Andreas Stang
email: andreas.stang@medizin.uni-halle.de

Charles Poole

Oliver Kuss

The ongoing tyranny of statistical significance testing in biomedical research

Keywords: Statistics, P-value, Confidence intervals

Since its introduction into the biomedical literature, statistical significance testing (abbreviated as SST) caused much debate. The aim of this perspective article is to review frequent fallacies and misuses of SST in the biomedical field and to review a potential way out of the fallacies and misuses associated with SSTs. Two frequentist schools of statistical inference merged to form SST as it is practised nowadays: the Fisher and the Neyman-Pearson school. The P-value is both reported quantitatively and checked against the a-level to produce a qualitative dichotomous measure (significant/nonsignificant). However, a P-value mixes the estimated effect size with its estimated precision. Obviously, it is not possible to measure these two things with one single number. For the valid interpretation of SSTs, a variety of presumptions and requirements have to be met. We point here to four of them: study size, correct statistical model, correct causal model, and absence of bias and confounding. It has been stated that the P-value is perhaps the most misunderstood statistical concept in clinical research. As in the social sciences, the tyranny of SST is still highly prevalent in the biomedical literature even after decades of warnings against SST. The ubiquitous misuse and tyranny of SST threatens scientific discoveries and may even impede scientific progress. In the worst case, misuse of significance testing may even harm patients who eventually are incorrectly treated because of improper handling of Pvalues. For a proper interpretation of study results, both estimated effect size and estimated precision are necessary ingredients.

Since its introduction into the biomedical literature, null hypothesis significance testing (abbreviated as SST) has caused much debate. As early as 1919, Boring criticized use of SST [START_REF] Boring | Mathematical vs. scientific significance[END_REF]. In 1957, Hogben described the logical and practical errors in theory and teaching of SST [START_REF] Hogben | Statistical theory: an examination of the contemporary crisis in statistical theory from a behaviourist viewpoint[END_REF]. In 1970, Morrison and Henkel [START_REF] Morrison | The significance test controversy: a reader[END_REF] published the compendium entitled ''The Significance Test Controversy''. More recently, Cohen wrote a very influential paper on the SST controversy [START_REF] Cohen | The earth is round (p \ .05)[END_REF]. Despite many warnings and critical remarks about the use of SST in biomedical projects, SST is still one of the most prevalent statistical procedures in biomedical publications. The aim of this perspective article is to review frequent misconceptions and misuses of SST in the biomedical field and to review a potential way out of the fallacies associated with SSTs.

What is a P-value?

A P-value may be viewed as the probability of obtaining an estimate at least as far from a specified value (most often the null value, i.e., the value of no effect, the so called ''nil hypothesis'') as the estimate we have obtained, if the specified (null or test) value were (note the subjunctive) the true value [START_REF] Greenland | Fundamentals of epidemiologic data analysis[END_REF], p. 220. In other words, the P-value is a tail area probability based on the observed effect estimate; it is calculated as the probability of an effect estimate as large as or larger than the observed estimate (more extreme in the tails of the distribution), assuming the null hypothesis were true [START_REF] Blume | What your statistician never told you about P-values[END_REF]. As Miettinen stated ''The P-value is a function of the data computed under the statistical model that underlies the analysis.'' [START_REF] Miettinen | Theoretical epidemiology[END_REF]. It should be noted that a P-value is a confounded (mixed) measure: it mixes the estimated effect size with its estimated precision, both crucial aspects of the data [START_REF] Lang | That confounded P-value[END_REF]. Obviously, it is not possible to present two quantities (estimated effect size and precision) by one single number. We can see this most clearly when the test statistic is Z: the parameter estimate divided by its estimated standard error. The higher the parameter estimate or the lower the estimated standard error, the greater the (absolute) value of Z and the lower the (twosided) P-value.

Perhaps more important than knowing what a P-value actually is, is understanding what a P-value is not. Goodman gave as many as 12 different misconceptions of the P-value [START_REF] Goodman | A dirty dozen: twelve p-value misconceptions[END_REF]. The most pervasive of these misconceptions is interpreting the P-value as the probability of the null hypothesis. The P-value is derived from the study data, assuming that the null hypothesis is true. It cannot, therefore, make a concurrent statement of the probability of that hypothesis. In addition, the P-value generally exaggerates the evidence against the null hypothesis, thus calling ''into question the validity of much published work based on comparatively small, including 0.05, P values'' [START_REF] Hubbard | Why p-values are not a useful measure of evidence in statistical significance testing[END_REF]. This is because true posterior probabiltities (as they could be derived, for example, in a Bayesian framework) are generally larger than the corresponding P-values.

What is a significance test?

Two frequentist schools of statistical inference merged to form SST as it is practised nowadays: the Fisher and the Neyman-Pearson school. The logic of Fisher's null hypothesis testing consisted of two steps: (1) One sets up a null hypothesis. (2) One reports the P-value, called the level of significance. This approach ''lacks a specific alternative hypothesis and therefore the concepts of statistical power, Type II error and theoretical effect size have no place in Fisher's framework'' [START_REF] Gigerenzer | Mindless statistics[END_REF]. Fisher saw the P-value as an index measuring the strength of evidence against the null hypothesis: the lower the P-value, the stronger the evidence. He argued that ''no scientific worker has a fixed level of significance at which from year to year, and in all circumstances he rejects hypotheses; he rather gives mind to each particular case in the light of his evidence and his ideas'' [START_REF] Fisher | Statistical methods and scientific inference[END_REF][START_REF] Sterne | Sifting the evidence-what's wrong with significance tests?[END_REF].

In contrast, Neyman and Pearson's decision theoretic approach included the specification of two statistical hypotheses the null hypothesis, H 0 , and an alternative hypothesis, H 1 ), the Type I and II error probabilities (a and ß), and sample size before the trial or experiment is started. Based on these specifications, rejection regions for both hypotheses are defined. If the data falls into the rejection region of H 0 , H 0 is rejected in favour of H 1 , otherwise H 0 is not rejected. Thus, we use a decision rule for interpreting the results of our experiment or trial in advance, and the result of our analysis is simply the rejection or acceptance of the null hypothesis.

Today's practice of SST is a mixture of both schools. The P-value is both reported quantiatively and checked against the a-level to decide on the destiny of the null hypothesis. In the world of SST, P \ 0.05 warrants a call for action and the farther below 0.05 P falls, the stronger the call. P C 0.05 means that the status quo is to be maintained and the closer to 1.0 the P-value falls, the more secure the status quo seems to be. Studies with P C 0.05 are commonly considered as failed studies, that is, studies that failed to ''achieve'' significance [START_REF] Poole | Commentary: this study failed?[END_REF]. Does it make sense to adopt a new therapy because the P-value of a single study was 0.048, and at the same time to reject another therapy because the P-value was 0.052? That did not make sense to Neyman and Pearson [START_REF] Neyman | On the use and interpretation of certain test criteria for purposes of statistical inference[END_REF], but it does to some today [START_REF] Rabe | Treating COPD-the TORCH trial, P values, and the Dodo[END_REF].

Presumptions for the interpretability of statistical significance tests

For valid interpretation of SSTs, several presumptions or requirements have to be met. We point here to four of them: study size, correct statistical model, correct causal model, and absence of bias and confounding.

