failure Ds 1 =The Electrical Actuator and the cause of failure Ds 2 = Stopper S 1 blocked. Ds 3 is the repair action and Ds 4 the Failure zone.

          ,,

Knowledge models associated with the Case

Moreover, the knowledge representation is based on two models associated with the case-base, namely: the context model and the components taxonomy model.

The components taxonomy model

A case has a formalization object and defines a hierarchy of descriptors containing both problem and solution [START_REF] Haouchine | Adaptation-Guided Retrieval for a Diagnostic and Repair Help System Dedicated to a Pallets Transfer[END_REF].

The model is determined from the functional components analysis of the industrial plant. Every group of components (in the source case or observed case) is regrouped by functional classes, and constitutes a component's hierarchy which is common to the problem descriptors "ds" and solution "Ds". A part of SISTRE hierarchical model of components is described in

Context Model

The context model is a contextual graph allowing the localization of components comprising a failure and selection of concerned components compared to the set. Therefore, the context model enables to inform the "localization" descriptors in order to determine the failure zone and the components potentially failing. The course of a pallet will be followed. Using a contextual graph, as shown in Fig. 3a, components likely to be failing will be localized.

An example of a context model concerning the descriptor "Ds 1 " is shown in Fig. 3b. The context allows the localization of components problems and the selection of the right descriptors compared to all others. Therefore, these present components constitute the context in which the failing component is identified. A dependency relation is associated with these components. The descriptors of the localization part are exploited by a context model in two phases of the CBR. In the phase of elaboration of the target case, the user is asked a dynamic tree of questions, and retrieval phase selects the correct element to be substituted in adaptation phase.

2.4

The diagnostic case base A set of 125 cases is considered in the SISTRE case base which corresponds to a part of the component malfunction. The cost to realize the exhaustive case base becomes too large in complex equipment, and to get to this industrial condition, we do not exhaustively list malfunctions of all components. The case problem part is composed of seven descriptors. The first two descriptors define the localization of the failure. This localization is determined by "ds 1 : zone", "ds 2 : pallet site". Let us consider the example of case S1 (Source 1 or observed case 1) in the Table 2.

R18 External ring

Internal ring exc conveyor

Internal ring

Locate the failure at the components D1, Bal0 and S1.

R19

Locate the failure at the components D3, D4 and ext.

Place of breakdown

Which under zone of secondary ring?

Which under zone of secondary ring?

Table 2: A part of the SISTRE case base

This case represents a problem in the detector "D 1 ". The localization part indicates that there is a failure on the entry of the "principal" internal ring. Then, the supervisor part provides the components state involved at the failure zone. The STP1 stopper is in "top" position which has a "normal" functional mode. The balogh "bal0" has value "1", which means that it must enter the working area so that it can be treated by a robot. Finally, the D1 detector does not detect the presence of the pallet which is in abnormal mode ("abn").

The solution part is made up of, the class descriptor of the failing component, a descriptor identifying the failing component, the repair action and the failure zone.

Reasoning process: retrieval phase

In this work, three phases, elaboration retrieve and reuse compose the reasoning process (Mille, 1999). Case descriptors of the localization part are exploited by a context model (see Fig. 3a) in (i)

The elaboration phase. The user answers a dynamic tree of questions in order to build a target case (case to solve). (ii)

The adaptation phase. The correct element is selected to be substituted in the adaptation algorithm. In this section, the retrieval phase is broached.

Retrieval phase

The main difficulty in the retrieval phase is the choice of the right selection criterion for cases to be retrieved [START_REF] Althoff | Decision support for case based applicat Wirtschaftsinformatik[END_REF]. Most of the traditional approaches use simple similarity measures, while others involve the organization of the case-base to improve the retrieval quality [START_REF] Cordier | Interactive and Opportunistic Knowledge Acquisition in Case-B Reasoning[END_REF]. Thus, the retrieval phase can be described by two categories: the first is "simple retrieval" and the second is "combination retrieval/adaptation". The current study is focused on the second type.

Before the Nineties, the two phases of retrieval and adaptation were used in a completely independent way until Veloso (Veloso, 1992) brought new life and suggested the combination of these two steps. [START_REF] Smyth | Adaptation-guided retrieval: Questioning the similarity assumption in reasoning[END_REF] also point out the synergy between plan adaptation and retrieval. In our work, we take into account this synergy and we combine the similarity measure with other criteria to retrieve the most adaptable case.

According to Lopez de Mantaras et al. [START_REF] Lopez De Mantaras | Retrieval, Reuse, Revise, and Retention in CBR[END_REF], six types of retrieval related to the adaptation are identified: Diversity-Conscious Retrieval [START_REF] Smyth | Similarity vs. diversity[END_REF][START_REF] Mcsherry | Diversity-conscious retrieval[END_REF][START_REF] Mcginty | On the role of diversity in conversational recommender systems[END_REF], Compromise-Driven Retrieval (McSherry, 2003, 2004), Order-Based Retrieval [START_REF] Althoff | Decision support for case based applicat Wirtschaftsinformatik[END_REF][START_REF] Bridge | An expressive query language for product recomme systems[END_REF] Explanation-oriented retrieval [START_REF] Cunningham | An evaluation of the usefulness of c based explanation[END_REF][START_REF] Doyle | Explanation oriented retrieval[END_REF], Optimization-Based Retrieval [START_REF] Mougouie | Similarity assessment for generalized cases by optimization methods[END_REF][START_REF] Tartakovski | MINLP based retrieval of generalized cases[END_REF] and Adaptation-Guided Retrieval (AGR). This present work is based on AGR.

3.2

Adaptation-Guided Retrieval phase.

The AGR assumes that the source cases most similar to the target case are not always the easiest to adapt, particularly when the similarity measure is based on surface characteristics. Retrieval therefore must not only search for similar cases but above all easily adaptable cases. Indeed, the authors explain how this retrieval which is guided by adaptation binds specifications and solutions spaces by using the knowledge adaptation. The work of [START_REF] Leake | CB Similarity Assessment: Estimating Adaptability from Experience[END_REF] addresses the adaptation effort concept and the impact of traditional semantic similarity measures on adaptation. Sometimes, the retrieved cases are "similar" to target problem but difficult or impossible to adapt. This generates an inability for the system to solve a problem or give a wrong answer squarely. Therefore, Leake takes into account the adaptation effort at the time of the retrieval step in order to facilitate the adaptation step. This consideration is concretized by inserting the "adaptation cost" in the similarity measure. Leake proposes two steps: first, a classic similarity measure is made by comparing case descriptors, then the most similar cases retrieved at the end of the first step are prioritized according to their adaptability.

To retrieve the best similar case for the adaptation in this work, two measures are implemented:  the first one is a similarity measure (RM) taking into account all the descriptors informed.

The retrieve phase associates the RM measure with a kNN algorithm in order to choose the set of the most similar cases to the target case  The second one, is an "adaptation measure" (AM). It emphasizes on the components which exhibit an abnormal functional mode. The AM measure selects the most adaptable retrieved source case (observed case) from the similar cases selected by the RM measure.

Retrieval Measure

To retrieve the best similar case for the adaptation, first one needs to evaluate the similarity between descriptors and between the attributes of each descriptor. Indeed, for the localization part, the problem descriptors of the target case and source cases will be compared. Then, concerning the functional part, it will be the attributes of descriptors which will be compared. Four local similarity measures are exploited.

In the first one, the hierarchical relation between the values of the descriptors is taken into account. For the value of which belongs to the hierarchical model of descriptors, Let S l be an expected state (source case) and T a component observed (target case).

 

,

value value ii sim d d is developed by               ,, 1
    ,1 0 MFM F M F M F ii i i MFM F ii sim ds dt if ds dt if ds dt  
The similarity metric depends on the formalization of the case. Note that not all descriptors are filled. In order to compare cases in which the descriptors are not all filled, a similarity measure of the presence is defined. It will reflect the presence of descriptors in the case.

To take into account the presence and/or the absence of information in descriptors, a local similarity is developed.

(0 1) (1 1) (1 1 1 1) (1 1 1 1) (1 1 1 1) (,) 0 , 8 5 M dd d d d RS T              12 3 5 6 7 91 (1 1) (1 1) (0, 8 1 0 1) (1 1 1 1) (0, 8 1 1 1) (1 1 1 1) (,) 0 , 8 6 M dd d d d d RST                 

Adaptation Measure

To determine the most adaptable case, an adaptation measure is established to take into account the functional modes associated to components involved in the case of diagnostic. Indeed, during a fault diagnostic, experts are particularly interested in cases with abnormal operating conditions. Therefore, the adaptation measure preferred descriptors associated with the functional mode and gives priority to abnormal functional modes.

The Adaptation Measure "AM" (2) takes into account the source cases supervisor descriptors (expected state) which are different from case target (observed component) and will be only linked to the class and to the functional mode compared to the solution descriptors. The adaptation measure is conditioned by the functional mode value. Indeed, a strong weight is affected to the dysfunctional mode related to the failure.

      1 1 , , , m value value ii i ip Ml m value value presence i j ip sim ds dt AS T sim ds dt            (2)
Where p represents the number of localization descriptor m-p-1: represents the number of supervisor descriptors λ i is the associated weight according to the functional mode.

