Chahrazed Bouhini
email: chahrazed.bouhini@univ-st-etienne.fr

Mathias Géry
email: mathias.gery@univ-st-etienne.fr

Chritine Largeron
email: christine.largeron@univ-st-etienne.fr

User-centered Social Information Retrieval Model Exploiting Annotations and Social Relationships

Keywords: Social information retrieval model, social test collection, annotations, relationships, indexing

Social Information Retrieval (SIR) has extended the classical information retrieval models and systems to take into account social information of the user within his social networks. We assume that a SIR system can exploit the informational social context (ISC) of the user in order to refine his retrieval, since different users may express different information needs as the same query. Hence, we present a SIR model that takes into account the user's social data, such as his annotations and his social relationships through social networks. We propose to integrate the user's ISC into the documents indexing process, allowing the SIR system to personalize the list of documents returned to the user. Our approach has shown interesting results on a test collection built from the social collaborative bookmarking network Delicious.

Introduction

The participants of social networks are not only allowed to share Web documents but also to annotate, to evaluate and to comment them [START_REF] Volkovich | Evaluation of valuable user generated content on social news web sites[END_REF]. Social tagging data, known as folksonomies, create social association between the users and the Web pages through the social annotations [START_REF] Xu | Exploring folksonomy for personalized search[END_REF]. Social annotation is a set of tags (keywords) freely assigned by a user to describe the content of a Web document. To this end, annotations are widely considered as an effective means of enriching content with meta-data. Folksonomies can be considered as a fairly accurate source to discover user interests [START_REF] Vallet | Personalizing web search with folksonomybased user and document profiles[END_REF]. In this context, Social Information Retrieval (SIR), defined as the incorporation of information related to social networks and relationships into the information retrieval process [START_REF] Kirsch | Social Information Retrieval[END_REF], attempts to extend classical IR by taking into consideration the user's ISC within his social network. Indeed, social networks users may be seeking different informations expressed by the same queries. Thus, SIR systems can exploit the user's ISC to refine the user's retrieval. Hence, one aim of SIR consists in adapting usual IR models and systems in order to deal with this user's social data (user's annotations and social relationships). We propose a SIR model, called BM 25F S , which integrates user's ISC during the indexing step. More specifically, the user's ISC is generated out of his annotations. Then, we benefit from social relationships of the user, that we call neighborhood, to enrich the user's ISC with the annotations of his neighborhood. Once the user's ISC generated, we investigate the way to integrate this social information into the SIR model. This paper is organized as follows: we discuss some related works in Section 2 and we explain the motivations of our work in Section 3. In Section 4, we present our methodological framework by describing the approach that we used to generate the user's ISC and we detail the main contribution of our work which consists in integrating the user's ISC in the documents indexing step in order to build a personalized documents index. Further, we present some results of experiments done on a test collection generated from the collaborative bookmarking network Delicious1 in Section 5, and we conclude with some perspectives.

Related Work

Social networks provide valuable additional information which have been used to improve the results of recommendation systems [START_REF] Guy | Social media recommendation based on people and tags[END_REF], collaborative filtering [START_REF] Ferrara | Improving collaborative filtering in social tagging systems[END_REF], or information retrieval [START_REF] Hotho | Information retrieval in folksonomies: Search and ranking[END_REF]. Much related works use social informations for query expansion and disambiguation [START_REF] Schenkel | Efficient top-k querying over social-tagging networks[END_REF], [START_REF] Lin | Social in query expansion[END_REF], [START_REF] Chirita | Personalized query expansion for the web[END_REF].

In this paper, we focus on the use of these social annotations and relationships in the SIR models for IR personalization. For example, with the aim to improve Web search, Bao et al. [START_REF] Bao | Optimizing web search using social annotations[END_REF] propose two methods: SocialSimRank and SocialPageRank. The former allows to find the latent semantic association between queries and annotations, while the latter takes into account the popularity of web pages [START_REF] Bao | Optimizing web search using social annotations[END_REF].

The first step of the IR personalization aims at modeling the user's profile and social context [START_REF] Au-Yeung | A study of user profile generation from folksonomies[END_REF], [START_REF] Cai | Personalized search by tag-based user profile and resource profile in collaborative tagging systems[END_REF], [START_REF] Vallet | Personalizing web search with folksonomybased user and document profiles[END_REF], [START_REF] Xu | Exploring folksonomy for personalized search[END_REF], [START_REF] Szomszor | Semantic modelling of user interests based on cross-folksonomy analysis[END_REF]. Indeed, several studies have proven that user's ISC can be effectively harvested from the social bookmarking systems [START_REF] Au-Yeung | A study of user profile generation from folksonomies[END_REF], [START_REF] Szomszor | Semantic modelling of user interests based on cross-folksonomy analysis[END_REF]. These works assume that the documents and the tags posted by users depend highly on their interests and provide rich information for building user profiles [START_REF] Cai | Personalized search by tag-based user profile and resource profile in collaborative tagging systems[END_REF], [START_REF] Vallet | Personalizing web search with folksonomybased user and document profiles[END_REF], [START_REF] Xu | Exploring folksonomy for personalized search[END_REF].

The second step aims at integrating the user's ISC into the SIR model by combining different weighting function. Authors in [START_REF] Cai | Personalized search by tag-based user profile and resource profile in collaborative tagging systems[END_REF], [START_REF] Vallet | Personalizing web search with folksonomybased user and document profiles[END_REF], [START_REF] Xu | Exploring folksonomy for personalized search[END_REF], [START_REF] Noll | Web search personalization via social bookmarking and tagging[END_REF], propose to personalize the user's search by ranking the resource based on, a matching between the user's interests and the documents' topics [START_REF] Xu | Exploring folksonomy for personalized search[END_REF], or between the user's profile and the resource's profile [START_REF] Vallet | Personalizing web search with folksonomybased user and document profiles[END_REF], [START_REF] Cai | Personalized search by tag-based user profile and resource profile in collaborative tagging systems[END_REF]. Rather than considering the resource's content, authors in [START_REF] Cai | Personalized search by tag-based user profile and resource profile in collaborative tagging systems[END_REF], [START_REF] Vallet | Personalizing web search with folksonomybased user and document profiles[END_REF], [START_REF] Noll | Web search personalization via social bookmarking and tagging[END_REF] propose to build a resource's profile through the resource's annotations and compute a matching function between this resource's profile and the query terms, on the one hand and between the resource's profile and the user's profile, on the other hand.