Study size

Given a fixed effect size, the P-value is a function of the sample size. Studies with small sample sizes tend to miss important clinical differences in significance tests. In contrast, studies with large study sizes tend to produce significant findings that are clinically meaningless. Despite these well-known relations between study size and statistical significance, study size related misinterpretations of SSTs are ubiquitous even after decades of educational articles. For example, the authors of a recent randomized trial hailed a statistically significant (P = 0.04) increase in median survival from 10.1 months to 11.3 among patients with advanced non-small-cell lung cancer as ''a new treatment option'' [START_REF] Rabe | Treating COPD-the TORCH trial, P values, and the Dodo[END_REF]. The clinical significance of that statistically significant difference is questionable.

In 1995, Altman and Bland noted that ''the non-equivalence of statistical significance and clinical importance has long been recognized'' [START_REF] Altman | Absence of evidence is not evidence of absence[END_REF]. It is just as well understood, and just as frequently ignored, that findings of potentially great clinical importance may be statistically non-significant. For example, the frequently replicated association between greatest tumor dimension (GTD) and prognosis is one component of the TNM-staging of uveal melanoma [START_REF] Sobin | TNM classification of malignant tumours[END_REF]. The authors of one study [START_REF] White | Correlation of cytogenetic abnormalities with the outcome of patients with uveal melanoma[END_REF] that replicated this association, with an estimated relative risk of poor outcome of 2.40 and a 95% CI of 0.98-5.88, failed to emphasize this result because, ''…of the clinical characteristics, only the presence of extraocular extension was associated with poor outcome''. By ''associated,'' the authors meant statistically significantly associated. The exceedingly plausible, highly replicated, clinically significant association with GTD was treated as no association merely because P C 0.05. We note in passing that a post hoc power analysis would not have changed that misinterpretation and, contrary to common beliefs, does not give any additional information in such situations [START_REF] Goodman | The use of predicted confidence intervals when planning experiments and the misuse of power when interpreting results[END_REF].

Stampfer et al. [START_REF] Stampfer | Effects of moderate alcohol consumption on cognitive function in women[END_REF] recently studied the association between daily alcohol drinking at baseline and cognitive impairment (three primary outcomes) during follow-up in the Nurses Health Study among 11,102 women. They concluded that ''women [drinking 1.0-14.9 g of alcohol per day] had significantly better mean cognitive scores than nondrinkers on all three primary outcomes''. They stressed that there were no significant associations with higher levels of drinking (15.0-30.0 g per day). A closer look at the distribution of drinking habits at baseline reveals that 51% of all women were nondrinkers, 44% were moderate drinkers (1.0-14.9 g per day), and only 5% (648 women) were in the highest drinking category. The results (Fig. 1) showed essentially the same association for the moderate and higher drinking levels compared with zero consumption, with a less precise estimate for the higher category owing to its low prevalence in the study population.

Correct statistical model

The validity of the calculated P-value, and therefore of the SST, depends on the correct statistical model. Referring again to the study of Stamper et al., the reported estimated relative risks are derived from a standard logistic regression model with alcohol consumption and several other confounders as covariates. Using this model explicitly assumes that (1) all confounders act linearly (and not, for example, in a quadratic relation) on the log odds of cognitive impairment, [START_REF] Hogben | Statistical theory: an examination of the contemporary crisis in statistical theory from a behaviourist viewpoint[END_REF] there are no interactions between confounders, (3) all important confounders were included in the model, (4) the link function between the probability of cognitive impairment and the confounders is indeed the logit function, and (5) no bias-inducing adjustment variables were included. Violations of any of these presumptions can potentially result in grossly biased P-values.

Correct causal model

To validly estimate the association between a treatment and outcome, presumptions have to be met regarding the timeline of treatment and treatment effect (see Fig. 2). Let us assume a beneficial treatment has to last for at least 6 months to reduce the risk of an adverse outcome and it takes an additional 6 months for that risk reduction to occur A clinical trial of this beneficial treatment would be Fig. 1 Association between daily alcohol consumption and risk of cognitive impairment in the nurses health study [START_REF] Stampfer | Effects of moderate alcohol consumption on cognitive function in women[END_REF]. Multivariate relative risk of cognitive impairment among women who drank 1.0 to 14.9 g of alchol per day, as compared to nondrinkers. TICS denotes the telephone interview for cognitive status. The verbal memory score reflects the results of immediate and delayed recall of both the TICS 10word list and the East Boston Memory Test. The global cognitive score is the average of the results of all cognitive tests meaningless if the treatment duration, the follow-up duration or both are too short. In the field of outcomes research, rough a priori ideas about these time intervals are often available. However, a priori ideas about these intervals may be more vague in other fields of biomedical research, including the study of etiologic factors. The Women's Health Initiative (WHI) study assessed the association between hormone replacement therapy (HRT) and risk of breast cancer [START_REF] Rossouw | Risks and benefits of estrogen plus progestin in healthy postmenopausal women: principal results from the women's health initiative randomized controlled trial[END_REF]. The risk of breast cancer among HRT users was not increased during the first 3-4 years of follow-up. Thereafter, the cumulative risks diverged, showing a higher risk among women with HRT. If the follow-up time had been only 3 years, the increased breast cancer risk could not have been observed in that study.

No bias and no confounding

Although it sounds like a truism, the interpretability of P-values and SSTs also depends on the absence of bias and confounding, because bias and confounding can influence the test size, power, or both-for better or worse. For Ronald Fisher, randomization was necessary to ensure that the estimates of error and tests of significance ''should be fully valid'' [START_REF] Fisher | The design of experiments[END_REF]. In contrast to randomized trials, in which randomization provides a solid theoretical basis for the probability models from which P-values are derived, such a mechanism is missing in observational studies. Hence, precise and hairsplitting interpretations of P-values and confidence intervals are even less wise in observational research than they are in randomized trials [START_REF] Poole | Low P-values or narrow confidence intervals: which are more durable?[END_REF].

Interpretation of nullhypothesis significance tests in presence of prior studies

How can we ignore available prior studies that dealt with the same study question as our own study when interpreting our own results [START_REF] Poole | Commentary: this study failed?[END_REF]? For example, a nonsignificant association between smoking (ever vs. never) and lung cancer (e.g., RR = 2.0, 95% CI: 0.9-4.2) in a cohort study should not prompt investigators to claim that there is no association between smoking and risk of lung cancer and therefore contradicting evidence. Their non-significant finding must be seen in light of the compelling evidence that smoking is a risk factor of lung cancer. Yet, many authors discuss their own study findings as if their study would be the first and only study addressing the specific study question, as in the study of GTD and uveal melanoma prognosis discussed above [START_REF] White | Correlation of cytogenetic abnormalities with the outcome of patients with uveal melanoma[END_REF]. In medical research, decisions are rarely made on the basis of a single trial or experiment [START_REF] Rothman | A show of confidence[END_REF] and many studies that are not statistically significant contribute to the overall weight of the evidence upon which associations come to be accepted as causal. Figure 3a, b illustrate the two ways of thinking: ignoring any prior evidence and accounting for it. Conducting systematic reviews has the immense side benefit that one comes to view the contributions of individual studies in a greatly improved light [START_REF] Poole | Commentary: this study failed?[END_REF].