 If 0 1 2 2 2 2 MF MF ii i MF MF ii i MF MF ii i ds dt nor ds dt ds dt ab             FM = normal  λ i = 2 0 = 1
A weight is associated to the functional mode because the latter is considered as being important in the determination of the failing component. The number of different descriptors is determined by the denominator in equation ( 2). The retrieved source case having the greatest adaptation measure value among the retrieval source cases will be the candidate chosen for the adaptation step.

-Adaptation measure calculation

The second step consists of applying the adaptation measurement (A M ) by taking the weight λ i = {2 0 , 2 1 , 2 2 }. This value considers that the abnormal functional mode is twice more important than the normal functional mode. (,) 1 , 1 4

M dd dd AST         
We note that A M (S 4 , T 1 ) > A M (S 9 , T 1 ). Thus, the case source 4 is the case which will be selected for the adaptation phase. We observe by at the same time that the source cases 4 and 9 are the same measures of similarity.

Adaptation Phase

Our objective in this study is to generalize the adaptation phase.

To obtain a general method in the field of diagnostic, we focused particularly on a unifying method. These methods have as objectives to propose general models from different angles (principles, algorithms, etc...). Hanney and Keane [START_REF] Hanney | Learning Adaptation Rules from a Case-Base[END_REF] build adaptation rules from differences between attributes of cases pairs. Fuchs [START_REF] Fuchs | An Algorithm for Adaptation in Case-Based Reasoning[END_REF] defines the adaptation rules for interval calculations on dependent descriptors (The dependence of descriptors are determined by an expert.) Our adaptation method is inspired by Fuchs work which defines the dependency relations between problem and solution.

To set this dependency relationship between the symbolic data, we relied on the definition of the relevance between problem descriptor and specially the class descriptor.

If the variation of a descriptor d i has an impact alone on the variation of D j , we qualify the relation as high since di is strongly relevant with respect to Dj. A problem descriptor ds i is strongly relevant compared to a solution descriptor Ds j when the value of ds i descriptor is crucial in the determination of Ds j value. The change of ds i value is directly reflected on Ds j value.

If this variation complete the variation of other descriptors, we have a weakly relevant with respect to Dj.

These relations identify for each couple (di, Dj=class) the existence of weakly relevant or strongly relevant features, this will select the kind of adaptation. Therefore three types of relationships are defined: [high, low, no relation].

Our data is symbolic; the adaptation rules must be detectable by using two models:  context model that allows a set of components to adapt and,  hierarchical components model that addresses the component class.

In this contribution, we propose an adaptation algorithm based on the context model, dependency relations between various problem and solution descriptors and descriptors hierarchical model. If the solution class of the best chosen source case is similar to the problem class then the algorithm uses the hierarchical model. If the class is different, then the algorithm uses the contextual model to localize a set of potentially failing components and then uses the hierarchical model.

Dependency relations (DR)

Definition

The influence of a descriptor problem "ds" on the solution descriptors "Ds" is expressed by a dependency relation. A dependency relation is a triplet (ds i , Ds j , DR ij ). DR ij gives us the type of relationship between the problem and the solution to a given case. The relationship DRij takes its value in the set DR ij  (No relation, Low, High).

 DR ij = High: there is a high dependency relation between dsi and Dsj descriptors.

Indeed, dsi descriptor is strongly relevant compared to Dsj descriptor.  DR ij = Low: there is a low dependency relation, i.e., the descriptors are connected thanks to the context which will be characterized by a contextual model.  DR ij = No relation: there is independency between ds i and Ds j .

These relations are based on the definition of relevance between descriptor problem and class of solution problem: The strong relevance, the low relevance and the non-relevance. The relevance notion is well known in the field of feature selection.

Remark

The measures AM and RM select the most adaptable case among the cases the most similar to the problem to be solved, while DR serves to qualify the type of the existing relations between the problem and the solution of the most adaptable case. This is done to select the category of adaptation.

Identification of DR.

There are three way to determine the relationship between the problem and the class. 1°-The expert determines this relation. 2°-The relevant descriptors leading to a class of failure can be identified thanks to the causal model and by the use of a fault tree which defines the relation between the symptom and the cause. The causal model is easily built by using the tools recommended by Rasovska's methodology. 3°-The learning algorithm: To identify this type of relationship, we can apply a variables filtering algorithm such as Reporting Strong Algorithm for Subset Selection (STRASS) [START_REF] Senoussi | A New Contextual Based Feature Selection[END_REF], which selects descriptors with high relevance and low relevance descriptors on a set of examples.

Adaptation Algorithm

The algorithm (algorithm 1) relies on the context model, the descriptors hierarchical model and the dependency relations. This algorithm checks descriptors one by one. The substitution's adaptation, by generalization and by specialization, will be taken into account in the algorithm. Algorithm 1. Adaptation algorithm 1. If by browsing through the list, a value of "DR = high" is found then the couple is selected and classes of " ret j Ds " and " ret i ds " are compared. If they have the same parent class, the influence of this substitution will be considered in " ret j Ds " and the algorithm will assign this new value to ret j Dt , otherwise, the algorithm looks at the context list descriptors and selects the "dt i " descriptor which belongs to the same parent class as " ret j Ds ". The target descriptor is denoted "dt*". Then, the value of the reminders will be determined and will be thereafter affected to "Dtj". 2. If in the list there is only DR = low then the algorithm selects the parent class of Ds solution which will be affected thereafter to "Dtj".

3. Finally, when all DR values are equal to "no relation" then there is no adaptation.

Retrieval and Adaptation Illustration

The diagnostic cases modeling took a specific form, described in Table 3. The three kind of adaptation which will be approached are illustrated in this Section.

Kind 1: DR = High and same functional class

Suppose a failure occurred in the D9 detector. The retrieved source cases closest to this target case 1 (noted "T 1 ") provide two sources cases: the source case 4 S 4 and the source case 9 S 9 (see Table 3).

Table 3: 3 kinds of adaptation.

The applied similarity threshold is 60%. Similarity Measure R M and adaptation measure Calculation A M are shown in Table 4. We observe at the same time that the similar source cases are S4 and S9 and they have the similar value of R M , but differ in the A M value It is important to note that the DR value of the pair (Ds 2 , ds 3 ) is "DR = high" and that the D8 detector which is shifted of the Ds 2 descriptor as well as the D9 detector of the descriptor dt3 belong to the same class "presence sensor" (Table 4 bis).

Kind 2: DR = High and different functional class

Suppose a failure occurred in the "post zone" and represented by target case 2, denoted "T2"in Table 5. The results of the calculation of retrieval and adaptation measures are the following:

According to the results of the adaptation measure(s), the selected source case for the adaptation phase is case 6, S6. The DRs values of the source case 6 are as follows :(Table 5bis) Ds4: Indexer of the post zone = Ft(ds 1 : post zone, ds 2 : indexer). Ds2: Balogh1 (strong magnetic fields) = Ft(ds 5 : STP4; DR 52 = high, ds 6 : D6; DR 62 = low).

We note that the DR value of pair (Ds 2 , ds 6 ) is "DR = High" and "D6" and "Balogh1" components do not belong to the same family. This confirms that we are in the scenario of: "DR = High and different functional classes". By applying the adaptation algorithm, we obtain:  The class of source solution descriptor " 2 ret ds " is "Magnetic detector"

 The "STP4" component of the target descriptor "dc5" (which corresponds to the descriptor "ds5" which is in abnormal mode) is in the context of the "Indexer of the post zone". However, in this zone there are other components: "D1, pusher, D5 and Bal0"  The "Bal0" component belongs to the same class of Dsret2 which is "Magnetic detector"  Substitute the "Bal1" value of the " ret ds 2 " descriptor by the "Bal0" value of "dc7". Thus, ret ds 2 = Bal0 (strong magnetic fields)  Assign this value to "Dc 2 ": Dc 2 = Bal0 (strong magnetic fields)

The solution is as follows:

By applying the adaptation algorithm the solution is as follows:

The failed component is: The Balogh: Bal0, belonging to the "Magnetic detector" class, which is located at the indexer of the post zone. The repair action is: Clean the Balogh: Bal0.

Kind 3: DR = Low

Suppose a failure occurred in the "external ring" and represented by target case 3, denoted by T3 in the Table 6. The results of the calculation of retrieval and adaptation measures are the following: The DRs values of the source case 7 are as follows (Table 6 bis) Ds4: External conveyor of exit external ring = Ft (ds 1 : exit of external ring, ds 2 : external conveyor). Ds 2 : Blocked puller = Ft(ds 3 : Bal1; DR 32 = low, ds 5 : STP 5 ; DR 52 = low, ds 6 : D7; DR 62 = low, ds 7 : Bal1; DR 72 = low).

We note that all couples (Ds 2 , ds 3 ), (Ds 2 , ds 5 ), (Ds 2 , ds 6 ) and (Ds 2 , ds 7 ) have values of "DR = Low" By applying the adaptation algorithm, we obtain the following steps:  The solution descriptor " ret Ds 2 " is from "pneumatic actuator" class.  The context of the target case components "D4, STP3, D5 and Bal1" is located at the external conveyor of the secondary ring. Furthermore, we have found through these three examples that the source case which is the most similar to the target is not necessarily the case chosen for the adaptation phase We applied this method for a car system with a diesel motor 1.5 dCi K9K 105ch of from Renault [START_REF] Chebel-Morello | A methodology to conceive a case based system of industrial diagnosis[END_REF]. In order to validate the results obtained by using our method and to compare it with a traditional one that uses simple retrieval and adaptation phases, we will conduct a validation method. This method will be described in the next section.