Unlike in [START_REF] Cai | Personalized search by tag-based user profile and resource profile in collaborative tagging systems[END_REF], [START_REF] Vallet | Personalizing web search with folksonomybased user and document profiles[END_REF] and [START_REF] Noll | Web search personalization via social bookmarking and tagging[END_REF], Xu et al., consider to match the document's content instead of matching only the document's profile over the query terms and the user's profile. The success they achieved is a strong support for our work [START_REF] Xu | Exploring folksonomy for personalized search[END_REF]. Although, Cai et al., [START_REF] Cai | Personalized search by tag-based user profile and resource profile in collaborative tagging systems[END_REF] discuss the limits of the weighting functions used in the previous cited works [START_REF] Vallet | Personalizing web search with folksonomybased user and document profiles[END_REF] [20], [START_REF] Noll | Web search personalization via social bookmarking and tagging[END_REF] as for a case study based only on a set of tags for resource recommendation, they propose a normalized term frequency to indicate the preference degree of a tag for the user and the representative degree of a tag for the resource [START_REF] Cai | Personalized search by tag-based user profile and resource profile in collaborative tagging systems[END_REF]. We note that the user's profile generated in these works is based only on the user's annotations without exploring his relationships.

In our approach, we assume that the document content is useful for IR. Thus, using only the document profile is not enough. We assume also that it is important to exploit the social annotations of the user's relationships (neighborhood) in addition to his own social annotations. As Stoyanovich et al. [START_REF] Stoyanovich | Leveraging tagging to model user interests in del.icio.us[END_REF] show, the predicted relevance of documents may be enhanced by exploiting the user neighbors' tagging actions [START_REF] Stoyanovich | Leveraging tagging to model user interests in del.icio.us[END_REF]. Finally, we think that combining the document content with this ISC requires IR techniques that are able to handle large textual documents. This led us to introduce an original approach presented in the following sections.

3 Personalized IR exploiting folksonomies

Ambiguous queries

Almost all test collections, in IR research, assume that queries have a single interpretation representing the information need expressed by one user, which is implicitly defined in his relevance judgments [START_REF] Sanderson | Ambiguous queries: test collections need more sense[END_REF]. However, in practice this is not necessary the case. For this reason, in this paper we propose a framework for personalized information retrieval based on folksonomies. Such a system should be able to handle ambiguous queries, i.e. queries having potentially several interpretations representing different information needs.

For example, suppose that two users u 1 and u 2 have the same query q = "smartphone android" (cf. Table 1). We consider two documents d 1 and d 2 ; each document contains one query term, but smartphone is more important than android in the first document since d 1 contains only smartphone, and android is more important than smartphone in the second one since d 2 contains only android. Assuming that the two query terms have the same importance, a classical IR system should estimate that d 1 is equally relevant as d 2 for the query "smartphone android ". However, depending on the user and his personal interests, the information need behind this query may focus either on the term smartphone or on the term android. The user u 1 is mainly interested in smartphone devices, then his information need is probably centered around smartphones with an opening on Android, and thus the query term smartphone should be more important than the query term android. On the other hand, the user u 2 is mainly interested by the Android operating system, consequently his information need is probably centered around Android, and thus the query term android should be more important than the query term smartphone. 1. Example: query, documents and user's profiles.

t1 = smartphone t2 = android t3 = features Query q 1 1 0 Document d1 1 0 0 Document d2 0 1 1 User u1 2 1 0 User u2 1 2 0 Table

Personalized information retrieval

A personalized information retrieval system should be able to identify the user's personal interests, in order to better interpret the information need behind his queries, and returns lists of relevant documents to the users depending on their personal interests. In our example, a personalized IR system should consider d 1 as more relevant than d 2 for u 1 , and the opposite for u 2 .

Folksonomies and user's informational social context (ISC)

We assume that folksonomies may be exploited in order to build the informational social context of the user that could represent the user's interests and that could help the system to handle ambiguous queries return personalized results to the user.

As pointed out in related literature, the user's profile can be inferred from his social annotations ( [START_REF] Au-Yeung | A study of user profile generation from folksonomies[END_REF], [START_REF] Szomszor | Semantic modelling of user interests based on cross-folksonomy analysis[END_REF]). The neighborhood's profile is defined by the annotations of his neighborhood. We assume that the user's profile can be enriched by the annotations of his neighbors to build the user's ISC.

Integrating user's ISC within the IR model

Since we exploit the social information about the user and his neighborhood to generate his ISC, we assume that the important terms representing the user's interests should appear in this ISC. Thus, reweighting such important terms when they are found within the document, should improve the document relevance score and allow to return the personalized relevant documents. We think that the integration of the user's ISC within the IR model is an important part of the personalization. One aim of this work is to handle textual documents containing thousands of terms, unlike most related work which only handle small sets of tags describing the document. Combining the user's ISC with this kind of textual data raises different issues than combining two sets of tags, like for instance in the work of Cai et al. [START_REF] Cai | Personalized search by tag-based user profile and resource profile in collaborative tagging systems[END_REF]). Our work attempts to deal with this issue.

Social information retrieval model

We present a SIR model, called BM 25F S , that takes into account the user's ISC, to better describe the documents with respect to the user viewpoint.

Notations

We represent the "social tagging data", also known as Folksonomies [START_REF] Vallet | Personalizing web search with folksonomybased user and document profiles[END_REF], by a tuple < U, Rel, T, D, A >, where:

-U = {u 1 , u 2 , ..., u x , ..., u |U | } is a set of social network users.
-Rel ⊆ U × U is a set of relationships between pairs of users, such that (u x , u y ) ∈ Rel iff there is a social relationship between a user u x and another user u y . The users related to u x are typically those declared explicitly by u x as his neighbors where neighborhood(u

x ) = {u y / (u x , u y ) ∈ Rel}. -T = {t 1 , t 2 , ..., t j , ..., t |T | } is a set of index terms. -D = {d 1 , d 2 , ..., d i , ..., d |D| } is a set of documents on the Web (images, videos,
Webpages, etc.). A document d i is represented by a set of terms (t j ∈ T ) and a term t j may appear one or several times in a document d i . We denote by tf ij the term frequency of t j in d i . A weight w ij of a term t j for a document d i is computed using this term frequency tf ij of t j in d i .

-A = {a 1 , a 2 , ..., a z , ..., a |A| } is a set of social annotations, i.e., a z = < d i , u x , T z > is the annotation of the user u x for the document d i using a subset of terms T z ⊂ T .