The mixture of SST schools (Fisher vs. Neyman-Pearson) tempts investigators with strong prior beliefs of their hypotheses to switch between SST schools depending on the results of their SSTs. For example, consider an investigator who strongly believes that a factor would be an important predictor of a poor outcome of a disease observes a nonsignificant association at a = 0.05 (the predefined Type I error according to Neyman and Pearson) between this factor and the outcome. This investigator might write, ''Our study result was close to significance (P = 0.08),'' and might even seriously discuss the ''almost positive'' study finding or ''trend'' [START_REF] Pocock | Translating statistical findings into plain English[END_REF]. Frequently, those investigators claim that their study finding would have become significant if the study size would have been larger. However, if the investigator does not believe in a real association between a factor and an outcome and the association becomes significant, this investigator might write, ''Our study result (P = 0.03) may be due to chance,'' and might argue why this finding most likely is an artefact due to unexplained errors including bias and confounding-again circumventing the Neyman and Pearson decision-theoretic approach.

Null hypothesis significance testing where chance plays the only role or no role P-values are frequently completely senseless. One example is the typical table 1 of the publication of a randomized controlled trial (two groups) presenting the distributions of study characteristics just after randomization by group. These tables are frequently accompanied by SSTs comparing the treatment groups with each other in order to assess comparability (exchangeability) between the groups. The mechanism that produced these distributions is a chance mechanism by definition. That is, we know in advance that the SST of equal means or proportions in treatment groups is true and so it is completely useless to test this hypothesis. If randomization worked, we expect that on average one out of 20 variables will show statistically significant differences (P \ 0.05) between the groups. Therefore, the assessment of chance as an explanation of an imbalance of confounders between the groups by SSTs is unreasonable as the answer is yes, regardless what the result of the SST is. It is the magnitude of imbalances between the groups that is important to consider [START_REF] Altman | A fair trial?[END_REF]. The meaning of these imbalances is based not on statistics but on subject matter judgment. For example, in a large antihypertensive treatment trial, at randomization one group may have a mean systolic blood pressure of 148 mmHg (standard deviation, SD = 5.6) and the other group a mean systolic blood pressure of 150 mmHg (SD = 5.7). Is the 2 mmHg difference of the mean blood pressure values a relevant difference from a clinical perspective? This question cannot be answered by SSTs. It can be answered by clinical judgment or by a quantitative evaluation of confounding.

Similar unreasonable uses of SSTs can be observed in observational studies. For example, it is popular to study the distribution of matching factors (for example age) between cases and controls (see for example [START_REF] Main | Flame retardants in placenta and breast milk and cryptorchidism in newborn boys[END_REF]). However, the similarity of the distributions of the matching factors among cases and controls is dependent on the quality of the matching, which is under control of the investigator. Comparisons of those distributions are therefore not scientific findings.

What is a confidence interval?

For a given estimate, a 95% confidence interval (CI) is the set of all parameter values (i.e., hypotheses) for which P C 0.05 [START_REF] Poole | Low P-values or narrow confidence intervals: which are more durable?[END_REF]. If the underlying statistical model is correct and there is no bias, the proportion of CIs derived over unlimited repetitions of the study containing the true parameter value is no smaller than 95% and is usually close to that value. This means that a confidence interval produces a move from a single value, or point estimate, to a range of possible effects in the population about which we want to draw conclusions.

A confidence interval conveys information about both magnitude of effect and precision and therefore is preferable to a single P-value [START_REF] Rothman | Significance questing[END_REF]. Although CIs can be used for a mechanistic accept-reject dichotomy by rejecting any null hypothesis if the value indicating the null effect is outside the interval, they can and should move us away from that dichotomy. The width of a CI indicates how much the point estimate is influenced by chance. For instance, when a relative risk estimate of 4.1 (95%CI: 1.2-14.0), P = 0.02, is compared with an estimate of 1.4 (95%CI: 0.8-2.4), P = 0.20, the first is much more influenced by chance (as reflected by its wider CI) and is therefore much less trustworthy, even though the first estimate is statistically significant and the second is not [START_REF] Poole | Low P-values or narrow confidence intervals: which are more durable?[END_REF]. Since 1999, the Board of Scientific Affairs (BSA) of the American Psychological Association (APA) has recommended the presentation of confidence intervals for all effect size estimates [START_REF] Wilkinson | Task force on statistical inference. Statistical methods in psychology journals: guidelines and explanations[END_REF].

Conclusions

It has been stated that the P-value is perhaps the most misunderstood statistical concept in clinical research [START_REF] Blume | What your statistician never told you about P-values[END_REF]. As in the social sciences [START_REF] Loftus | On the tyranny of hypothesis testing in the social sciences[END_REF], the tyranny of significance testing is still highly prevalent in the biomedical literature, even after decades of warnings against SST [START_REF] Rothman | A show of confidence[END_REF]. The ubiquitous misuse and even tyranny of significance testing, given the authoritarian way in which it tends to be practiced, threatens scientific discoveries and may even impede scientific progress [START_REF] Goodman | A dirty dozen: twelve p-value misconceptions[END_REF]. In the worst case, it may even harm patients who eventually are incorrectly treated because of improper handling of P-values. As Sterne and Davey Smith recently reminded us, ''In many cases published medical literature requires no firm decision: it contributes incrementally to an existing body of knowledge'' [START_REF] Sterne | Sifting the evidence-what's wrong with significance tests?[END_REF]. Biomedical research is an endeavour in measurement. Its objective is to obtain a valid and precise estimate of a measure of effect, the true value of which may be no effect,

Ignoring Prior Studies

Accounting for Prior Studies Fig. 3 Interpretation of study results ignoring and accounting for prior studies a small and unimportant effect, or a large and meaningful effect. We should strive for good data description and careful interpretation of estimated effect measures and their accompanying precision rather than mechanical significance testing with yes-no answers which lead inherently to biased interpretations. An important way out of significance fallacies such as those we have described is to interpret statistical findings based on confidence intervals that convey both the size and precision of estimated effect measures.

Disclosure None of the authors reports any conflict of interest.

Fig. 2

 2 Fig. 2 Minimum treatment duration, minimum latency period, and observable treatment effect on outcome


Keywords: information retrieval, user profile, personalized IR, indexing, user re-indexing, ontology

Information Retrieval is a process made by a user to obtain relevant information which meets his needs using an Information Retrieval System (IRS). However the IRS shows some differences between user relevance and system relevance. These variations are primarily related to the imperfection of the indexing process (approach directly related to the IR) and specially the non consideration of the user profile. This paper presents a study about formalisms proposed in the literature and addresses some reflections to deal with problems arising from this survey, in order to satisfy the final user in Information Retrieval process.

I. INTRODUCTION

As an Academic field of study Information Retrieval (IR) might be defined thus "Information Retrieval is finding material (usually documents) of an unstructured nature (usually text) that satisfies an information need from within a large collections (usually stored on computer)" [START_REF] Baeza-Yates | Modern Information Retrieval[END_REF]. Furthermore, in the case of textual documents in which we are interest a significant part of difficulties are due to the difficulty lies in ambiguity inherent to human languages [START_REF] Van Rijsbergen | A non-classical logic for information retrieval[END_REF] and the carelessness of the user. As a consequence, we observe that in order to build more robust Information Retrieval System (IRS) able to interact naturally with human, we should imply the user. The work presented in this paper deals with an overview of IR. In section III, we present the main information retrieval models in section IV we present the evolution of the classic information retrieval to adaptive information retrieval, our work in progress, proposing some key ideas in order to improve information retrieval. Finally, we conclude this paper.

II. FOUNDATIONS OF CLASSIC INFORMATION RETRIEVAL

In this section, we present the foundations of classic Information Retrieval and an outline of the main models.