Validation & Discussion

In this section we present three experiments concerning our case based reasoning system:

The first one concerns the need for the adaptation phase in our system. Accuracy of the diagnostics system with and without adaptation phases is compared.

The second one compares the computational speed of the system in the adaptation guided retrieval AGR phase with the retrieval classical phase.

The second one evaluates the computational speed of diagnosis reasoning (retrieve+ reuse phases) and compares it in the adaptation guided retrieval AGR phase with the classical retrieval phase.

And the third one studies the performance of the adaptation algorithm. The main goal of these experiments is to prove the feasibility of the proposed method of diagnostics composed of a case base, knowledge model, AGR and adaptation method. We used a leave-one-out cross-validation method for the first two parts to assess SISTRE's ability to accurately adapt retrieved cases for a case base containing 125 cases.

Accuracy of the adaptation phase

In this part, the accuracy rate with and without adaptation will be calculated. The results show that the proposed method with the adaptation selects the cases which are the best adaptable ones and thus it gives an accuracy rate of 88% (See Fig. 5 and Fig. 6). If the adaptation algorithm is powerful, one can get a good performance concerning the CBR system applied to a limited number of cases.

AGR meth

This experim

To evaluate and to comp We start w y, we apply easures. Th ty measure Fig. 7 sho ion algorith microproces The algorithm of adaptation, which is in relation to the knowledge of the system SISTRE through the models of knowledge, reacts differently depending on the AGR principle.

Fig. 7: Analysis of the adaptation-guided retrieval method According to Fig. 7 which shows the elapsed adaptation time in ms, we note that the adaptation effort is less important when the AM adaptation measure is employed (in the AGR case). In fact, the larger the case base it, the more important the variation of the adaptation effort is. Consequently, the adaptation measure which is resulting from the AGR principle decreases considerably the adaptation effort. Moreover, this principle allows selecting the good case that will lead easily and correctly to the given final solution according to the equipment knowledge context. Moreover, retrieval using only the similarity measure without adaptation does not select the cases which are the best adaptable ones (in particular concerning their classes). These results also show that the most adaptable is not necessarily the most similar.

Adaptation phase

This experiment is designed to study the accuracy of the help diagnostics system; overall accuracy, and more precisely the accuracy of only retrieval cases. The results of this study are shown in Table 7 Table 7: Results of the adaptation.

We note that the accuracy rate 88% reflects that 110 cases were adapted correctly to the set of 125 cases. This accuracy is computed using "Ds 2 " as the component responsible for the failure. By analysing the results, one can find that there are 5 retrieval failure cases. These cases are considered as generic cases that are covered by no other case of the case-base. Thus, their adaptation is impossible.

To evaluate the performance of the adaptation algorithm, these 5 cases are not considered in the accuracy calculation. For this subset of cases the accuracy rate is 91.66%. These algorithms can be applied to any type of discrete event systems (transfer systems). To prove the genericity of this method on any type of industrial plant, we applied these algorithms to another type of equipment, a diesel engine, but on a limited number of cases [START_REF] Haouchine | Rememoration guide par l'adaptation et maintenance des systèmes de diagnostic industriel par l'approche du raisonnement partir de cas[END_REF].

6.4

Comparison with other in industrial diagnosis system.

State of art in industrial diagnostic.

Concerning the reasoning process in the industrial diagnosis system, Gas Turbine, IRACUS and NodalCBR systems do not use adaptation: they offer directly the solution of the retrieved case. These systems are not effective because the retrieved case is not necessarily the chosen case for adaptation. As for Cassiopee, Pad'im (Mille et al, 1999) and Patdex systems, they propose simple adaptation rules and cannot ensure an accurate solutions' result of the adapted cases. FormTool [START_REF] Cheetham | Case-Based Reasoning in Color Matching[END_REF] exploits the relation between the retrieval and the adaptation phases. However, even if FormTool relies on this relation, the technique used is strictly dedicated to the treatment of plastic colors following a specific function. Consequently, it is not easily exploitable in other fields and cannot adapt other cases outside the characteristics of plastic colors.

Finally, the Creek system is the most effective among them because it proposes an explanation oriented retrieval and it exploits more complex and more complete relations in its adaptation phase thanks to models. In the retrieval step, the Creek system uses a similarity measure combined with measures related to the computation path in a semantic network. Concerning the adaptation step, it does not give any algorithm and it does not show how this step is carried out. The adaptation begins by activating a set of cases that are matches, and the step that refines the selection of a subset of cases by exploiting the non-identical explanation characteristics of each case. Lastly, the final focusing step is based on the solution and that's by selecting the best case corresponding to the request and by copying or modifying the solution of this case. The adaptation phase is complex and is usually designed for a specific application.

Comparison methodology

To compare our diagnostic method to other CBR diagnostic method, we define three indicators linked to the elicitation and reusing knowledge in the new application.

CBR systems differ from each other in the development tool, the formalization of the case, the use of knowledge models, the different phases of the CBR cycle and the different algorithms and methods used in each step. Table 8 summarizes these points associated with the effort of creating the knowledge models the cost of adaptation and the cost of re-use. 1°-The effort of creating the knowledge models can be quantified by a development cost.

To use development systems one needs to buy the system, to understand it and develop models. This requires expert availability. We will identify a cost of 8 on a scale of 0 to 10. However, representing expert knowledge from scratch will cost 10. The creation of the knowledge model without the development system relies on the analysis of functional and dysfunctional models like Creek and our method requires less effort to model the creation. We can estimates the cost of this complex model to be 3 2°-To estimate the capacity to reuse the cases in the retrieval phase, we define the easiness of the adaptation by a cost of adaptation. Adaptation guided retrieval method takes into accounts the cost of adaptation to select the most suitable course. The adaptation cost will be minimized (= 0) in this case. When there is an adaptation in the CBR system, the cost of adaptation is equal to 5 When there is no adaptation in the CBR system, the cost of adaptation is equal to 10. Creek system is the most effective among the other diagnostic methods because it proposes an explanation oriented retrieval and it exploits more complex and more complete relations in its adaptation phase thanks to models.

3 °-For the re-use of the program of adaptation in the new application, the cost of re-use will depend on the generality of Adaptation methods.

If there is no algorithm of adaptation or there is a specific algorithm of adaptation the cost is maximal because a new algorithm must be developed. The cost of re-use is maximal, it is equal to 10.

If the application takes support on specific rules or models of knowledge the cost can be averaged, and it is equal to 5 When a method is independent of the application, as it is the case for our method, the cost is minimal and it is equal to 0.

Applying these criteria to the methods we obtain Table 8:

We can see that our method was implemented to minimize the cost of designing a knowledge system and therefore it gives better results than other methods.

Conclusion

A knowledge oriented diagnostics system integrated in the e-maintenance platform is proposed in this paper. The case based reasoning system is well adapted to solve fault diagnostics issues because problems are recurrent and cases can be reused. A state of the art in the frame of case based reasoning for technical diagnostics highlighted that there is no common method to build a diagnostic support system. To conceive this system, we have followed the method of Rasovska which is to develop the two processes of Lamontagne model (offline and online processes):

1. Firstly we developed the following knowledge containers: (i) vocabulary that takes into account the definition of diagnostics.(ii)case base that formalizes the experience;(iii)two similarity metrics (one for the retrieval phase and one for the adaptation phase) and knowledge adaptation with two models. We set up a formalization of the cases and we associated to the descriptors a hierarchical model. This model is common to problem and solution descriptors of the case-base cases and a model relating to the application context. All steps depend on the cases formalization and the associated knowledge models. The elaboration of this expert knowledge has a cost, but this cost is minimized because the first model which is the component taxonomy model is well known by the maintenance users. However, the second model requires a minimum of work. This modeling has influenced the proposed similarity measure as well as the adaptation measure. The latter is directly related to the functional mode of the supervised components (an attribute specific to the descriptor).

2. Secondly, concerning the reasoning process, an adaptation-guided retrieval method has been proposed. The retrieval phase is related to the adaptation phase using the conjunction of similarity and adaptation measures. This conjunction makes it possible to select among the retrieved cases the most adaptable. The adaptation phase will exploit the dependency relations between the problem and the solution. These dependency relations will be given either by the selection of a relevant descriptor or by the use of a context model between the various failures which can appear in an industrial plant. And the results depend on this relation. One difficulty to overcome in this method is to define these relationships in the case base. The adaptation algorithm detects three scenarios and proposes associated actions to each case. We detailed one scenario where the most similar case is not necessarily the one selected for the adaptation.

Our previous studies have enabled us to formalize the case of a supervised industrial system of pallets transfer (SISTRE) in this paper and in a diesel engine, but on a limited number of cases. The proposed adaptation-guided retrieval and adaptation methods for a diagnostic application have been validated on a set of 125 cases resulting from the study carried out on SISTRE.

We have proved the feasibility of this diagnostic support system. To build it in any type of industrial equipment, two knowledge models need to be elaborated. To avoid the cost of the development of knowledge models, we are currently working on the use these algorithms with models (functional events and components models) developed in a webmaintenance platform. This model is defined in the domain ontology of maintenance, in the context of Semantic-maintenance and life cycle (SMAC) Project.