We define also:

-Q = {q 1 , q 2 , ..., q l , ..., q |Q| }, a set of users' queries, where each query q l is represented by a set of terms. -Qrels = {qrels 1 , qrels 2 , ..., qrels l , ..., qrels |Q| }, a set of global relevance judgments, where qrels l ⊂ D denotes the set of relevant documents for q l . -Q U C = {(q l , u x ) ⊂ Q × U }, a set of couples (q l , u x ) where the query q l is issued by the user u x to express his information needs. -Qrels U C = {qrels 1,1 , qrels 1,2 , ..., qrels l,x , ..., qrels |Q|,x }, a set of user-centered relevance judgments, where qrels l,x ⊂ D denotes the set of relevant documents for the query q l and the user u x .

Information retrieval model: BM25

We choose as baseline the IR weighting function BM25 [START_REF] Robertson | Some simple effective approximations to the 2-poisson model for probabilistic weighted retrieval[END_REF], which is one of the most used indexation models in the IR research benchmarks such as INEX2 , TREC3 , etc. In this IR weighting function, the weight of a term t j within a document d i is computed according to the formula (1):

w ij = (k 1 + 1) × tf ij k 1 × ((b -1) + b × ( dli avgdl )) + tf ij × log( N -df j + 0.5 df j + 0.5 ) (1)
where:

dl i is the document length of d i and avgdl is the average documents length.

tf ij is the term frequency of t j within the document d i .

k 1 is the saturation parameter of tf ij .

b is the length normalization factor.

-N is the total number of documents in the corpus.

df j is the number of documents containing the term t j .

In the BM 25 model, the global score of a document d i for a query q l is computed as follows:

BM 25(q l , d i ) = tj ∈q l ∩di w ij (2)

User informational social context (ISC)

The user's informational social context may contain different information types (annotations, comments, citations, social relationships, etc.). In this work we generate the user's ISC from the terms in his annotations and those of his neighbors. We present two variants of the user's ISC: ISC u (u x ), called "the user's profile" and ISC n (u x ), called "the neighborhood's profile": The user's profile ISC u (u x ) is the set of terms which occur within the social annotations of the user.

ISC u (u x ) = {t j ∈ T z / a z = < d i , u x , T z > ∈ A ux } (3)
where: A ux is the set of social annotations of u x . The user's profile may contain several occurrences of the same term. Thus we can compute the term frequency tf u xj for a given term t j that has been used by u x to annotate the documents. In the example provided in Table 1, the user profiles with the tf u xj associated are represented as follows: ISC u (u 1 ) = ISC u (u 2 ) = {"smartphone", "android"} and the tf u xj associated are (2,1) for u 1 and (1,2) for u 2 .

The neighborhood's profile ISC n (u x ) is the set of terms which occur within the user neighborhood's annotations.

ISC n (u x ) = ∪ uy∈U / (ux,uy)∈Rel ISC u (u y ) (4)
Like previously, the neighborhood's profile may also contain several occurrences of the same term t j , and we can compute the term frequency tf n xj for a given term t j that has been used by the neighborhood of u x to annotate the documents. In our example, the neighborhood(u 1 ) is composed of u 3 and the neighborhood(u 2 ) is composed of u 4 , with the user's profiles for u 3 and u 4 . Then, we can compute the neighborhood's profiles for u 1 and u 2 , given in Table 1.

Personalized index

Now, our aim is to take into account the user's ISC during the indexing step, in order to personalize the documents index. 2. Example: term frequencies of the user's ISC.

t1 = smartphone t2 = android t3 = features Query q 1 1 0 tf1j 1 0 0 tf2j 0 1 1 tf u1j 2 1 0 tf u2j 1 2 0 tf u3j 2 3 0 tf u4j 3 3 1 tf n1j 2 3 0 tf n1j 3 3 1 Table
We combine the content of the document d i , represented by a vector of term frequencies tf ij , with the user's ISC, composed of two vectors of term frequencies tf u xj and tf n xj . Each document is indexed by a vector of weights ws xij , with ws xij the weight of the term t j in the document d i for the user u x . The term weight ws xij is a personalized version of w ij for the user u x .

We propose to combine the document's content and the user's ISC as three different fields of information (i.e. 3 vectors). As it has been shown more coherent than combining the score of each vector computed independently, in [START_REF] Robertson | Simple BM25 extension to multiple weighted fields[END_REF]. Thus, we built a personalized documents index based on these three fields:

the content of d i , represented by a vector of field term frequencies f tf xij is equal to the classical tf ij :

f tf xij = tf ij (5)
the user's profile ISC u (u x ), represented by a vector of field term frequencies f tf u xij . Only the weights of the terms appearing both in the content of the document and in the user's profile should be considered:

f tf u xij = tf u xj if tf ij > 0 0 else. ( 6
)
where tf u xj is the term frequency of t j used by u x in his annotations. -the neighborhood's profile ISC n (u x ), represented by a vector of field term frequencies f tf n xij . Similarly, only the weights of the terms appearing both in the content of the document and in the neighborhood's profile should be considered:

f tf n xij = tf n xj if tf ij > 0 0 otherwise. ( 7
)
where tf n xj is the term frequency of t j used by the neighborhood of u x to annotate documents.

The BM25F weighting function has been proposed by [START_REF] Robertson | Simple BM25 extension to multiple weighted fields[END_REF] in order to index structured documents composed of several fields (e.g. title, abstract, body, etc.). BM 25F seems to be suitable for indexing our three fields-documents. This function was extended by [START_REF] Zaragoza | Microsoft cambridge at TREC 13: Web and hard tracks[END_REF] in order to optimize the length normalization fieldby-field. We chose to use this latter BM25F variant. Then, like Zaragoza et al., the first step of the BM25F function normalizes the term frequencies of each field by the field length [START_REF] Zaragoza | Microsoft cambridge at TREC 13: Web and hard tracks[END_REF]:

f tf xij = f tf xij 1 + b d × ( dl avgdl -1) (8)
f tf u xij = f tf u xij 1 + b ux × ( ul avgul -1) (9)
f tf n xij = f tf n xij 1 + b nx × ( nl avgnl -1) (10)
where:

b d , b ux and b nx are some field-dependent parameters, similar to b (in BM25), for the d i content field, the user's profile field and the neighborhood's profile field, respectively, -ul and nl are the length of the user's profile field and the length of the neighborhood's profile field, respectively, -avgul and avgnl are the average length of the user's profile field over the collection and the average length of the neighborhood's profile field over the collection.