According to Rijsbergen who defines Information Retrieval as "The user expresses his information need in the form of a request for information, Information Retrieval is concerned with retrieving those documents that are likely to be relevant to his information need as expressed by his request". This definition contains two important things that we should define: the document and the request.

-The document: we call document any unit of information which can constitute an answer to a user's request. It can be a text, a part of text, a picture, a video band -The Request: constitutes the expression of the need in user's information, the user has to subject to the search engine his need in information. Diverse types of query languages have been proposed in the literature: List of keywords: SMART system and OKAPI [START_REF] Robertson | Okapi at trec-7: Automatic ad hoc, ltering, vlc and interactive track[END_REF], [START_REF] Lafferty | Probabilistic relevance models based on document and query generation[END_REF] Natural language: SMART system and SPIRIT [START_REF] Paijmans | SMART: Tutorial for Beginners[END_REF] Boolean language: DIALOG system [START_REF] Bourne | Dialog labworkbook. PaloAlto[END_REF] Graphic language: NEURODOC [START_REF] Paijmans | SMART: Tutorial for Beginners[END_REF] The principle goal of an information retrieval system is to select the nearest documents that answer a user request. For that purpose, the information retrieval system regroups a set of methods and procedures allowing the management of the collections of documents stored in the form of an allowing intermediate representation. Thus, the interrogation of the collection of documents by means of request requires the representation of this last one under a shape one unified compatible with those of documents. These features are represented with a global process of IR, collectively named process in U illustrated in figure 1. This process consists in two main phases: the indexing and the interrogation.

A. The indexing process: it's a very important step in the process of information retrieval it consists of determining and extracting the representative terms of a document or request. The result of the indexing process is called descriptor: it can be a list of terms, or a set of significant terms. The descriptors are grouped in a catalogue called "Dictionary». This dictionary constitutes the language of indexing process.

There are different types of indexing: manual indexing, semi-automatic indexing and automatic indexing.

Towards an architecture for personalized information retrieval implying the user's profile and votes azza.harbaoui@gmail.com, 2 Sahbi.Sidhom@loria.fr, 3 malekghenima@gmail.com, 4 Henda.BG@cck.rnu.tn -Manuel or intellectual indexing: an expert chooses terms that he judge relevant in the description of the document content. Furthermore, this approach is subjective on the one hand because it depends on the expert's knowledge, and not very efficient for voluminous collection. -Automatic indexing: It's based on algorithms that associated automatically descriptors for parts of documents. -Semi-automatic indexing: it's a combination of the two previous processes: a first automatic process for extracting terms form documents, then a specialist of the domain will use a vocabulary of thesaurus or a terminological base. According to [START_REF] Baeza-Yates | Modern Information Retrieval[END_REF], the basic techniques of automatic indexing are made according to these steps:

1. Collect the documents to be indexed 2. Tokenize the text 3. Do linguistic pre-processing of collections 4. Index the documents that each term occurs in In order to find the terms that represent best the semantic contents [START_REF] Baeza-Yates | Modern Information Retrieval[END_REF] has defined term frequency (TF) and Inverse Document Frequency (IDF).

-TF: This measure is proportional with the frequency of the term in the document: the idea is that more the term is frequent in the document, more it's important in the description of the document. -Inverse Document Frequency: it measures the importance of the term in all the collection: the idea is that the terms that less appears in the collection are more representative of the document content than those that appear in all the documents collection.

B. The Interrogation mechanism:

The interrogation system implies a process of interaction with the user illustrated in figure 1.In fact this interaction includes the formulation of the request by the user that translate his need in information, the representation of the query in an internal form according to a defined indexing language, the correspondence between the request and the documents. More precisely, the interrogation implies the following scenario: The user expresses his need of information with a request. The system will index the request and create a request index which is compatible with the documents index. Then the system evaluates the relevance of the documents comparing to the request using a function of correspondence. This function interprets the generated index in order to calculate a score of similarity called "Relevance Status Value" (RSV). Thus, several kinds of models in information retrieval have been proposed in the literature trying to formalize the relevance starting from basic models towards more elaborated ones.

III. INFORMATION RETRIEVAL MODELS

According to [START_REF] Lalmas | Advances in Information Retrieval, 28th European Conference on IR Research, ECIR 2006[END_REF], a model gives a formalization of the information retrieval process as well as a theory for the modelling of the measure of relevance. Because of space limitations, we will not present all the models. References are provided to give further information concerning these works.

----Boolean Model

This model was proposed to represent the request in the form of logical expression. The indexing terms are connected with logical connectors (AND, OR, NOT). A Boolean model only records term presence or absence in order to realize an exact matching with the equation of the request. The documents that satisfy the logical request's expressions are considered as relevant.

Thus the Retrieval Status Value (RSV) is the logical result of d→q (d: document, q: request) in the extension of this model, the selection of documents is based on approximately matching not exact one. The Boolean model is easier to implant and requires relatively few resources. Furthermore, it is sometimes difficult for the user to express his information need's with boolean expression in particular for the complex request, what does not allow to make best use of the model's features.

----Vector Space Model

Inspired by Luhn [START_REF] Grangier | Information retrieval on noisy text[END_REF], the vector space model, introduced by Salton [START_REF] Paijmans | SMART: Tutorial for Beginners[END_REF] in the SMART project (Salton's Magical Automatic Retriever of Text).This model is based on the mathematical bases of vectorial spaces. The request and the document are represented in the vector space generated by indexing terms. In fact, the degree of relevance of a document toward a request is proportional to the position of these vectors in the space. It's evaluated with a correlation degree correlation between associated vectors. The similarity measure between documents and requests can be made of several manners: for example by a scalar product, by a measure of train which forms vectors, by the measure of the angle which forms the vectors, or still by a measure of distance. The measure of similarity SIM(D, Q) can be calculated according to:

i n i ij j q d Q D SIM . ) , ( 1 ∑ = = (Scalar Product) Or ∑ ∑ ∑ = = = • • = n i n i i ji i n i ji j q d q d Q D SIM 1 1 2 2 1 ) , ( (Cosine)
We can also use a measure of distance:

2 ) 1 ( ) , ( i q n i ji d Q j D DIST - ∑ = = (Euclidian distance).
Other measures of similarity are useful, being situated halfway enter the vector space model and the Boolean model such as:

∑ ∑ ∑ = = = = = = + × = n i i ji n i i i n i i i ji j d q q d Q D cient DiceCoeffi 1 1 1 2 ) , ( : - -Jaccard Coefficient ∑ ∑ ∑ ∑ = = = = - + = n i n i ij n i i ij i n i ij i j d q d q d q Q D JACCARD 1 1 1 2 2 1 . . ) , ( -Pseudo cosine ∑ ∑ ∑ = = = = = = + × = - n i i ij n i i i n i i i ij j d q q d Q D pseudo 1 1 1 ) , cos( -Measure of recovery: ) , min( . ) , ( 1 Q D q d Q D recouvr j i n i i ji j ∑ = = =
The advantages of the vector space model are many: -Conceptual Simplicity and implementation -Uniform representation of the document and query -Results are ranked in order of relevance using a similarity measure. Many methods of sequencing results were compared with the vector model, and the later, despite its simplicity, is higher than or at least as good as the other alternatives. For all these reasons today the vector model is most popular in information retrieval. Furthermore, the vector space model has the "theoretical" inconvenient that all the terms of the index are independent, in practice, the global consideration of the terms dependence can make lower the quality of the system's answer (because dependence are in general local).