NOWLEDG

This work w nance and li rland. path through the system. The feasibility of our approach was studied in Section 6 through 125 generic cases q. Functional analysis of the system has identified main, additional (compelmentary) and secondary functions. Main functions are as follows: transfer assembly parts between stations; identify pallet and product lines; store product information; transform Content of the pallet. Secondary functions secure the transfer system; provide pneumatic energy and electrical energy. Secondary functions are concerned with adding/removing pieces of pallets, and managing assembly lines.

NDIX A: T AGNOSTI

The base case is studied in relation to the functional analysis of the level 2 on a station which is shown in Figure 9.

The studied case base is in relation with the internal functional analysis of level 2 relative to the working station that is represented in figure 9.

Fig. 2 :"

 2 Fig. 2: A part of the SISTRE's components hierarchy.

Fig. 3a :

 3a Fig. 3a: A part of contextual graph of the equipment. Fig. 3b : A context model of "Ds 1 "

"

  A feature d k is strongly relevant to sample N if there exist examples A and B in N that differ only in their assignment to d k and have different labels (class) (or have different distributions of labels if they appear in N multiple times). Similarly, d k is strongly relevant to target D class and distribution D if there exist examples A and B having non-zero probability over D that differ only in their assignment to d k and satisfy D class (A) # D class (B) (Blum and Langley et al, 1997 definition's pages 248-249)"."A feature d k is weakly relevant to sample N (or to target D class and distribution D) if it is possible to remove a subset of the features so that d k becomes strongly relevant)(Blum and Langley et al, 1997 definition's pages 248-249)".

  Three possible scenarios are treated differently by the algorithm:  DR = high and same class of problem and solution descriptors.  DR = high and different class of problem and solution descriptors  DR = Low This algorithm deals with the adaptation of one descriptor at a time. It is conditioned by the solution descriptor class found at retrieval step. After the retrieval phase which makes it possible to select a retrieved case ( to both RM and AM measures, the adaptation phase is launched. The initialization step creates a list of couples having a relation either high or low. According to the nature of the relation, the treatment differs. Consequently, the second step will depend on the DR values and the classes of the descriptors.

  , it identifies the dt i descriptor belonging to the same parent class as ret j Ds which will change status (dt i  dt*). After that, the relationship dt* will influence the transformation of the ret j

  target solution Dt 2 . By applying the adaptation algorithm the solution is as follows: The failed component is: the detector D9, which is located in the entry of the external ring The repair action is: Replacement of the failed component.



  In this context, the component pertaining to the same context and which belongs to the same class of ret Ds 2 (pneumatic actuator) is the component: "pusher".  Substitution of the "puller" value by "pusher" value in the descriptor 2 Assign this new value to the "Dc2" descriptor.By applying the adaptation algorithm the solution is as follows:The failed component is: The pusher in the external conveyor of the exit external ring The repair action is: Unblock the pusher.

Fig

  Fig. 5:

  Fig

  Fig

  Fig.

FigFP5:

  Fig. 9: Functional Analysis Internal Level 2 (Post)

  : P

	ng in this m method, and	unlike mac chine learni ing methods s where the e learning p hase has
	ficant cost an nd requires	time, can st tart with an n incomplete e case.
	ore designin ng a diagnos stic knowle	dge oriente ed system re equires time e and availa ability of
			to explicit		their practi ce. Howeve er, the cost	of this serv vice is very y high, and t thus this
	needs to b be optimize ed. Indeed,	the challe enge in kn nowledge m managemen nt is the
	dge gatherin ng. AI Jaco obson and L L. Prusak(1 996) studie ed the know wledge man agement
	nizations, th heir predict ion on the	knowledge	cost for th e year 2010 0 was € 2.6 6 billion.
	tatistical stu udy involve d more than n 200 know wledge work kers of diffe ferent organ nizations.
		hows that 3 37.7% of the e time of kn nowledge w workers is de edicated to t the elicitatio on phase
	9% to the k knowledge a adaptation p phase.
	sc cheduling			s searching fo r	e eliciting
	mee etings with			knowledge	kn nowledge
			experts			10,2%	from
			6,2%			e experts
						37,7%
						adapting
						k knowledge
						gained
						45,9%
	Percentage o of knowledge e workers 'tim me spent (Ja acobson, A., P Prusak, L. 19 996 )
	Our pro oblem is to	develop a	knowledge e based dia agnostic sys stem for SI ISTRE a su upervisor
	industri	al system	of pallet tr ransfer (see e APPEND DIX A) w while minim mizing deve elopment
	costs, th his can be		achieved th hanks to th he use of an n existing m method. To o reuse an a adequate
	method, , we briefly y review som me of the CB BR industri ial diagnosis s system.
	Industri ial systems	developed d in the C CBR diagno osis, can v ary from a a car studi ied in a
	diagnos stic techniqu ue system	named CR REEK (Aam modt, 2004) ) to a locom omotive def fined for
	remote		diagnostics s (Varma,	1999), to a aircrafts (su uch as Boe ing 747 air rcraft devel loped in
	CaseLin ne (Watson		et Marir, 1 1994) and u used as a de emonstrator	by British	Airways or r Boeing
	737 in		CASSIOPE EE (Bergma an et al, 20 003) owned d by CFM	Internation nal). There	are also
	industri	al printers		studied by	Domino U UK Ltd in C CHEKMAT TE and pre	sented in ( Grant et
	al,1996) ), gas turb bines studie ed in a fau ult system	of Genera al Electric	Energy in	Atlanta
	(Devane ey et Cheet tham, 2005 ) and comp plex machin ne like Patd dex (Richter r, 1991) an nd Nodal
	CBR (C Cunningham m,1994). Th here is a w wide variety y of method ds ranging	from class sification
	problem ms when t there is ju ust a weak k domain	theory, to o knowledg ge based	systems.
	More k knowledge--oriented s systems su uch as Ga as Turbine, , Creek, C Cassiopee,
	NodalC CBR, Patdex x, use mode els of know wledge diffe erent from e each other.	In most sy
	case cha aracterizes a a diagnostic c experience e.
	We no ote that ther re is no co ommon met thod for bu uilding a C CBR system m. The cons
	depends s crucially		on the repr resentation	of cases a nd knowled dge models s of the app
	domain	. That is p phases of C CBR cycle. . Actually,	authors pr rovide us j just with th
	represen ntation of th he system an nd without	any associa ated method dology.
	Very fe ew authors	defined th he design m methodolog gy in the c case based d diagnosis
	Lamont tagne & L apalme (La amontagne, , 2002) rep present a g generic mo odel of cas
						2

Table 1 :Table 1 :

 11 The descriptor associated to the puller in a diagnosis case can be written as illustrated in example of source caseWe can see in Table1a case containing in the supervisor descriptor the first symptom relative to the pusher ds 3 .

	  ii dT d t 	  il dS		i d s
	value i  state FM l i l i l d S d S d S        ,, value state FM ii il d S  ii d T d T d T d T dt 	, i ds i state , state dt value i ds , value i dt , FM ds i FM  i

  For example: When the descriptor values are on the same level.

	sim d	value il S d	value i	T	value i sim ds  value i dt	value i when ds		value i dt
									0,8  value ii value when level ds level dt
										0,6	when	1  different level		, ii value value ds dt
	 sim ds ii ,0 , 8  value value dt  value i if ds	1 D	and dt	value i		D	2
	When descriptor's values have parents with the same level.
	 sim ds ii ,0 , 6 value value dt  value i if ds  see Fig.4	1 D	and d	value i		1 Bal
								Magnetic sensor
									0.4
							Detector		Balogh
									0.6
						D1	D2	D9	Balogh0	Balogh1	0.8
							Class	Instance
					Fig. 4: Example of a descriptor hierarchy.
	For the descriptor value	state i ds and		MF i ds
	 sim ds ii ,1  state state dt	if	state i ds		dt	state i
				0	if	state ii state ds dt

  The global similarity measure (1) is obtained by aggregation of these functions on the whole set of descriptors. From this measure, a set of cases can be selected.

	value i 11   ,,  p m value value value ii ii value 1 ii p m i sim ds dt sim ds dt sim   presence sim ds dt  , , state state ii value value    i j sim ds dt ,,   FM FM value i i p r e s e n c e ii value sim ds dt ds dt            d value i ds Where p represents the number of localization descriptor   , Ml RS T  (1)
	m: represents the number of problem descriptors.
	-Similarity Measure Calculation
				12	3	5	7
		41
	sim	presence		value i ds	,1 value i dt  If information is present both in S l and T  = 0 if an information is not present in one descriptor

Table 4 :

 4 AM and RM calculation of case 1.

		T1 S4	T1 S9
	RM	(0+1+0,8*1+1+1)/5 = 0,76	(1+1+0+1+0,8+1)/6 = 0,8
	AM	(0,8*4+1+1)/3=1,73	(0,8*2+1+0,8*1+1)/4=1,1

Table 4bis :

 4bis Context of the case.

	case 4 Ds 2	Identification of the failure component
		ds i	ds 3	ds 4	ds 5	ds 7
	subst. ds3 by	value S4	D8	Puller	STP6	Bal1
	dt3	value T1	D9		STP6	Bal1
		DR(Ds2;dsi) high	low	low	low
	Result=D9	DS 1 ( Class)

Table 5 :

 5 AM and RM calculation of case

		T2 S3	T2 S6	T2 S8
	RM	1+0+0,6+0,8+1)/4 =0,85	(1+1+0+0,8*0)/4=0,5	(1+1+0,6+0,8+1+0,8*0)/6=0,733
	AM	(0,6+4*0,8+1)/3=1,6	(1+0,8*4)/2=2,1	(0,8+4*0,8+1+2*0,8)/4=1,65

Table 5bis :

 5bis Context of the case.