Then, following Zaragoza's BM25F [START_REF] Zaragoza | Microsoft cambridge at TREC 13: Web and hard tracks[END_REF], we compute the term weight ws xij for t j within the document d i for the user u x using the weighting function in formula 11:

ws xij = ctf xij k 1 + ctf xij × log N -df j + 0.5 df j + 0.5 (11)
where:

ctf xij is the combined term frequency of the three fields:

ctf xij = w d .f tf xij + w ux .f tf u xij + w nx .f tf n xij (12)
w d , w ux and w nx are three field-dependent parameters used to tune the importance of the user's profile field and the neighborhood's profile field in relation to the importance of the document content field.

Finally, the personalized relevance score of a document d i for the query q l and the user u x is given by equation 13:

BM 25F S (q l , d i , u x ) = tj ∈q l ∩di ws xij (13)
Table 3 shows the field term frequencies for the example given in Table 1 and the relevance scores obtained using BM 25 and BM 25F S . These scores have 3. Impact of the user's ISC on the indexing process. The document relevance score increases when the frequencies of the user's profile terms are combined to those of the document terms. Furthermore, for two different user's ISCs with the same query terms, the ranking of the documents could vary according to the ISC of each user. For instance, when the user's ISC is considered, the document d 1 is more relevant than d 2 for u 1 (1.983 vs 1.898), whereas d 2 is more relevant than d 1 for u 2 .

U D f tf T BM 25(q l , di) BM 25FS(q l , di, ux) t1 t2 t3 f tf u11j 2 0 0 d1 f tf n11j 3 0 0 1.681 1.983 u1 f tf11j 1 0 0 f tf u12j 0 1 0 d2 f tf n12j 0 3 0 1.681 1.898 f tf12j 0 1 1 f tf u21j 1 0 0 d1 f tf n21j 2 0 0 1.681 1.898 u2 f tf21j 1 0 0 f tf u22j 0 2 0 d2 f tf n22j 0 3 1 1.681 1.983 f tf22j 0 1 1 Table

Experiments

Experiments have been carried out to evaluate the SIR model that considers the user's ISC compared to the classical IR model. Using the social test collection (Del SIR ) described bellow, and the evaluation measures MAP (Mean Average Precision) and P[0.1] (the precision at 10% of recall) [START_REF] Manning | Introduction to Information Retrieval[END_REF], we evaluated the rankings produced:

by a classical IR model with two kinds of data: the first one is composed of 79 global queries (Q) and their global relevance judgments (Qrels), the second one is composed of 244 user-centered queries (Q U C ) and corresponding usercentered relevance judgments (Qrels U C ).

by our SIR model (BM 25F S ) with only user-centered data (Q U C and Qrels U C ), since the SIR model is not suited to handle global queries.

Social test collection

To the best of our knowledge, no SIR test collection exists providing a list of relevant documents for each user. So, we built a test collection Del SIR based on Web documents and user annotations extracted from the social collaborative bookmarking network Delicious. We collected 30,224 documents annotated by 370 users with 21,284 terms. To complete the social dataset with the user-centered data composed of pairs (query, user) and user-centered relevance judgments, we created automatically 79 queries. Each query is composed by 2 terms occuring frequently together4 in the annotations collected from Delicious. Then we generated 4,685 global relevance judgments and user-centered relevance judgments.

A document is globally relevant if it has been annotated by any user with the 2 query terms in the same annotation. A document is user-relevant if it has been annotated by the user with the 2 query terms in the same annotation.

We kept only the pairs (query, user) with at least 10 relevant documents (|qrels l,x | ≥ 10) so that we obtained 244 pairs (q l , u x ) in the set Q U C . This led to a reduction of the number of users (70 users left).

Evaluation results with the classical IR model BM 25

For each given query, the classical IR model returns the same relevant documents whoever the user is. The results quality obtained with the BM 25 decreases for both the M AP (0.0308 vs 0.1012) and the P [0.1] (0.0521 vs 0.1775) when the user-centered relevance judgments Qrels U C are considered. This confirms our expectations that the classical IR system has to adapt its models in order to deal with the user-centered data (Q U C and Qrels U C ).

Evaluation results with the SIR model BM 25F S

In our experiments we have tuned the same way as in [START_REF] Zaragoza | Microsoft cambridge at TREC 13: Web and hard tracks[END_REF], using the grid-based 2D optimization, the b and k parameters for BM25 and for each field of BM 25F S . Table 4 shows results obtained with two fields-weight settings:

-BM 25F S , settings 1 : w d = 1, w ux ∈]0..1] and w nx = 0 -BM 25F S , settings 2 : w d = 1, w ux ∈]0..1] and w nx ∈]0..1]

We selected the users having at least 5 queries and obtaining with classical IR a MAP result between 0.5% and 50%. We obtain a set of 10 users, corresponding to a set of 60 pairs (query, user) The results of our SIR model BM 25F S compared to the baseline BM 25 are shown in Table 4.

Considering the precision at 10% of recall (P[0.1]), the SIR model provides less good results than the baseline. While, using the Mean Average Precision measures (MAP), the SIR model BM 25F S results (MAP = 0.0297 and MAP = 0.0293) are statistically better than the baseline (MAP = 0.0257). The significance has been checked by using statistical tests based on Wilcoxon matchedpairs signed-rank test at the 0.05 level, i.e. the improvement is significant when the p-value is less than 0.05. These results, obtained on a set of 60 pairs (q l ,u x ) for the 10 users in Table 4, confirm that a SIR model which takes into account the user's ISC, with or without neighborhood, enhance the relevance score results using the MAP measure which is considered as a global evaluation metric.

Conclusion

We presented an approach that integrates the user's ISC into the documents. The user's ISC has been built using the user's annotations and those of his relationships. The aim is to personalize the user's search by considering his preferences and interests. Our approach allows highlight and reweight the important terms of the user's ISC when they are found in the document content. As we consider textual documents containing thousands of terms, we proposed to combine the user's ISC with textual content, in the weighting function. The SIR model, that considers the user's ISC, allows to better find the relevant documents for the user query than the classical IR model which does not consider the user's ISC. As future works, we plan to extend the user's ISC with further social data including the neighborhood of neighborhood's (friends of friends) annotations and build a bigger social test collection from Delicious' bookmarks. We would also like to study different parameters with further experiments to evaluate our SIR model.

  been computed using the formula 11, with usual BM25 parameters values: b d = b ux = b nx = 0.75 and k = 1.2.