----Probabilistic Model

This model [START_REF] Research | Web query characteristics and their implications on search engines jason zien, j org meyer, john tomlin[END_REF], [START_REF] Brini | Relevance feedback : introduction of partial assessments for query expansion[END_REF] treats the problem of information retrieval in a probabilistic way. The relevance documentrequest is translated by the calculation of the probability of relevance of the document with regard to a request.

The probabilistic approach was developed by Turle et Croft in INQUERY, where the indexing uses the probabilistic model. In order to calculate the probability that the relevant event arrives knowing the formulated request (Q) and the Document (D), INQUERY uses Bayesian network by taking into account dependences between certain events. It's advantage that it takes into account in particular between the document and its representation.

Probabilistic model are focused on document and query. It uses simple framework and efficient computation time and performance. Furthermore, distribution of the terms for relevant and irrelevant documents should be known or requires labelling.

----Language Model

It's a statical language model assigns a probability to sequence of m words P(w 1 , ... , w m ) by means of a probability distribution. This model tries to capture the proprieties of a language and to predict the next word in a speech sequence. When used in information retrieval, a language model is associated with a document in a collection. With a query Q as input, retrieved documents are ranked based on the probability that the document's language model would generate the terms of the query, P (Q|M d ).

----N-gram model

It's a type of probabilistic for predicting the next item in such a sequence. N-gram models are used in various areas of statical natural language processing (NLP) and genetic sequence analysis. In an n-gram model, the probability P(w 1 , ... , w m ) of observing the sentence w 1 ,...,wm is approximated as:

∏ ∏ = = - - - - ≈ = m i m i i n i i i i m w w w P w w w P w w P 1 1 1 ) 1 ( 1 1 1
) ,..., ( ) ,..., ( ) ,..., (

The language models provide an interesting method for developing information retrieval in a well-defined theoretical framework. Although, language models require a Part-Of-Speech-Tagging by assigning each word with its grammatical category. As a language model is a probabilistic concept, it will determine the probability of each bigram that appears in a given text. In fact, the common point to all the models in classic Information Retrieval is that all the selected documents has to contain all the words(or a part of), formulated by the closest semantically to the user's need. Thus, the relevance of the selection relies mainly on the quality of the indexing and matching mechanism. Nevertheless, in the practise the majority of the requests expressed by the users are ambiguous [START_REF] Trajkova | Improving ontology-based user pro_les[END_REF] and short, what gives incoherent specification onto their need in information Besides, the list of terms does not correspond necessarily to those used to index the relevant documents of the collection and always miss a significant term that really expresses the user's need. This leads to crucial problems in classic information retrieval: the first problem is called "term mismatch problem".

The second problem is ambiguity [START_REF] Wen | Probabilistic model for contextual retrieval[END_REF]: the user and the author of the document do not use the same vocabulary, so that a document can be relevant even if it does not contain the same terms as those of the request. However, the classic information retrieval system answers invariably the users by sending back the same results to two different users having expressed the same request but having different needs in information and different preferences.

Therefore, the performance of an information retrieval system, do not depend only on the efficiency and quality of mechanisms of indexing and matching but also on the capacities of interaction with the user in order to better satisfy what his expectations. Of this report appears the adaptive information retrieval.

IV. FROM CLASSIC INFORMATION RETRIEVAL TO ADAPTIVE INFORMATION RETRIEVAL

The goal of the adaptive information retrieval is to adapt the process of information retrieval in order to return relevant results to the user. Techniques developed in adaptive information retrieval [START_REF] Mobasher | The Adaptive Web: Methods And Strategies Of Web Personalization[END_REF] focus mainly on the assistance in the reformulation of the request.

Reformulation of the requests

It aims to generate a new request without explicit retroaction of the user, just by exploring the first documents presented by the system as an answer to the user's request (blind feedback).Different techniques were introduced in the literature, in models of information retrieval such as Vector Space Model [START_REF] Tanudjaja | Persona : A contextualized and personalized web search[END_REF], and the probabilistic model. We quote the techniques of the reformulation of the requests by relevance feedback, techniques of disambiguation of the request's sense or the techniques of clustering.

Disambiguation of the request's sense

The aim of these techniques is to assist the user in order to better express his need by adding additional semantic resources [START_REF] Sieg | Using concept hierarchies to enhance user queries in web-based information retrieval[END_REF] (dictionaries, networks, thesauruses).Most of them are based on exploring interactive interfaces of clarification based on ontology The figure 2 shows an example of an ambiguous query (apple) on Google and the semantic categories that offers Google to the user in order to assist him to find the expected result. In this example, the user is searching for apple which is an ambiguous request because "apple" can be "Apple iPod", "apple fruits", Google proposed disambiguation of the request and the user choose apple fruits from the list proposed by Google

Techniques of Clustering

In order to face major growth of the web, and the difficulties faced by the classic search engines, the aim of the clustering techniques [START_REF] Wen | Clustering user queries of a search engine[END_REF] is to group the results into categories such as Grouper [START_REF] Tamine | Exploiting multi-evidence from multiple user's interests to personalizing information retrieval[END_REF], Vivissimo, Kartoo, as well as techniques of repertorisation of the web into taxonomy of concepts (Google hierarchy).All these techniques were developed in order to satisfy the user and to simplify his navigation. Thus, the users will have a virtual classification of the documents. Several ontology of domain has been conceived in order to build Conceptual information retrieval. The theoretical study that we carried out reveals that beside the earnings bring by the techniques of adaptive information retrieval, there still some limitations that we explicit in these points:

i) The context: The context in adaptive information retrieval is not well defined the techniques of reformulation of the requests have to help the user to select documents via interfaces that clarifies the information needs. The context is limited with the terms that the user uses to express his need in information Thus the context in which the research is made is rarely used to interpret the request (the geographic situation for example) .

ii) Limitations due to the explicit interaction: The techniques of reformulation of the request by relevance feedback need an explicit interaction with the final user. Besides, among the studied works, the majority of the users are not motivated to interact with the system, they prefer the automatic mechanism that ameliorates the results without asking for additional information

iii) The impact of the familiarity of the user with the subject:

The relevance of the techniques of the query's reformulation depends relatively on the one hand on the level of familiarity of the user with information retrieval system [START_REF] Ruthven | A survey on the use of relevance feedback for information access systems[END_REF] and on the other hand on the number of iterations of research.

Although the limitations of the adaptive information retrieval, it has certainly brought solutions in particular in the mechanism of matching document and request and always ameliorates the performances of the information retrieval process. However, fine analyses of the domain shows the adaptive information retrieval depends on many factors described above.

Figure2: An example of request's disambiguation

Thus, in order to ameliorate the performances of information retrieval system, many studies focused on the impact of the cognitive dimension of the context defined with the user's interest and his preferences.

V. CONTEXTUAL AND PERSONALIZED INFORMATION

RETRIEVAL

According to [START_REF] Allan | Challenges in information retrieval and language modeling: report of a workshop held at the center for intelligent informationretrieval[END_REF], the contextual information retrieval is defined as "combine search technologies and knowledge about the query and user context into a single framework in order to provide the most appropriate answer for a user's information needs".