			Identification of the failure
	case 6 Ds 2		component
		descriptor ds 5	ds 6
	subst. ds3	value S6	SPT4	D6
	by dt3	value T2	SPT4	D5
		DR	low	high
	Result= Bal0	D 1 (S6)	Electrical actuator	Presence sensor
	D 1 (T1):	magnetic detector	DS 2 : T1 Bal0

Table 6 :

 6 AM and RM calculation of case 3.

			T3 S5		T3 S7
	RM (1+1+0+0+1+1)/6=0,667 (1+1+0,6+0,8*0+0,8+1)/6=0,733
	AM	(0,8+0,8+1+1)/4=0,9	(0,6+0,8*2+0,8+1)/4=1
			Table6bis: Context of the case
				Identification
				of the failure
			Ds 2	component
	case 7		descriptor	ds 3	ds 5	ds 6	ds 7
	contextual		value S7	Bal1	STP5	D7	Bal1
			value T3	D4	STP3	D5	Bal1
			DR(Ds2;dsi)	low	low	low	low
	subst. ds3 by dt3	D 1 (S7)	magnetic detector	Electrical Actuator	Presence sensor	Magnetic Sensor
	Result=Pusher		D 2 (T3): dt3	Pusher		D 1 (T3)	Pneumatic Actuator

Table 8 .

 8 Comparison of the CBR diagnostic system.

Retrieval descriptors problem.

Retrieval descriptors solution.


Marie-Hélène Corréard

Mathieu Mangeot

Marie-Helene Correard

¶ -Mathieu Mangeot-Lerebours

XML -A Solution For LDBs, Eds and MRDs?

published or not. The documents may come

Introduction

Dictionaries and other lexical resources are a key element of NLP (natural language processing) applications. Often they come from different sources and in different formats.

Currently, users of lexical resources must either write their own dictionaries, not a trivial task, or process them to make them compatible with their own environments and applications.

This paper describes how the use of DML could simplify working with lexical resources, make possible their reuse and improve their shareability. Firstly, the available resources will be briefly examined, secondly the solution that was adopted to "unify" these resources will be shown, including a look at constraints and requirements and thirdly concrete examples will be presented before concluding on the future of such an approach.

Starting point

Dictionaries used in NLP vary greatly, according to the final purpose of the application they are used by, but essentially they are of two kinds: either they were designed specifically for computer applications or they were written for human users. In this paper the word dictionary is used to refer to ordinary, general language dictionaries created for humans whose texts exist in electronic format.

First, a quick survey was performed to find out more about what kind of lexical resources were needed and how the available ones could be improved. Accessibility of the data was one of the items that came high on the list of possible improvements. One way to make the data more accessible without altering the contents was 'standardisation' of the format. Then, several dictionaries were used to test this approach.

A brief description of each one of them is given below. • The Oxford-Hachette French Dictionary (OHD) is a bilingual dictionary. It consists of two sections roughly equal in size: French-English and an English-French. The dictionary is encoded in SGML. Its structure is fairly complex; a great number of elements are embedded. • The New Oxford Dictionary of English is a new monolingual dictionary. It contains most of the elements of a monolingual dictionary, including etymology, sample material and encyclopedic information. • The Password semi-bilingual English-French dictionary consists of one developed semi-bilingual section and one French index which cross-refers to English entries in which the French word is given as a translation. • The FeM (French-English-Malay) dictionary supplies English and Malay translations of the French entry. English was used as a help for lexicographers during the dictionary development.

Solution

In order to get around the difficult it was decided to adopt, at a higher level, a common format for all the dictionaries. This common standard format had to be easily readable and to make it possible to keep all the information which was present in the original format. Then tools based on this common format could be built.

This section describes the requirements and gives an explanation of how the common format was defined.

Requirements

The design of the solution was driven by a list of specifications. These specifications came from previous experiments in computational lexicography and lexicology such as the indexation of the French-English-Malay dictionary [START_REF] Lafourcade | Structured Lexical data: how to make them widely available, useful and reasonable protected? -a practical example with a trilingual dictionary[END_REF], the building of the French-UNL database [START_REF] Mangeot-Lerebours | Outils pour lexicographes naïfs (en informatique)[END_REF], [START_REF] Mangeot-Lerebours | Conception, implémentation et indexation de BaLeM, une base lexicale multilingue[END_REF] or the computerization ✂✁ ☎✄ ✝✆ ✂✞ ✠✟ ☛✡ ✌☞ ✍✞ ✏✎ ✒✑ ✏✓ ✂✔ ✖✕ ✘✗ ✚✙ ✛ ✂✜ ✣✢ ✂✤ ✒✥ ✂✦ ✏✄ ✒ ✂✧ ✩★ ✪✦ ✏✥ ✌✫ ✭✬ ✯✮ ✂✰ ✌✎ ✝✦ ✱✥ ✂✦ ✏✄ ✒ ✂✧ ✩★ ✪✲ ✳✤ ✒✴ ✏✄ ✒✤ ✝ ✂✥ ✌✦ ✏✧ ✩★ ✪ ✂✁ ☎✴ ✏ ✌✥ ✂✄ ✝✞ ✱✜ ✣✰ ✂ ✂✧ ☛✦ ✏✧ ✩★ ✪✵ ✶✧ ☛✞ ✏✥ ✌✴ ✏✆ ✪✷ ✩✸ ✶✹ ✱✧ ✺✦ ✱✗ ✻✗ ✻✞ ✱✄ ✝✼ ✌✽ ✂✾ ✺✡ It was essential to find a way to preserve all the information present in the original format of the dictionary during the conversion. The dictionaries might be used for various applications, so it was not possible to predict in advance the kind of information that should be kept or left out.

In order to guarantee a maximum of compatibility for the new format and to reuse previous work in the domain, the obvious approach was to use existing norms and standards as much as possible. Furthermore since most of the resources available at the time were encoded in SGML [START_REF] Iso | IISO 8879 (SGML) Information processing --Text and office systems --Standard Generalized Markup Language[END_REF] it seemed reasonable to try and chose a format which did not need a lot of conversion work.

On the one hand, the power of object programming as well as that of relational database query facilities were attractive. On the other hand, the opacity of data repositories and portability problems were decisive factors for the choice of a textual format, either for storage or exchange when manipulating dictionaries.

Format adopted

All these considerations led to the choice of eXtended Markup Language (XML) [START_REF] Connolly | XML Principles, Tools and Techniques World Wide Web Journal[END_REF] for encoding the dictionaries. XML is a W3C recommendation [W3C98a]. It is also UNICODE [START_REF]UNICODE) Information technology --Universal Multiple-Octet Coded Character Set (UCS[END_REF] compliant. XML makes it possible to represent a large variety of information. All these features guarantee readability, perenniality and compatibility with an increasing number of tools.

Furthermore, because XML is a subset of SGML, the conversion of SGML dictionaries, well formed according to XML, into XML is unnecessary. Also, XML is a textual format, therefore it will always be possible to read the original files encoded in XML. Now that the format is defined, a problem remains: how to encode the structure of the dictionaries? Two alternatives are possible:

Using a general DTD

The first option was to define a general DTD. This DTD would have to be generic enough to allow the encoding of all the dictionaries currently available. The conception of tools would then be easy because all of them would be based on the same DTD. This solution, despite its simplicity, was rejected because it was not possible to convert all the dictionaries following the same DTD without loss of information. It was also obvious that each dictionary has its own particular structure and, except for some rare cases, it was impossible to convert all the contents of one dictionary into another dictionary structure.

Keeping the original structure

An easier solution was to keep the original structure of each dictionary. A difficulty then rises at the stage of designing a tool for more than one dictionary. It appears quickly that each dictionary requires its specific tool. Therefore this solution does not solve all the problems.

A hybrid solution

A hybrid solution was then envisaged. XML is designed to be used with namespaces [W3C99]. It seemed appropriate to introduce a new one, specialized for dictionaries: DML for Dictionary Markup Language. This namespace is used for a hierarchised restricted set of tags. This set is composed of tags describing the same information in different dictionaries. For example, <dml:entry> always refers to an entry or <dml:headword> to the headword of an entry.

When some information in a dictionary cannot be represented with a tag from the DML set, it is still possible to copy it from the source file without transforming it. Specific tools manage it as they would the original file. If this type of information is present across several dictionaries, a new tag is then added to the DML set. The DML tags are used by the various tools as points of reference in an unknown converted dictionary.