Table 4 .

 4 BM 25FS evaluation results.

		BM 25			BM 25FS
				settings1		settings2
		M AP	P [0.1]	M AP	P [0.1]	M AP	P [0.1]
	u1	0.0614	0.1310	0.0816	0.1426	0.0819 0.1503
	u2	0.0404	0.2614 0.0402	0.1265	0.0416 0.1203
	u3	0.0358	0.1076	0.0486 0.1438	0.0483	0.1438
	u4	0.0262	0.0922	0.0278 0.0956 0.0275	0.0948
	u5	0.0287 0.0569	0.0253	0.0484	0.0284	0.0688
	u6	0.0174	0.0529	0.0199 0.0568 0.0197	0.0550
	u7	0.0183 0.0207	0.0173	0.0298	0.0148	0.0391
	u8	0.0138	0.0296	0.0148	0.0316	0.0156 0.0319
	u9	0.0074	0.0221 0.0093 0.0215	0.0091	0.0188
	u10	0.0077	0.0095	0.0085	0.0115 0.0103 0.0101
	Average 0.0257	0.0784 0.0293	0.0708	0.0297 0.0733

https://delicious.com/

INEX (INitiative for the Evaluation of XML-Retrieval): https://inex.mmci.unisaarland.de/

3 TREC (TExt Retrieval Conference): http://trec.nist.gov/

In fact, the 79 couples of terms having the highest Jaccard Index


K Musumbu
email: musumbu@labri.fr

Semantics Web and Ontology Learning

Keywords: Business rules, knowledge based systems, model driven architecture, reasoning, semantics web, ontology

HAL is

Introduction

Certain commonly used terms had multiple meanings all equally valid but which we had not differentiated adequately, leading to much confusion. To avoid this cufusion one must define an ontology, which included semiformal definitions of all key terms and their relationships. The term ontology means a specification of a conceptualization. It is used to describe the highlevel structure of the software in the most direct and most expressive manner possible. The Semantic Web endows the network of disparate information that is currently accessible on the Internet with meaning. Because this meaning can be gleaned and processed automatically by software, the Semantic Web opens up the exciting and awe-inducing possibility of a unified global intelligence accessible to all. The e-learning is the use of the new multimedia technologies of the Internet to improve the quality of the learning by facilitating on one hand the access to resources and to services, on the other hand the exchanges and the collaboration in distance. The main goal of this paper is to present comprehensive introduction into MDA-based ontology development. It will provide an introduction to the field of the Semantics Web and ontology engineering, a description of several UML-and metamodelingbased solutions and tools for ontology development. The objectif is to show how the ontologies can be useful in the domain of e-learning. This paper will be divided in three parts. The first covers the basis of both main topics ontology, semantic web and standards. The second part, explain the Model Driven Architecture. The last is the central one, it starts with a review of several approaches and aim to bridge the gap between ontology development and software engineering methodologies.

Semantic Web

The Semantic Web has the potential for semantically richer representations of things (e.g., Web pages, applications, and persons) and their relations on the Web, and thus should provide us with more intelligent services. In MDA, an instance of MOF [START_REF]The Object Management Group OMG. Meta Objet Facility (MOF) Specification Version 1.4. OMG Specification[END_REF] is use for representing models but our works are only concerned by UML models. For adding semantics in UML models we can use:

• UML profile: UML can be used for modeling many domains. The problem with this is that UML models are so generic that it is impossible to know either it is object application, a metamodel, a model, a database structures or anything else only by looking at it [START_REF] Blanc | MDA en action[END_REF]. For adding precision, the OMG has standardized the concept of UML profile [START_REF]The Object Management Group[END_REF]. A UML profile is a set of techniques and mechanisms allowing to adapt UML in a particular and specific domain. UML profile can be used in any UML model and do not modify the structure of the meta-model. UML profiles are stereotypes or labels which can be injected into models. After having stuck labels on models, we can make inference using it. As we can see, doing this can solve our problem of semantics lack on model in a low level, but this is not exploitable by machines because there is no notion of logic and taxonomy and semantic is not formally defined.

• Object Constraint Language: In UML it was not possible to define the body of an operation (or a method) so the OCL [START_REF]The Object Management Group OMG. UML 2.0 OCL Specification. OMG Specification[END_REF] was standardized by OMG for doing it. OCL allows expressing any kind of constraints on UML models. For example, we can express constraints like: "before renting a car you must be sure that it is OK". Well, OCL seems to be a good solution for our problem but it is not the case. The first problem with OCL is that he does not support side effect operations and the second is that he does not offer automatic inference for machines.

• Action Semantic: remember that the main constraint with OCL was that he only supports no side effects operations. To solve this constraint, the OMG standardize Action Semantic [START_REF]The Action Semantics Consortium[END_REF].

Well, now we have a formalism being able to express any king of operations and constraints but it is not enough. This formalism is complicated to use [START_REF] Blanc | MDA en action[END_REF], was not created while thinking to machine comprehension and self-use, and do not have a textual formalism.

As we can see, none of the UML "techniques" is suitable for adding semantics in models. In an other side a new domain of computer is growing more and more: semantic web. The aim of the semantic web is to make the web both comprehensible by humans and machines [START_REF] Thomas | Explorer's guide to the Semantic Web[END_REF]. A part Figure 2: ODM principe of semantic web is about ontology and reasoning. Modeling concept defined by ontologies can be used to model the concepts in a domain, the relationships between them, and the properties that can be used to describe instances of those concepts [START_REF] Cranefield | Bridging the Gap Between the Model-Driven Architecture and Ontology Engineering[END_REF]. In addition, the Web Ontology Language (OWL) [START_REF] Mellor | Guest Editors' Introduction: Model-Driven Development[END_REF] supports the inclusion of certain types of constraint in ontology, allowing new information to be deduced when combining instance data with these logic's description [START_REF] Cranefield | Bridging the Gap Between the Model-Driven Architecture and Ontology Engineering[END_REF]. At this point our dilemma was how can we use MDA models and Semantic Web? Ontology Definition Meta Model was the response to our need.

Moreover, careful selection of a knowledge representation may simplify problem solving, whereas an unfortunate selection may lead to difficulties or even failure to find a solution. Complex problems require a combination of several different representations. In order to be practical, every knowledge representation technique needs a notation. A formal notation often used for representing knowledge is first-order predicate calculus. Of course, it is not suitable for all types of knowledge. The notation of description logics has also proven expressive enough to represent knowledge formally. More recently, the trend has been to use XML syntax for representing knowledge structures.