In the literature [START_REF] Ruthven | A survey on the use of relevance feedback for information access systems[END_REF], the context of research is bound to dimensions dependent to the user, the request and the environment of research. We are interested in personalized information retrieval which is a specific branch of contextual information retrieval. The goal of personalization in information retrieval is to tailor the search engine results. The first approaches of the contextual information retrieval have focused on the context of the user represented by his profile. This context includes his interests, his votes, his comments, and also his knowledge. In fact people's future preferences and needs must be able to personalize services, the same way recommender systems do. The personalization is a process which changes the feature, the interface, the content information or the aspect of a system in order to increase his personal relevance related with the socio-demographic characteristics declared by the user and his observed behaviour contained in what we call user model. This model describes any information about the user, as his preferences, his need in information and also his environment of research. The applications are diverse: recommender systems, filtering systems, learning systems as well as information retrieval systems. Independently of the aimed application objective, we identify three main aspects to be promoted in the personalized access systems: -The capacity to identify the abstract intention of the user -The flexibility of the process selection in order to adapt itself to the common context of the user -The interactions between the user and the system Thus, the relevance of these systems depends on the precision of the exploited user models. Indeed the modelling of the profile constitutes the essential element in the development of a successful personalized system. To reach this goal, we are now focusing on the construction of the user profile, and its representation in order to ameliorate the information retrieval. The profile [START_REF] Van Dijk | Alter Ego: State of the art on user profiling[END_REF] can reflect the interest of the users in several subjects at one particular moment. It's a database where the user information, interests and preferences are stored. A general improvement of personalization systems would be taking into account more semantics in the process. This aspect has to be considered in the construction of the profile (and thus in the user model). One way to deal with this task is to consider some results coming from the "Indexing Process" and try to ameliorate it by implying the user (his comments, his history, his virtual communities...). In fact, the idea is to integrate the user profile in the indexing process in order to achieve a process that we called "Re-indexing".

We implement the first part of our architecture which is the baseline system as shown in Figure 3.

VI. WORK IN PROGRESS

The reflections presented in this paper are situated precisely in the context of personalized access to information. We are now focusing on the construction of architecture, as shown in Figure 4. The objective is to add semantic value to information retrieval systems toward a new indexing process or re-indexing of the contents. We are interested to imply the user, his profile, his traces, his culture, and his point of view in order to collect all these points into ontology of interest. In the proposed architecture we start from a classic information retrieval: we want to proceed first with the Baseline System (Figure 4) that we have already implement observing the results in order to compare after with the process of "re-indexing". We focus on the indexing process by adding semantic values in order to re-index the contents by the votes of the users. In fact, first a user is connected via a user interface, asking for information with a query, the information retrieval system will treats his need and send to the user the documents selected. Ontology of interest will be created and updated later. When a new user is connected to the system, his ontology of interest is created too, and the system will recommend to him previous selected documents that matches the best his profile. Our architecture is divided into two main parts: The classic information retrieval (Baseline System) and the personalized IR( which includes the Re-indexing process) where the user is involved with his profile. In the first part of our system, we implement a generic indexing tool for structured, semi-structured and nonstructured documents. We used the vector space model (described in section II).For non-structured documents or "Full text", we use a test corpus CACM(Communications of the ACM) composed of 3204 documents with the title and links to bibliographic citation in the field of computer science,64 queries and a file of assessments. We followed the steps bellow to generate the inverted documents file and the queries inverted file:

-Tokenization, -Dropping common terms (Stop words), -Stemming and lemmatization. As shown in figure 4, we have an indexing process applied to corpus and a set of queries in order to have two inverted files (for the corpus and the queries), we have then a module of similarity having as input the inverted files, as output a similarity file. The result obtained with the assessment file is used by TREC-EVAL to generate a result file. We used the TREC-EVAL result for the curve of Recall/Precision in order to evaluate our indexing approach with metrics of evaluation (Recall/Precision). Bellow some user interfaces of our indexing tool. The first user interface (Fig. 5) allows to him to choose the language (English or French) and the type of the documents (structured, semi-structured, and non-structured). In the second user interface (Fig. 6), the user has to load the corpus and the queries to prepare them for the indexing process. The third user interface (Fig. 7) is reserved for calculating the similarity for each document of the corpus with each query. The fourth user interface(Figure8) is reserved for evaluation using TREC-EVAL, the user has to load the assessment file, to save and to obtain the result of Precision/Recall(Figure 9).

VII. CONCLUSION

Although a considerable number of works focused on information retrieval, some important challenges for the research community still remain. This paper provides a brief survey of the evolution of information retrieval from the classic to the contextual, the problems faced. We have proposed architecture of personalization by implying the user with representing his interests and profile into an ontology of interest, then using this ontology for re-indexing of the contents. We have exposed our indexing tool for baseline system and observed the results of Recall/Precision. As future work, we have to focus on the process of Re-indexing by implying the user (profile models, profile construction and profile evolution.).In fact, a key part of a personalization system is the user model. We have to develop an expressive model in order to encompass all aspects of the user taking into account the environment that the user interacts with and try to identify changes in user interests and needs. Finally we can say that the ultimate goal of personalization system being the satisfaction of the user. To reach this goal, the user has to be implied in the construction process in order to add the semantic value to the information retrieval.

Figure 1 :

 1 Figure 1: Information Retrieval process (U form)

Figure 3 :

 3 Figure 3: Architecture of the Baseline System

Figure 4 :

 4 Figure 4: Proposed Architecture of our system

Figure 5 :Figure 6 :

 56 Figure 5: First User Interface

Figure 7 :

 7 Figure 7: Third User Interface

Figure 8 :

 8 Figure 8: Fourth User Interface

Figure 9 :

 9 Figure 9: Curve of Recall/Precision


Michael Scherer

Sahbi Sidhom

Progress of concepts and processes in library information system: towards Library 2.0

Keywords: Library information system, economic intelligence, watch process, information needs, knowledge organisation

The main principle of the Library 2.0 is in the fact that the information has to be spread from the library to the user and viceversa, to allow fast and permanent adaptation of the library services. Within the framework of the implementation of the "Departmental Plan of the Public Services Reading" by the "General Council of Moselle", the division of the public reading develops a departmental portal as main vector of the information with various users' profile. The context of this research work takes a part of a Master degree training Diploma in STI-Economic Intelligence (Nancy2 University), combining facets of R&D in a professional context at the "Conseil Général de la Moselle" in France.

I. INTRODUCTION

ublic needs change, manners change and the library has to answer to new practices. It is not any more question of giving physically documents to readers within library, media library or multimedia library infrastructures. We are crossed in the era of libraries 2.0 with the connection to Web 2.0. The major principle of the library 2.0, living in the fact that the information has to circulate from the library to the user and from the user to the library, is to allow a fast adaptation and perm services. The last investigation on the cultural practices of French people confirmed the fact: "the number of subscribers in libraries decline; young people, turned to new numeric practices, they are less interest and don't come usually to library infrastructures". The library professionals wonder about the risk of obsolescence of the library equipments and think about the possible solutions.

-"Should we speak about the concept of the digital library?" 94 % of 18-24 yrs. people have a computer [START_REF] Bigot | La diffusion des technologies de l'information et de la communication dans la société française[END_REF].

-"Have they still reasons for frequenting a library?" At the same time, the public reading is in the target of most current stakes in the public politics. It is enough to see actually numbers of projects to create new libraries or media libraries with the fact "No interest" shown by cities for these infrastructures: It is the paradox about the actual situation. User's notion is consequently very important in the implementation of a multimedia library system. Users must be considered as consultants, participants, co-creators; this is to allow the innovation as well in the virtual services as the physical services of a library or a media system.