The set of tags is composed of tags coming from standards like TEI/MARTIF [START_REF] Ide | Text Encoding Initiative, background and context[END_REF], [START_REF] Johnson | The Text Encoding Initiative[END_REF], [START_REF] Melby | The Machine Readable Terminology Interchange Format (MARTIF), Putting Complexity in Perspective[END_REF], [START_REF] Iso | ISO DIS 12620 (MARTIF) Terminology -Computer Applications-Data Categories[END_REF]; GENELEX/EAGLES [START_REF] Genelex | Projet Eureka Genelex, modèle sémantique. Rapport Technique[END_REF] and GENETER [START_REF] Geneter | Modèle générique de représentation des données terminologiques[END_REF]. The matching between a DML tag and an original tag is performed by a linguist to avoid possible conflicts between the tags.

Here is an alpha version of the DML tagset. The tags were chosen on the basis of their frequency. If an element occurred in more than 2 dictionaries (this figure may change at a later stage) it was added to the tagset. The tagset itself is evolving as new dictionaries are explored and converted. The next section shows how conversion was performed using DML tagset and how the results were exploited.

Examples

Conversion

According to the source format of the dictionary, there are three types of conversion. The simplest type occurs when the source format is well-formed SGML; the second type, when all the information is under the form attributevalue, and the third, the most complex one, relates to typographic formats which have to be parsed in order to extract as much information as possible.

Well-Formed SGML

If the dictionary is encoded in SGML and "well formed" in the XML sense (ie all opening tags are closed and the file is parsable by a context-free grammar), the conversion is very easy, since the structure is already, de facto, in XML. In this case, the only tasks are: conversion of characters into UNICODE characters set, changing the file encoding to UTF-8 and adding as much DML tags as possible.

If some information is redundant between the DML tag and the original tag, the latter is replaced and a note is kept of the changes. If the replacement DML tag is less precise than the original one, the original one remains in the text, embedded inside the DML tag. If some information is not in the same format (eg an element instead of attribute), it is altered to conform to DML and a note is kept of the changes.

The example is taken from the OHD [OUP-H94].

First, here is a sample of the entry abréger in original format: <se><hw>abr&ea.ger</hw><pr><ph>abKeZe</ph></pr><hg><ps>vtr</ps></hg><s2 num=1>(<ic>rendre court</ic>) to shorten [<co>mot,expression</co>]; to summarize [<co> texte, discours</co>]; <sl>&hw. &oq.t&ea.l&ea.vision&cq. en &oq.t&ea.l&ea.&cq.</sl> to shorten &oq.television&cq. to &oq.TV&cq; (...) </se> The headword abréger is followed by its pronunciation in Alvey notation, its part of speech vtr and its English translation to shorten then to summarize; the translations are differentiated by context (collocates). An example follows: abréger 'télévision' en 'télé' then its translation: to shorten 'television' to 'TV'.

Attribute-Value

When the original dictionary is represented by series of attribute-value pairs, the conversion remains simple. It consists in devising a DTD for the dictionary and converting the attribute-value pairs into <tag>value</tag>. Characters and file encoding are also converted.

The example for abréger below is taken from the FEM [START_REF] Lafourcade | Structured Lexical data: how to make them widely available, useful and reasonable protected? -a practical example with a trilingual dictionary[END_REF]. (:fem-entry (:ENTRY "abréger") (:FRENCH_PRON "abre-je-") (:FRENCH_CAT "v.tr.") (:FRENCH_GLOSS "un texte") (:ENGLISH_EQU "to shorten") (:ENGLISH_EQU "to abridge") (:MALAY_EQU "memendekkan") (:MALAY_EQU "meringkaskan") )

The entry after conversion looks as follows: <dml:entry><dml:headword>abréger</dml:headword> <dml:pronunciation><dml:phonetic encoding="GETA">abre-je-</dml:phonetic></dml:pronunciation> <dml:part-of-speech>v.tr.</dml:part-of-speech> <FRENCH_GLOSS>un texte</FRENCH_GLOSS> <dml:translation language="en">to shorten</dml:translation> <dml:translation language="en">to abridge</dml:translation> <dml:translation language="ml">memendekkan</dml:translation> <dml:translation language="ml">meringkaskan</dml:translation> </dml:entry>

Typographic Format

The most complex case occurs when a dictionary needs to be converted from a typographic format such as typesetters' tape, word processor, HyperText Markup Language (HTML). One particularly complex aspect these formats is that they represent knowledge designed to be readable by humans who can infer structure and disambiguate senses easily. In order to extract the information and, above all, build a deep structure for such a dictionary, a powerful tool built by [START_REF] Haï | Techniques génériques d'accumulation d'ensembles lexicaux structurés à ‡ partir de ressources dictionnairiques informatisées multilingues hétérogènes[END_REF] called RECUPDIC was used. This tool combines two methods: a string transducer and a special tree parser. The structure of the result is described as a grammar and the tool extracts as much information as possible.

Here is the entry babble from Password semi-bilingual English-French dictionary: >U43<babble >U1<[\B.270babl] >U2<verb >U23<1\N>U1<to talk indistinctly or foolishly: >U2<What are you babbling about now? >U8< bafouiller, bavarder\L >U23<2\N>U1<to make a continuous and indistinct noise: >U2<The stream babbled over the pebbles.>f5h8<. >U8< gazouiller\L and the same entry converted in XML: <dml:entry><dml:headword>babble</dml:headword> <dml:pronunciation><dml:phonetic encoding="Password">'babl</dml:phonetic ></dml:pronunciation> <dml:syntactic-cat><dml:part-of-speech>verb</dml:part-of-speech> <dml:semantic-cat num="1"><dml:definition>to talk indistinctly or foolishly </dml:definition><dml:example>What are you babbling about now?</dml:example> <dml:translation>bafouiller</dml:translation> <dml:translation>bavarder</dml:translation><dml:semantic-cat num="2"><dml:definition>to make a continuous and indistinct noise</dml:definition><dml:example>The stream babbled over the peb bles.</dml:example><dml:translation>gazouiller</dml:translation></ dml:semantic-cat> </dml:syntactic-cat></dml:entry> A summary of conversion operations is described in the

Usage

Some dictionaries do not contain information corresponding to some of these DML tags and some others contain information that is not covered by the DML tagset. However, as the tools are based on the DML tagset they will always find those elements which are represented by the tagset and present in a given dictionary, eg the <dml:headword> tag will always refer to the headword of an entry. Tools must be evolutive, to take into account the changes of DML.

As the resources are encoded in XML, all XML-compliant tools can be used. For example, a dictionary can be exported into a specific format with the help of XSL [W3C98b] or DSSSL [START_REF] Iso | IISO/IEC 10179 (DSSSL) Information technology --Processing languages --Document Style Semantics and Specification Language[END_REF]. Tree transformations operations become possible. Dictionary readability can be improved with an associated stylesheet and an XML-compliant browser. Because of the relative youth of XML, few good tools are available yet but there should be more in the near future.

Two applications realised with XML/DML-encoded dictionaries are presented below.

Dicoweb

Dicoweb is a dictionary webserver. It was designed for human usage. It is used for experiments and research 1 . For legal reasons, not all these dictionaries are accessible to the public. The Dicoweb user first selects the source language of the headword she is looking up, then she selects the target language(s). The user can select as many target languages as are available. Before consulting the dictionaries, she can process the headword through a morphological analyser. Two buttons, labelled "previous" and ""next", give access to the preceding and following entries in dictionary order. For clarity reasons, each language is visualised in a specific colour and font.

A Common Gateway Interface (CGI) script written in PERL [START_REF] Wall | Programming PERL[END_REF] works as a link between the user, the morphological analysers and the dictionaries. Dictionaries are selected according to the languages checked by the user. The files corresponding to the dictionaries are browsed by the script looking for a PERL regular expression such as: "/<dml:headword[^>]*>$ENTRY<\/dml:headword>/" where $ENTRY represents the headword entered by the user.

XML browsers are not widespread so it was decided to convert the result into HTML before sending it back to the user. The pages are built on the fly, with no breach of copyright and the possibility to modify directly the rendering of the final page.

Adding a new resource simply means adding the location of its file and the languages it covers to the script.

1 URI: http://silfide.imag.fr

XeLDA

XeLDA (Xerox Linguistic Development Architecture) was built to provide developers and researchers with a common development architecture for the open and seamless integration of linguistic services. These services may include such applications as translation aids, syntax checking, terminology extraction, and authoring tools in general.

The above sample of Password English-French semi-bilingual dictionary was integrated into XeLDA. The dictionary was converted to comply with XeLDA DTD with the help of an XSL stylesheet. Here is the result of the transformation: <xbdict> <entry> <headword><spl>babble</spl></headword> <hwinfo><pronunciation><phonetic>['babl]</phonetic> </pronunciation></hwinfo> <syntactic><senseinfo><pos>verb</pos></senseinfo> <semantic> <subsense>to talk indistinctly or foolishly: What are you babbling about now?</subsense> <subsense><trans>bafouiller</trans></subsense> <subsense><trans>bavarder</trans></subsense></semantic> </syntactic> </entry>

  table below:

	Dictionary	Format	Size (in bytes)	Time spent
	OHD -en/fr	SGML	17 Mb	1 day
	OHD -fr/en	SGML	15 Mb	1/2 day
	NODE -en	SGML	38 Mb	1 day
	Password -en/fr (letterset) typesetter's tape	300 Kb	5 days
	Password -en/ja (letterset) typesetter's tape	250 Kb	1 days
	FeM -fr/en/ml	attribute-value	9 Mb	1/2 day

Conclusion

The work presented in this paper is still under development. The current results are satisfactory. However, further investigation is needed to establish the adaptability and coverage of DML. In the longer term it is planned to build new tools that will enable a user to set personal parameters according to the task at hand.