Ontologies and their application

There is an increasing awareness among researchers and developers that ontologies are not just for knowledge-based systems, but for all software systems. The major application fields for ontologies nowadays include knowledge management, e-learning, ecommerce, and integration of Web resources, intranet documents, and databases. Knowledge sharing and reuse through automatic exchange of Web documents among applications and agents is possible only if the documents contain ontologically encoded information. One view of ontology representation languages is that they can be used as metadata languages ontologies engineered with such languages can provide vocabularies for metadata to facilitate the management, discovery, and retrieval of resources on the Web. A metamodel is an explicit model of the constructs and rules needed to build specific models within a domain of interest. This characterizes a valid metamodel as an ontology, since such constructs and rules represent entities in a domain and their relationships i.e., a metamodel is an ontology used by modelers.

The MDA and its four-layer architecture provide a solid basis for defining the metamodels of any modeling language, and thus a language for modeling ontologies based on the MOF. Such a language can employ MDA tool support for modeling, model management, and interoperability with other MOF-based metamodels. The current software tools do not implement many of the fundamental MDA concepts. However, we can expect that most of these tools, which are presently oriented towards UML and the modeling layer (M1), will be improved and equipped with MDA support in the coming years.

Ontologies Learning

Ontology learning (ontology extraction, ontology generation, or ontology acquisition) is the automatic or semi-automatic creation of ontologies, including extracting the corresponding domain's terms and the relationships between those concepts from a corpus of natural language text, and encoding them with an ontology language for easy retrieval. As building ontologies manually is extremely labor-intensive and time consuming, there is great motivation to automate the process. Typically, the process starts by extracting terms and concepts or noun phrases from plain text using linguistic processors such as part-of-speech tagging and phrase chunking. Then statistical or symbolic techniques are used to extract relation signatures. ontologies have to be structured optimally against the knowledge base they represent. The structure of an ontology needs to be changed continuously so that it is an accurate representation of the underlying domain. Recently, an automated method was introduced for engineering ontologies in life sciences such as Gene Ontology (GO), [START_REF] Ruleml | The RuleML initiative[END_REF] one of the most successful and widely used biomedical ontology. [START_REF]The Object Management Group OMG[END_REF] Based on information theory, it restructures ontologies so that the levels represent the desired specificity of the concepts. Similar information theoretic approaches have also been used for optimal partition of Gene Ontology. [START_REF] Blanc | MDA en action[END_REF] Given the mathematical nature of such engineering algorithms, these optimizations can be automated to produce a principled and scalable architecture to restructure ontologies such as GO.

Conclusion

Ontology engineering aims to make explicit the knowledge contained within software applications, and within enterprises and business procedures for a particular domain. Ontology engineering offers a direction towards solving the interoperability problems brought about by semantic obstacles, such as the obstacles related to the definitions of business terms and software classes. However, realworld applications and their development are rarely clearly bordered in their scope; thats why these environments are not enough. It is, therefore, necessary to integrate applications that are used for intelligent systems development into mainstream software platforms. This topic is going to gain more and more attention with the development of the Semantic Web [Berners-Lee et al., 2001] and with increased integration of intelligent techniques in common information systems.

Figure 1 :

 1 Figure 1: Global view of the Model Driven Architecture approach

Figure 3 :

 3 Figure 3: Our approach throughout the MDA layers


Gilles Sérasset
email: gilles.serasset@imag.fr

Multilingual Legal Terminology on the Jibiki Platform: The LexALP Project

This paper presents the particular use of "Jibiki" (Papillon's web server développement platform) for the Lexalp 1 project. LexALP's goal is to harmonize the terminology of the Alpine Convention's 2 four languages (French, German, Italian and Slovenian) so that member states are able to cooperate effectively. For this, the project uses the Papillon platform in order to build a term bank used to compare the specialized terminology of six different national legal systems in four different language, and to harmonize it, optimizing the understanding between various alpine states in environmental matters at a supranational level. In this paper we present how a generic platform like the Papillon one is to be used in order to cope with a new kind of dictionary.

2 Jibiki, The Papillon Dictionary Development Platform

2.

Introduction

One of the most blocking problem in supranational law or convention negotiation comes from the lack of understanding among negociators and writters. This lack of understanding is not only due to the fact that different languages are involved, but also, and mainly, to the fact that different legal systems are involved. Countries using the same language (like France and part of Switzerland) may use the very same word to represent very different legal concepts defined in their respective legal texts. Terms, that are usually considered as translations of each others may also represent very different legal notions.

In order to concretely address these problems, several institutions representing translators, lexicographs, legal experts and computational linguists joined in the LexALP project, funded by EU's INTERREG IIIb Alpine Space program. The objective of the project is to compare the specialised terminology of six different national legal systems in four different languages, and to harmonise it, optimising the understanding between the various Alpine states in environmental matters at the supranational level.

The tools that are to be developed for these objectives comprise a corpus bank and a term bank. The corpus bank is developed by reusing the bistro system [START_REF] Streiter | BISTRO: Data structure, term tools and interface[END_REF][START_REF] Streiter | BISTRO, the online platform for terminology management: structuring terminology without entry structures[END_REF]. The term bank is based on the Jibiki platform [START_REF] Mathieu Mangeot-Lerebours | Construction collaborative d'une base lexicale multilingue, le projet Papillon[END_REF][START_REF] Sérasset | A generic collaborative platform for multilingual lexical database development[END_REF].

This paper details the way the Jibiki platform is used in order to cope with a new dictionary structure. The platform provides dictionary access and edition services without any new and specific development.

After a brief overview of the Jibiki platform, we describe the choices made by the LexALP team for the structure and organization of their term bank. Then, we show how this structure is described using Jibiki metadata description languages. Finally, we give some details on the resulting Lexalp Information System. This platform offers several services to its users:

• access to many different dictionaries from a single easy to use query form,

• advance search for particular dictionary entries through an advanced search form,

• creation and edition of dictionary entries. What makes the Jibiki platform quite unique is the fact that it provides these services regardless of the dictionary structure. In other words it may be used by any dictionary builder to give access and collaboratively edit any dictionary, provided that the resulting dictionary will be freely accessible online.