Within the framework of the implementation of the "Departmental Plan of Public Reading" [START_REF] Valentin-Bemmert | Schéma Départemental des Services de Lecture Publique 2009-2014[END_REF] by the "General Council of the Moselle", the Service of the Development of Territories and the Public (SDTP), under Pascale Valentin-Bemmert's responsibility, as Project Manager, develops a departmental portal of the public reading. In this context, the main reflections turn around the following axes, as a set of questions "How to...?":

-Go towards the user; -Open to the user, that he becomes "a contributor"; -Improve the existing tools with the deepening of the traditional missions; -Propose new services with the diversification of the traditional missions. These questions are proposed in the objective of this library 2.0 portal. It is therefore to understand the stakes of this portal, to know the decision maker and his objectives, to understand the decision problem and to convert it into information retrieval problem. Also, to think about the information integration in the library 2.0 system, their updates and about the collaborative aspects to define: "What will have access to the user?". It is this report problem that concerns the problematic of our paper. This paper is articulated around three research dimensions, namely: the User, the Information and the Knowledge. We integrate a modelling work on the library information system opened to the multimedia objects. In a first part, section II, we present the methodology and the implemented tools. Then in section III, we present the results obtained with their impacts. At last section, we approach the perspectives of the library 2.0 information systems. We want our readers to observe that this work has been done in the context of a Master degree training Diploma in STI-Economic Intelligence (Nancy2 University), combining facets of R&D in a professional context at the "Conseil Général de la Moselle" (Metz) in France.

II. METHODOLOGY AND TOOLS

Since the Internet networks, the quantity of information doesn't stop increasing and it's important to target well these information resources to propose the most relevant. It is a question to:

Progress of concepts and processes in library information system: towards Library 2.0

Michael SCHERER (Conseil Général de la Moselle, France), Sahbi SIDHOM (LORIA/KIWI & Nancy Université, France) e-Mails: michael.scherer@cg57.fr, sahbi.sidhom@loria.fr P i)-Qualify the resources to organize routes (courses) in the mass of the contents:

-Make readable the contents, -Help to find for what we do not look: leave information, re-cut it, develop it, etc. -Show the professional competences and recommendations. ii)-Build with the user:

-Comments: suggestions, requests and preferences of users, -New information contributions.

To anticipate at the best our reflections (i, ii), we set up a methodology applied to the study dimensions, as well as the working tools.

A. Applied Methodology

"How to organize the courses of the users in the mass of the library resources?" The complexity of this problem lives in the fact that several axes of reflections are to be taken into account. We shall speak about three dimensions for the construction of a coherent information system [START_REF] Sidhom | Conception de systèmes d'information multimédia[END_REF] (Fig. 1): a) the dimension of "actors" (or users: U); b) the dimension of "information" (I) which circulate and to capitalize; c) the dimension of "knowledge" (K). An association takes place in these three dimensions (cf. Fig. 1), namely:

-Documents and their diversity: from text to multimedia; -Processes and interoperability in the indexing and in knowledge management; -Users and their information needs: the indexer, the watcher and the decision maker through the sharing processes (information, knowledge and collaboration).

We can separate this representation in two parts:

-Construction/Modeling with links and transitions between actors, information and knowledge;

-Exploitation, visible part by the public (H-M interface).

The information integration by the user and the transition of information to knowledge is an important phase in a (multimedia) library information system. This phase is made with the importance given to the transitions with the "kernel" about actors, information and knowledge. In this project, the notions of information validation by the user, the accessibility to information are also taken into account.

The document indexing process is an important stage in the implementation of a library system and the information retrieval. If the indexation is not relevant, the document will not be found with regard to a user query. It is important to transform and to find communication symmetry between the natural language and the documentary language. Today, it is useful to take into account the index descriptors in the form of (social) tags or a set of referenced words (in thesaurus), which are realized by users directly and with their own terminology.

In the information dimension, the contents and the capacity to find it in a most coherent way are essential keys of the system.

B. Working Tools

Within the framework of the implementation of a multimedia library information system, we evolve on three dimensions: user (U), information (I) and knowledge (K). Each dimension has its importance because of its implication in the functional architecture of the library system.

User Dimension

The exploration of the user axis, we proceed to a modelling of the decision-maker and its environment. It is to make explicit the knowledge of the decision-maker and about the knowledge of his environment. We are going to meet on a structure a set of knowledge about the decision-maker and knowledge about the organization environment (library infrastructure).

The decision-maker in the internal and external environment of the organization: "who is capable of identifying and raising the problem to be resolved into term of stake, risk or threat which presses on the company". In other words, he knows the needs of his company, the stakes and possibly the risks.

In our reflection, the environment of the decision-maker is rather particular because it gets organized around a complex organization of the "Division of the Public Reading" and Libraries. This division consists of 4 poles distributed on the "Mosellan territory" all around of which weaves 135 libraries.

The Department of the "Moselle" chose, via the Departmental Plan of Reading on Public 2009-2014 to decentralize all the missions and the services of public reading. The notion of portal as collaborative space can put in the Direction of the Resources and the Computing Services of the problems of access and security. It is one of notions to be taken into account at the time of the study of the proposed library information systems by companies. Indeed, the current IT environment is complex with centralized IT architecture and security constraints which, at the moment do not allow us to reach in certain "collaborative" sites (like facebook) and sites of audio, videos… resources.

Workgroups Professional networks

Level of comptences 3

Level of comptences 2

Level of comptences 1

Users

Information about user :

-Psycho-social Profile -Intellect Level -Comptence Level -Hierarchy Level (confidentiality) Fig. 2: User dimension: actor in the organization.

In the library environment, the decision-maker knowledge allows us to know other elements to implement the portal, such as the questions of homogeneity in the communication, the respect for the graphics standards and the integration of the portal in the public reading within the portal of the CG57. In this information search process, it is fundamental to establish a profile of the user, to treat the information and establish the representation of documents to users (Fig. 2).

There is a big variety of users. Every user is going to express different and very specific needs. It is important to know the level of knowledge and the preferences of user. The needs of the users are often expressed in natural language. The difficulty lives in its transformation (ie. processing) in request.

In this case, we can observe the recommendation models and collaborative filtering to get the profile and user preferences in the system. The users' information will feed the user database.

To acquire this information, we can use three methods:

-Explicit model: it consists in asking explicitly the user for his preferences. It is the simplest method; -Implicit model: it consists in observing the user activities in search situation (to find his information tracks); -Mixed model: it consists to combine static and dynamic methods about user activities and profile. In library infrastructures with feasibility reasons, we shall privilege the explicit model for the information user acquisition as well as library professionals are listening to their public.

Information dimension

In the flow of continuous information, it is necessary to establish a methodology of information search, by taking into account the stratification of the information: primary resources (documents), the secondary resources (bibliographic records) and tertiary resources (user annotations, social tags...).

In a consideration of contents supply, we can ask the question about: "what information we want to make available to the user?", to determine the types and information sources collected or made.

Within the framework of multimedia information system such as in the library portal, the information types should be diverse: text, photos, videos, (musical) audio extracts... and resources according to their relevance in Google books, wikipedia, lastfm, BnF, Gallica, etc. In primary information, we can consider resources in the open databases, open archives, Internet. At this level, tools proposed by other poles of public reading reflect the features about the use, the relevance of the services and tools' ergonomy proposed to the user. With the progress of the information contents of towards the Web 2.0 tools (like as multimedia tools): blogs, flow RSS, podcast, webradios, netvibes, digitalizations (Gallica resources). These sources allow to complete the information according to the user needs (Fig. 3).