Mathieu Mangeot-Lerebours
email: mangeot@nii.ac.jp

An XML Markup Language Framework for Lexical Databases Environments: the Dictionary Markup Language

Introduction

Lexical data resources are growing rapidely thanks to the Internet. Unfortunately, despite numerous existing standards like TEI, MARTIF, GENELEX, EAGLES/PAROLE, etc. each resource has its own format and own structure. Furthermore, the existing lexical data is generally developed for a specific purpose and can't be reused easily in other applications.

In this paper, we intend to define a complete framework for developing multilingual lexical database for multipurpose. The framework is generic enough in order to accept a wide range of dictionary structures and proposes for manipulating heterogeneous dictionaries a set of common pointers into these structures. We will first present the organisation of Dictionary Markup Language (DML) framework. Then we will describe more precisely the DML language based on XML schemata. Next, we explain how to describe dictionary macro and microstructures with the DML. Lastly, we will explain our concept of common pointers defined in a Common Dictionary Markup (CDM) set.

Presentation of the DML Framework

The DML Framework described first by Mangeot-Lerebours ( 2001) is a complete framework for the consultation of heterogeneous dictionaries, cooperative construction of new dictionaries and communication with other lexical databases or lexical data client and supplier applications. The framework is completely generic in order to manage heterogeneous dictionaries with their own proper structures. The consultation of heterogeneous dictionaries is possible as soon as they are encoded in XML, consultation of other resources via remote servers through API, possibility of adding pre-consultation help modules such as spell checking and morphological analysis before consultation or postconsultation modules like syntethisers, conjugation of verbs, learning drills, etc. Possibility of automatic consultation of the database via client API.

The construction of new dictionaries can be done by a community of contributors and validated by a group of head lexicographers specialists. The management of user profiles, preferences and weights for consultation, annotation and edition of lexical data with inheritance and sharing possibilities among groups of users is also handled by the framework. The < database> element describes a lexical database and lists the dictionaries that are stored in it.

Figure 1. Logical Organisation of a Lexical Database

The <dictionary> element describes the metadata linked to ther dictionary. It links all the volumes of the dictionary. The <volume> element describes a dictionary part. The content is principally a list of dictionary entries. For example, a bilingual bidirectional French-English dictionary will be described by only one <dictionary> element. The French->English entries will be in one <volume> element and the English->French entries in another <volume> element

The DML Language

The DML Namespace

To describe the structure of all the documents, elements, attributes and XML types, we use an XML

DML Common Types and Attributes

For some information, we define type and attributes common to all DML elements. It allows to standardize the data. The XML schemata have originally simple predefined types. We selected and reused some in our definitions.

Dates and Time

Dates are represented by the date DML attribute of the XML schema type dateType taken from the extended format of the ISO 8601 standard.

Response Delay

The delay DML attribute of an element indicate the response delay when a request has been launched on this element. This delay is a duration of the XML schema durationType type. For example, 5 seconds and 10 cents will be indicated! : "5.10S".

Unique ID

The id DML attribute of an element is a unique ID in all the lexical database. It allows to create links between elements. It redefines the XML schema ID simple type.

Modifications History

The modifications history of an element has a unique ID. The element links to its history thanks to the DML attribute history that gives the value of the history ID. The type redefines the XML schema ID simple type.

Languages Notation

To note the various languages, we use the ISO-639-2/T (T for Terminology) [ISO98] standard that defines a 3 letter code for each language (French->fra; English->eng, Malay->msa, etc.). It is far more complete that the two letters code standard ISO-639-1. We also add our proper codes like "unl" for the UNL language. This codes list represents the lang DML type. The lang DML attribute is from this type.

1 http://www-clips.imag.fr/geta/services/dml/

Documents Encoding

To note the encodings of the various documents in the database, we define the encodingType. DML type. The values are those described by the IANA (Internet Assigned Number Authority) for the encodings. These are also the values used for MIME types (Multipurpose Internet Mail Extension). Among the most used, we find ASCII on 7 bits, ISO-8859-1 on 8 bits for latin languages, Shift-Jis on 8 or 16 bits for the Japanese, UTF-8 on 8 bits for UNICODE characters, etc.

Status of an Element

The status DML attribute is used to indicate its status. The values can be among others auto if the element has been obtained automatically, rough if the element has not been revised and revised if so, etc.

DML Architecture

Macrostructure Definitions

To describe the macrostructure of our dictionaries as well as our lexical database, we use XML elements.

We principally based our definitions on the LEXARD language defined by Serasset (1994) and added some information

Description of a Lexical Database

To describe a lexical database, we use the <database> element formally described inthe DML schema.

The modifications of the <database> element and its descendants are stored in a document linked with the history-ref atttribute.

We add to LEXARD the possibility to define various users and groups in the database. At the beginning three groups are predefined! : universe contains all the users of the database, administrators contains the administrators of the database and lexicologists contains the users in charge of the control of the data.

The information relative to each user are stored in another element referenced by the <user-ref> element.

All the dictionaries of the database are referenced by pointers on XML documents that describe them. The pointers are the href attributes of the <dict-ref> elements grouped in the <dictionaries> element.

Description of a Dictionary

To describe a dictionary, we use the <dictionary> element. The modifications information is stored in a document pointed by the history-ref attribute.

We indicate meta-information on the resources.

The elements <category>, <type> and <links> describe the dictionary macrostructure.

The <category> element indicates the dictionary type (monolingual, bilingual, multilingual, interlingual). The <type> element indicates if the dictionaries are unidirectional, bidirectional or pivot based.

The <links> element indicates the links between the volumes of the dictionary. For example, if a dictionary is pivot based with 3 languages English, French and Malay, it contains 4 volumes Interlingual, English, French and Malay linked as follows: <links> !!<link from="English" to="Interlingual"/> !!<link from="French" to="Interlingual"/> !!<link from="Malay" to="Interlingual"/> </links> The dictionary volumes are referenced by their unque name. The <volumes> element gathers all the reference to the volumes files noted with the <volume-ref> element.

The source and target languages are indicated with the 3 letter code DML lang type.

The <content> element describes the content of the dictionary. The <domain> element indicates the domain covered by the dictionary (general, medecine, computer, etc.)

We indicate also the size of the dictionary in bytes by <bytes>, and the headword number by <hw-number>.

For the version management, we indicate the version number (<version>), the creation-date of the dictionary (<creation-date>) and the date of the integration of the dictionary into the database (<installation-date>).

For the non-DML resources, we need to indicate the file format (<format>) and the encoding (<encoding>). The encoding values are determined by the DML type encodingType.

We also indicate meta-information on the dictionary like the resource supplier (<source>), the owner (<owner>), the responsible at the database level (<responsible>), the rights attached to the dictionary (<legal>) and miscellaneous comments (<comments>).

The CDM (see chapter 4) elements list (<cdm-elements>) is stored with for each element, its real name in the resource and the maximal response delay. The (<corpus>) element is special, it allows to indicate that we search a string anywhere in the dictionary.

Description of a Volume

The <volume> elements gathers dictionary entries with the same source language. The modificaitons history is referenced with the history-ref attribute.

Microstructure Definitions

To represent dictionary microstructures, we propose to redefine in XML the structures defined with LINGARD (see serasset (1994).

Trees

To represent a dependance tree associated to the sentence "Le chat mange une souris.", for example, we can use a "decorated node" <dn> with attributes corresponding to the grammatical variables. <dn ul="manger" time="present" aspect="imperfectif"> !!<dn ul="chat" determ="defini" gnr="masc" pos="-1"/> !!<dn ul="souris" determ="indefini" gnr="fem" pos="+1"/> </dn>

Links

The definition of a link is done with the xlink standard [XLink 1.0]. We alslo add our attributes:

• The attribute type="bidirectionnal" or type="oriented" indicates if the link is bilingual or not;

• The attribute id is of the DML id type. It allows to attribute a unique id for each link; • The content text of the element allows to tag the links. Here is a link example: <link type="oriented" id="l001" href="example.xml#xpointer(//node[x l:label='n002'])"/> The reference to the external element is done with the href attribute. The reference is noted as a URI. If the object does not have a unique id (id), the link is described with the [XPointer] standard. Otherwise, it is pointed as follows: <link type="oriented" id="l001" href="example.xml#n002"/>

Graphs and Automatons

The xlink standard [XLink 1.0] is used to describe arcs. The arcs type is oriented type="oriented" or bijective type="bijective". The source and the target of the arc are noted with the node identifiers from="n001" and to="n002". The definition of an automaton follows the definition of a graph. The starting node is noted with the xl:title="starting-node" attribute. The ending nodes are noted with the xl:title="ending-node" attribute.