Jibiki platform Architecture

The Jibiki platform is a framework used to set up a web server dedicated to the collaborative development of multilingual dictionaries. All services provided by the platform are organised as classical 3-tiers architectures with a presentation layer (in charge of the interface with users), a business layer (which provides the services per se) and a data layer (in charge of the storage of persistent data). In order to adapt the Jibiki platform to a new dictionary, the dictionary manager does not have to write specific java code nor specific dynamic web pages. The only necessary information used by the Jibiki plateform consists in:

• a description of the dictionary volumes and their relations,

• a mapping between the envisaged dictionary structure and a simple hypothetical dictionary structure (called CDM)4 ,

• the definition of the XML structure of each envisaged dictionary volumes by way of XML schemas,

• the development of a specific edition interface as a standard xhtml form (that can be adapted from an automatically generated draft)

3 The LexALP Terminology Structure

Overview

The objective of the LexALP project is to compare the specialised terminology of six different national legal systems in four different languages, and to harmonise it, optimising the understanding between the various Alpine states in environmental matters at the supranational level. To achieve this objective, partners are collecting terms used in the Alpine convention (a trans-national convention on spatial planning and durable development gathering countries from the alpine region) and their counterparts in national laws. The collected terms are structured in a specific term bank that will support the future harmonisation work.

As the project deals with law terms, each term refers to a concept that is proper to the considered national law or international convention, we have to deal with contrastive problems as concepts are not "stable" between the different national laws. Here, standard terminology techniques are not applicable, as the "normalisaion" approach [START_REF] Felber | Manuel de terminologie[END_REF][START_REF] Felber | Terminology research: Its relation to the theory of science[END_REF] is not applicable. For this, we chose to use "acceptions" as they are defined in the Papillon dictionary (Sérasset, 1994a;Sérasset, 1994b) to represent the translation links between concepts of the different legal systems (thus adopting a method closer to [START_REF] Arntz | Terminological equivalence and translation[END_REF]).

The example given in figure 2 shows a concept defined in the Alpine Convention. This concept is realised by terms in the languages of the convention. The Alpine Convention also uses the terms "circulation intra-alpine" or "transport intra-alpin" which are identified as synonyms by the lexicographer.

This illustrates the first goal of the LexALP project. In different texts, the same concept may be realised by different terms in the same language. This is considered as a mistake. Hence, a single term has to be chosen as an In this example, the lexicographers and jurists did not identified any existing concept in the different national laws that could be considered as close to the presented concept. This is coherent with the minutes from the French National Assembly which clearly states the the term "trafic intra-alpin" (among others) should be clarified by a declaration to be added to the Alpine Convention. This illustrates the second goal of the project, which is to help jurists with the fine comprehension of the Alpine Convention and with the detailed knowledge necessary to evaluate the implementability of the convention in their legal system.

As a by-product of the project, one can see that there is a indirect relation between concepts from different national legal systems (by way of their respective relation to the concepts of the Alpine Convention. However, establishing these indirect relations is not one of the main objectives of the LexALP project.

Macro-and Micro-Structures

The LexALP term bank consists in 5 volumes (for English, French, German, Italian and Slovene) containing the term descriptions. The translation links are established through a central acception volume. Figure 2 and3 show examples of terms extracted from the Alpine Convention, synonymy links in French and Italian volumes, as well as inter-lingual relations by way of acceptions.

All language volumes share the same microstructure. This structure is stored in XML.

<entry id="fra.trafic_intra-alpin.1010743.e" lang="fra" legalSystem="AC" process_status="FINALISED" status="HARMONISED"> <term>trafic intra-alpin</term> <grammar>n.m.</grammar> <domain>Transport</domain> <usage frequency="common" geographical-code="INT" technical="false"/> <relatedTerm isHarmonised="false" relationToTerm="Synonym" termref=""> transport intra-alpin </relatedTerm> <relatedTerm isHarmonised="false" relationToTerm="Synonym" termref=""> circulation intra-alpine </relatedTerm> <definition> Figure 4 shows the xml structure of the French term "trafic intra-alpin", as defined in the Alpine Convention. The term entry is associated to a unique identifier used to establish relations between volume entries. Each term entry belongs to one (and only one) legal system. The example term belongs to the Alpine Convention legal system5 (code AC). The set of known legal systems includes of course countries belonging to the alpine space (Austria, France, Germany, Italy, Liechtenstein, Slovenia, Switzerland) but also international treaties or conventions. The entry also bears the information on its status (harmonised or rejected) and its process status (to be processed, provisionally processed or finalised).

The term itself and its part of speech is also given, with the general domain to which the term belongs, along with some usage notes. In these usage notes, the attribute geographical-code allows for discrimination between terms defined in national (or federal) laws and terms defined in regional laws as some of the countries involved allows several level of legislative authorities.

Then the term may be related to other terms. These relations may lead to simple strings of texts (as in the given example) or to autonomous term entries in the dictionary by the use of the termref attribute. The relation itself is specified in the relationToTerm attribute. The current schema allows for the representation of relations between concepts (synonymy, hyponymy and hyperonymy), as well as relations between graphies (variant, abbreviation, acronym, etc.).

Then, a definition (along with its source) and a context may be given. The definition and context should be extracted from a legal text that is identified in the source field.

An interlingual acception (or axie) is a place holder for relations. Each interlingual acception may be linked to several term entries in the languages volumes through termref elements and to other interlingual acceptions through axieref elements, as illustrated in figure 5.

LexALP Information System

Overview

Building such a term bank can only be envisaged as a collaborative work involving lexicographers, translators and jurists from all the involved countries. Hence, the LexALP consortium has set up a centralised information system that is used to gather all textual and lexicographical data.

This information system is organized in 2 main parts. The first one is dedicated to corpus management. It allows the users to upload legal texts that will serve to bootstrap the lexicographical work (by way of candidate term extraction) and to let lexicographers find occurrences of the term they are working on, in order for them to provide definition or contexts.

The second part is dedicated to lexicographical work per se. It has been developed with the Jibiki platform described in section 2. In this section, we show the Lexalp Information System functionality, along with the metadata required to implement it with Jibiki.

Dictionary Browsing

The first main service consists in browsing the currently developed dictionary. It consists in 2 different query interfaces (see Figures 6 and7) and an unique result presentation interface (see Figure 10).

In the provided examples, the user of the system specifies an entry (or part of an entry) and a language in which the search is to be done. The expected behaviour may only be • the system knows in which volume the search is to be performed,

• the system knows where, in the volume entry, the headword is to be found,

• the system is able to produce a presentation for the retrieved XML structures.

However, as the Jibiki platform is entirely independent of the underlying dictionary structure (which makes it highly adaptable), the expected result may only be achieved if additional metadata is added to the system.