Available

Knowledge dimension

To be able to establish footbridges and comparative degrees between the existing information and the one that we wish to propose, we set up a watch process. The watch process consists in giving the pertinent information to the target user, at the right time, to make the good decision.

"What information? to whom? and how?". From these questions, we can establish a projection between the exploitable information and the nature of the knowledge proposed to the user. This projection is based on the treatment and the analysis of the information through the watch process.

In front of the increasing mass of information, the libraries need systems of assistance to analyse more and more successful. These tools have to offer possibilities of very fine exploration and synthetic representation of the meditative information and the new generated knowledge observed in the processing. So, watch tools did not stop perfecting to allow a follow-up reasoned by this strategic information flow, by automating partially the process. The contribution of the watch actors will also be integrated into this process.

Intellect Effort (Human) The information corresponds to the human interpretation on the raw data. The information arises from resources grouping. About primary information, it produced by the authors, secondary information (or bibliographical notes) produced by the information officers and the professionals of libraries, and tertiary information (annotations, tags and votes of users) produced by every user who lands in system interface (Fig. 4).

The knowledge is information crossings by process, interpretation by user in the information models which give meaning and makes it operational (meaningful in the system).

In the system implementation context, we attempted to define how user can access to product resources, how this information is transformed into knowledge, how user access to knowledge and how he enriches the contents: it is about the re-indexing contents by user activities [START_REF] Harbaoui | Enrichissement des contenus par la réindexation des usagers: un état de l'art sur la problématique[END_REF].

The user became an author. He can, by acquainting with the document, bring it additional and complementary information. As annotator, this new actor develops informative contents by integrating "new information, links, and interpretative elements on the document" [START_REF] Sidhom | Approche conceptuelle par un processus d'annotation pour la représentation et la valorisation de contenus informationnels en Intelligence économique (IE)[END_REF].

The annotation indicates at the same time the activity of the additional new information on a document and the result of the annotator action. In a common way, a note (by annotation) is a brief comment, an explanation, etc. about the document or about its content [START_REF] Kislin | Modélisation du problème informationnel du veilleur dans la démarche d'Intelligence Economique[END_REF].

From a general point of view, notes are well appreciated by readers. A comment, an appreciation or a critic is going to call to the future reader, who via these notes is going to find an interest for the annotated document, is going to want to build up to himself its own opinion (notice).

This library 2.0 portal is in progress in a collaborative way between the agents of the DLPB, the mosellans librarians, the users via forums or comment zones, and must be daily updated. These new "places" of exchange and sharing have for purpose: "to support cooperation activities in more structured way, in which the interactions also lean on information or documents, shared by a collective pursuing with common objectives" [START_REF] Zacklad | Processus de documentation dans les Documents pour l'Action (DopA) : statut des annotations et technologies de la coopération associées[END_REF].

It exists various relations between the transmitter (Library professionals) and the receiver (Library users):

-Common project in the main objectives or in the partially shared interests; -Social relationships between the professional actor and the beneficiary user to ensue partly the interests or the common objectives. However, filtering techniques on information must be organized to verify the coherence and the relevance of certain information additions. The enrichment of information databases by users brings an added value to the information system as far as this one is relevant. The library 2.0 information system has to take into account the information about the environment in which it is going to be used and which will be in its objectives.

In next, the objective is going to involve in the model proposed to the decision-making problem and so to our problem in the Economic Intelligence (EI) process.

III. RESULTS

More than a simple Website of contents, the library 2.0 portal includes at least application resources and complementary services. It has a double mission of resource aggregator and dynamic menu. As aggregator of resources, it combines in the same frame of the information stemming from diverse and heterogeneous systems. As dynamic menu, it presents a state of the available resources and offer of the links towards information. Others proposed services, the appropriate added value in the portal is the selection and the meeting, from a certain number of tools and resources: contents editorial, search engine, diverse information, news, classification of Websites by themes, e-mail, forums, etc. Also, the portal integrates a dimension of customization and identification of the Internet users according to their various objectives. So, it allows the Internet users to define a personal space, to place selections, requests, alerts which they wish to define, etc.

The objective of this departmental portal of reading is to propose a big panel of services from a single address. The portal has to enter the participative Web (or Web 2.0) and so turn to the major principles of the library 2.0. With the real strategy of services 2.0, it is important to locate as development vector of the new technologies within libraries, and to associate it all the participants of the departmental network. The librarians and the Internet users, in Web 2.0 with IT infrastructures, can add opinions (notices) on a book, a movie record next, comments or to import extern source on it.

For resources, every library 2.0 user can index by proposing new tags or keywords besides those existing. It is a question of enriching the possibilities about discovering books, records and videos... [START_REF] Desmottes | S'approprier les outils de l'information et de la communication[END_REF]. The notes on books, records and videos will integrate the library information system. Finally, the portal allows an access to the common "catalogue" (library user interface) of all libraries in Internet network: in autonomous or guided way, user navigates via tags, themes' lists, books' categories, videos' collections… (Fig. 5).

Application dimension

The departmental public portal of reading of the Moselle should be born during the 1st half-year 2011. This very ambitious project was launched more than a year ago through the constitution of a workgroup which has for mission to work on a reflection relative to the use of this portal (Fig. 6). Several points will already be to respect, at the level of the Graphics standards in particular. It is important to have viewed well the objectives, the missions and the constraints of the portal to estimate the work to be led for its realization and for its functioning (update, maintenance).

Portal for public reading in Moselle

It will be a question of proposing the most relevant information by the actions of users as the library actors. The user is in the target of this portal. The portal is a shop window of the politics of the Department in public reading. It will be the interface between the professionals of the reading, the libraries and the Internet users.

IV. CONCLUSION AND PROSPECT

Today, the roles were inverted. The reader does not come to look any more for the information, it is the information which has to come to him. The library infrastructure is not any more the place of information access. The user does not want to have any more customer's simple role, passive way. He wants to be a contributor, an actor, to express his opinion, to participate actively in the library life. Also, today, the information is exchanged and shared by Internet networks. The library, via its Website or its portal has to allow the user to reach its resources from his home (or connected WIFI place).

The interaction with the library and user needs is in the target of this library information systems. The library has to renew its services, see again (and revise) its missions. Without this objective, the Division of the Public Reading and the Library politics could see it in restriction. The proposed departmental portal is going to bring by the 1st half-year 2011 these new services: this new vision of librarian's job which turns to the tools of Web 2.0 to attract new public, to develop new adapted services (Fig. 7). It is necessary in a short time to progress from BDP (in French: "Bibliothèque Départementale de Prêt") as "traditional" type of library, whitch its main function was the contribution on collections and the document services, to a BDP as "modern" type, which the digital technology and where the e-resources (multimedia documents) contribution are the main activity with user and professional.

Fig. 1 :

 1 Fig. 1: Representation of dimensions: User, Information and Knowledge [4].

  Abstracts, documents, Net/DB resources, etc.

Fig. 3 :

 3 Fig. 3: Information dimension: information resources in the organization.

Fig. 4 :

 4 Fig. 4: Knowledge dimension: conceptual entities in processing.

Fig. 5 :

 5 Fig. 5: "Departmental portal of public reading" architecture.

Fig. 6 :

 6 Fig. 6: Application plan of the departmental portal of reading.

Fig. 7 :

 7 Fig. 7: The new departmental portal of reading.

V. BIBLIOGRAPHY