Functions

The following example represents the lexical function [lambda]x1 (CausOper 1 x0x1). The results of its application to the French lexie DÉSESPOIR are the following: pousser, réduire quelqu'un au désespoir, jeter quelqu'un dans le désespoir, frapper quelqu'un de désespoir. The function is noted in XML as follows:

<function name="CausOper 1 "> !!<arguments> !!!!<first value="desespoir"/> !!</arguments> !!<valgroup> !!!!<value>pousser</value> !!!!<value>réduire [qqun au désespoir]</value> !!!!<value>jeter [qqun dans le désespoir]</value> !!!!<value>frapper [qqun de désespoir]</value> !!</valgroup> </function>

Feature Structures

If the features are typed, the type is noted with an attribute. If the feature has several values, the element is duplicated. <feature1 type="type1">valeur1</feature1> <feature1 type="type2">valeur2</feature1>

Sets and Disjonction

Sets and disjunctions are defined directly at the XML schema level with the two elements <xsd:choice> and <xsd:sequence>

Basic Types

The basic type of an XML document is the character string. Thanks to XML schemata, we can use many other basic types like boolean, entity, decimal, float,etc.

The Common Dictionary Markup Subset

We defined a subset of DML element and attributes that are used to identify which part of the different structures represent the same lexical information. This subset is called Common Dictionary Markup (CDM).

Definition of the Subset

The DML framework may be used to encode many different dictionary structures. Indeed, two dictionary structures can be radically different.

Conclusion

This framework has been extensively used for the Papillon project (see Serasset & Mangeot-Lerebours ( 2001)) of mutualized construction and consultation of a pivot multilingual lexical database. This experiments allowed us to correct and adapt some parts of the DML. Nevertheless, the framework need to be opened to the public in order to receive feedback and comments.

We plan to open a web site dedicated to the DML soon.

Annexs

!</users> !!<groups> !!!!<group!name="universe"> !!!!!!<user-ref!name="Mathieu.Mangeot"/> !!!!!!<user-ref!name="Mutsuko.Tomokiyo"/> !!!!</group> !!!<group!name="lexicologists"><user-ref!name="Mutsuko.Tomokiyo"/></group> !!!!<group!name="administrators"><user-ref!name="Mathieu.Mangeot"/></group> !!</groups> !!<dictionaries> !!!!<dict-ref!name="FeM"!href="FeM.xml"/> !!!!<dict-ref!name="Papillon"!href="papillon.xml"/> !!</dictionaries> </database>
Annex 2: XML Document Describing a Dictionary <dictionary!! xsi:schemaLocation="http://clips.imag.fr/geta/services/dml http://clips.imag.fr/geta/services/dml/dml.xsd" category="multilingual" creation-date="21/1/97 00:00:00" encoding="ISO-8859-1" format="rtf" hw-number="192460" installation-date="23/06/99 15:04:00" fullname="dictionnaire français-anglais-malais" name="FeM" owner="GETA" type="unidirectional" version="1"> !!<languages> !!!!<source-language lang="fra"/> !!!!<target-language lang="eng"/> !!!!<target-language lang="msa"/> !!</languages> !!<contents>general vocabulary in 3 languages</contents> !!<domain>general</domain> !!<bytes>9106261</bytes> !!<source>ML, YG, PL, Puteri, Kiki, CB, MA, Kim</source> !!<legal>all rights belong to ass. Champollion</legal> !!<cdm-elements> !!!!<headword!delay="1s"/> !!!!<pronunciation!delay="5s"/> !!!!<part-of-speech!delay="5s"/> !!!!<translation!lang="eng"!delay="5s"/> !!!!<translation!lang="msa"!delay="5s"/> !!!!<corpus!delay="10s"/> !!</cdm-elements> !!<administrators><user-ref!name="Kim, ML"/></administrators> !!<volumes><volume-ref name="FeM" href="fem_fr_en_ms.xml"/></volumes> </dictionary>

! ! ! <activities> ! ! ! ! ! ! <activity dictionary="FeM">interface</activity> ! ! ! ! ! ! <activity dictionary="Papillon">administration</activity> ! ! ! ! </activities> !! </profiles> !<credits>10</credits> !!<annotations! href="mangeot-ann.xml"/> ! ! <contributions> ! ! ! ! <contribution source="French.xml" href="mangeot-cnt1.xsl"/> ! </contributions> !!<requests! href="mangeot-req.xml"/> !!<xml-stylesheet! type="text/css"! href ="mangeot-sty.css"/> !!<groups> !!!!<group-ref name="universe"/> !!!!<group-ref! name="administrators"/> !!</groups> </user>
Annex 5: XML Document Describing a supplier API <api! type="supplier"! category="consultation"! name="JMDict_en-ja"> !!<info>Dictionnaire japonais-anglais de Jim Breen</info> !!<url! href="http://www.csse.monash.edu.au/cgi-bin/cgiwrap/jwb/wwwjdic"/> !!<protocol! type="get"/> ! ! <delay! min="1s"! average="1s"! max="2s"! timeout="10s"/> ! ! <encoding! input="UTF-8"! output="EUC-JP"/> !!<format! input="txt"! output="html"/> !!<arguments> !!!!<element! name="source-language"> !!!!!!<complexType> !!!!!!!!<restriction! base="string"> !!!!!!!!!!<enumeration! value="jpn"/> !!!!!!!!!!<enumeration! value="eng"/> !!!!!!!!</restriction> !!!!!!</complexType> !!!!</element> !!!!<element! name="headword"! type="string"/> !!!!<element! name="regex"! type="boolean"/> !!</arguments> !!<result><element! name="output"! type="string"/></result> </api> Annex 6: XML Document Describing a client API <api! type="client"! category="consultation"! name="getabase"> !!<info>API de consultation de la base lexicale du GETA</info> !!<url! href="http://www-clips.imag.fr/cgi-bin/geta/dicoweb mailto:dicoweb@imag.fr telnet://www-clips.imag.fr:2628"/> !!<protocol! type="post get mailto DICT"! login="anonymous"/> ! ! <encoding! input="ASCII ISO-8859-1 UTF-8"! output="UTF-8"/> ! ! <format! input="txt xml"! output="xml html txt"/> !!<arguments> !!!!<element! name="name"! type="string"/> !!!!<element! name="source-language"! type="lang"/> !!!!<element! name="word-order"! type="string"/> !!!!<element! name="cdm-elements"! type="string"/> !!!!<element! name="context"! type="positiveInteger"/> !!!!<element! name="input"! type="string"/> !!</arguments> !!<result> !!!!<element! name="output"> !!!!!!<complexType> !!!!!!!!<sequence><element! name="article"! type="articleType"/></sequence> !!!!!!</complexType> !!!!</element> !!</result> </api>

  namespace [XML Namespaces]. Our namespace is called DML for Dictionary Markup Language. The namespace URI points to an XML schema [XML

	Schemas] describing the contents of the namespace.
	It is available online 1 to allow users to edit and
	validate their files online with an XML schema
	validator.
	<MyElement
	xmlns:dml="http://www-
	clips.imag.fr/geta/services/dml">
	!!...
	!!<dml:MyDescendant/>
	!!...
	</myElement>
	Figure 2: Usage Example of the DML Namespace
			Database
			Dictionary
	History	User	Volume	Client API	Supplier API
		Group
			Entry		CDM set
					•headword
		tree automaton graph function link	Basic Types •integer •boolean	•pos •pronunciation •translation •example
				•date	•idiom

Table 1 : CDM Elements Subset 4.2. CDM Correspondance Examples When

 1 a resource is recuperated, a correspondance table is established between the original element names and CDM elements. The table 2 has been used for the FeM, OHD and NODE dictionaries.

	So, in order to
	handle such heterogeneous structures with the same
	tools, we need a common formalism. Standards like
	TEI [Ide95], MARTIF [Melby94], [ISO99];
	GENELEX/EAGLES [GENELEX93] and
	[GENETER] aim to be universal but very few
	resources implement them.

Table 2 :

 2 Equivalents of the CDM elements in the FeM, OHD and NODE

Annex 1: XML Document Describing a Database

	<database!xsi:schemaLocation="http://www-clips.imag.fr/geta/services/dml
	http://clips.imag.fr/geta/services/dml/dml.xsd"
	name="GETA Lexical Database"
	creation-date="22/10/99"
	owner="GETA">
	!!<partner-servers>
	!!!!<user-ref!name="XRCE Analyser"!href="xrce.xml"/>
	!!</partner-servers>
	!!<users>
	!!!!<user-ref!name="Mathieu.Mangeot"!href="mangeot.xml"/>
	!!!!<user-ref!name="Mutsuko.Tomokiyo"!href="tomokiyo.xml"/>

Annex 3: XML Document Describing a Volume

	<volume
	xsi:schemaLocation="http://clips.imag.fr/geta/services/dml
	http://clips.imag.fr/geta/services/dml/dml.xsd"
	name="FeM_fr_en_ms"
	source-language="fra">
	!!<entry>…</entry>
	!!!!...
	</volume>
	Annex 4: XML Document Describing a User
	<user
	xsi:schemaLocation="http://www-clips.imag.fr/geta/services/dml
	http://www-clips.imag.fr/geta/services/dml/dml.xsd"
	name="Mathieu MANGEOT"
	creation-date="22/10/2001">
	!!<login>Mathieu.Mangeot</login>
	!!<password>toto</password>
	!!<email>Mathieu.Mangeot@imag.fr</email>
	!!<profiles>
	!! <competences>
	! ! ! ! ! ! <eng level="good">translation</eng>
	!!!!!!<fra level="mother tongue">phonetic, collocations, examples, grammar</fra>
	!!!!!!<jpn level="beginner"/>
	!!!!!!<spa level="good">translation</spa>
	!!!!</competences>
	! ! ! ! <interests><interest lang="hun,jpn"/></interests>
	!