These pieces of information are to be found in the mandatory dictionary descriptor. It consists in a structure defined in the Dictionary Metadata Language (DML), as set of metadata structures and a specific XML namespace defined in [START_REF] Mangeot-Lerebours | Environnements centralisés et distribués pour lexicographes et lexicologues en contexte multilingue[END_REF].

Figure 8 gives an excerpt of this descriptor. The metadata first identify the dictionary by giving it a name and a type. In this example the dictionary is a pivot dictionary (DML Figure 10: Query result presentation interface also defines monolingual and bilingual dictionary types). The descriptor also defines the set of source and target languages. Finally, the dictionary is defined as a set of volumes, each volume being described in another file. As the Lexalp dictionary is a pivot dictionary, there should be a volume for the artificial language axi, which is the pivot volume.

Figure 9 shows an excerpt of the description of the French volume of the Lexalp dictionary. After specifying the name of the dictionary, the descriptor provides a set of cdmelements. These elements are used to identify standard dictionary elements (that can be found in several dictionaries) in the specific dictionary structure. for instance, the descriptor tells the system that the headword of the dictionary (cdm-headword) is to be found by applying the specified xpath6 to the dictionary structure.

With this set of metadata, the system knows that:

• requests on French should be directed to the LexALP fra volume,

• the requested headword will be found in the text of the term element of the volume entry element, Hence, the system can easily perform a request and retrieve the desired XML entries. The only remaining step is to produce a presentation for the user, based on the retrieved entries. This is achieved by way of a xsl7 stylesheet. This stylesheet is specified either on the dictionary level (for common presentations) or on the volume level (for volume specific presentation).

In the given example, the dictionary administrator provided two presentations called LexALP (the default one) and short-list, both of them defined in the dictionary descriptor.

This mechanism allows for the definition of presentation outputs in xhtml (for online browsing) or for presentation output in pdf (for dictionary export and print).

Dictionary Edition

The second main service provided by the Jibiki platform is to allow lexicographers to collaboratively develop the envisaged dictionary. In this sense, Jibiki is quite unique as it federates, on the very same platform the construction and diffusion of a dictionary.

As before, Jibiki may be used to edit any dictionary. Hence, it needs some metadata in- • the complete definition of the dictionary entry structures by way of a XML schema,

• a template describing an empty entry structure,

• a xhtml form used to edit a dictionary entry structure (which can be adapted from an automatically generated one).

When this information is known, the Jibiki platform provides a specific web page to edit a dictionary entry structure. As shown in figure 12, the XML structure is projected into the given empty XHTML form. This form is served as a standard web page on the client browser. After manual edition, the resulting form is sent back to the Jibiki platform as CGI 8 data. The Jibiki plateform decodes this data and modifies the edited XML structure accordingly. Then the process iterates as long as necessary. Figure 11 shows an example of such a dynamically created web page. After each update, the resulting XML structure is stored in the dictionary database. However, it is not available to other users until it is marked as finished by the contributor (by 8 Common Gateway Interface clicking on the save button). If the contributor leaves the web page without saving the entry, he will be able to retrieve it and finish his contribution later.

At each step of the contribution (after each update) and at each step of the dictionary edition (after each save), the previous state is saved and the contributor (or the dictionary administrator) is able browse the history of changes and to revert the entry to a previous version.

Conclusion

In this article we give some details on the way the Jibiki platform allows the diffusion and the online edition of a dictionary, regardless of his structure (monolingual, bilingual (directed or not) or multilingual (multi-bilingual or pivot based)).

Initially developed to support the edition of the Papillon multilingual dictionary9 , the Jibiki platform proved useful for the development of other very different dictionaries. It is currently used for the development of the GDEF (Grand Dictionnaire Estonien-Français) project10 an Estonian French bilingual dictionary. This article also shows the use of the platform for the development of an European term bank for legal terms on spatial planning and development in the Lexalp project.

Adapting the Jibiki platform to a new dictionary requires the definition of several metadata information, taking the form of several XML files. While not trivial, this metadata definition does not require any competence in computer development. Hence this adaptation may be done by experimented lexicographers. Moreover, when the dictionary microstructure needs to evolve, this evolution does not require any programming. Hence the Jibiki platform gives lexicographers great liberty in their decisions.

Another positive aspect of Jibiki is that it integrates diffusion and edition services on the same platform. This allows for a tighter collaboration between lexicographers and users and also allows for the involvement of motivated users to the edition process.

The Jibiki platform is freey available for use by any willing lexicographer team, provided that the resulting dictionary data will be freely available for online browsing.

In this article, we also presented the choices made by the Lexalp consortium to structure a term bank used for the description and harmonisation of legal terms in the domain of spacial planning and development of the alpine space. In such a domain, classical techniques used in multilingual terminology cannot be used as the term cannot be defined by reference to a stable/shared semantic level (each country having his own set of non-equivalent legal concepts).

Figure 1 :

 1 Figure 1: The Jibiki platform general architecture

Figure 2 :

 2 Figure 2: A quadri-lingual term extracted from the Alpine Convention

Figure 3 :

 3 Figure 3: A quadri-lingual term extracted from the Alpine Convention with reference to a French translationFigure3shows an analogous quadri-lingual example where the Alpine Convention concept may be related to a legal term defined in the French laws. In this example, the French term is distinguished from the Alpine Convention terms, because their concepts are not identically defined in their respective legal systems. Hence, the lexicographer created distinct acceptions, one for each concept. These acceptions are related by a translation link.This illustrates the second goal of the project, which is to help jurists with the fine

  Figure 4: XML form of the term "trafic intraalpin".

Figure 5 :

 5 Figure 5: XML form of the interlingual acception illustated in Figure 2.

Figure 7 :

 7 Figure 7: Advanced search interface

Figure 8 :

 8 Figure 8: Excerpt of the dictionary descriptor

Figure 11 :

 11 Figure 11: Edition interface of a Lexalp French entry

Figure 12 :

 12 Figure 12: Basic flow chart of the edition service

LexALP: Legal Language Harmonisation System for Environment and Spatial Planning within the Multilingual Alps

2 http://www.convenzionedellealpi.org

This mapping is sufficient for simple dictionary access

Strictly speaking, the Alpine Convention does not constitute a legal system per se.

an xpath is a standard way to extract a sub-part of any XML structure

XSL is a standard way to transform an XML structure into another structure (XML or not).

http://www.papillon-dictionary.org/

http://estfra.ee/