Marta Barbero
email: marta.barbero@unito.it

Inés M Gómez-Chacón

ANALYSING REGRESSIVE REASONING AT UNIVERSITY LEVEL

Keywords: Teaching and learning of specific topics at university level mathematics, Teaching and learning of logic, reasoning and proof, Regressive reasoning, Logic of Inquiry, Strategy games

This paper focuses on the epistemic and cognitive characterization of regressive reasoning in resolving strategic games. It explores the use of the Finer Logic of Inquiry Model as a tool for the analysis of the regressive reasoning. It reports the results of a study carried out on 32 undergraduate students who are studying a Mathematics Degree in a university of Spain.

INTRODUCTION

The method of analysis has proved to be extremely stimulating in various fields, and has played a crucial role in the emergence of the modern world-view. The combination of the two branches of analysis and synthesis has been applied to several fields of artificial intelligence, theoretical computer science, and in programming methodology [START_REF] Peckhaus | Regressive Analysis[END_REF][START_REF] Grosholz | The Growth of Mathematical Knowledge[END_REF]. For many engineering students and mathematics undergraduate students, learning the method of analysis in tertiary education mathematics is a critical issue. They have the challenge of incorporating it in different disciplines related to the design and production of products and services, such as, Project Management, Systems Engineering and Design Science. They have no theoretical and methodical basis [START_REF] Koskela | The proto-theory of design: the method of analysis of ancient geometers[END_REF]. A conscious integration of regressive reasoning in mathematics university learning raises the need for articulation between epistemological and cognitive aspects. Regressive reasoning is not completely logically determined, but has elements of contingency, creativity and intuition. The purpose of this text is to highlight the potential of Finer Logic of Inquiry Model [START_REF] Arzarello | Notes for a seminar "A new structural approach to argumentation in mathematics: from Toulmin model to Hintikka Logic of Inquiry[END_REF]) as a tool for the didactical analysis of the regressive reasoning. This model has been used at secondary level education, not being used at tertiary level so far.

Here we will report the results of a study carried out on 32 undergraduate students studying a Mathematics Degree at a Spanish University, using strategy games in order to promote the regressive reasoning. The choice of strategy games is justified by antecedents to this study in which they have been shown to be a key tool for teaching problem solving and regressive reasoning [START_REF] Gómez-Chacón | Los juegos de estrategias en el curriculum de matemáticas[END_REF].

The present research is primarily exploratory for two reasons: 1) Regressive reasoning has been scantly analysed in mathematics and educational psychology; 2) the use of the Finer Logic of Inquiry Model methodology to analyse data from mathematical thought at tertiary education is a new development. The theoretical background and empirical studies related to regressive reasoning needs to be developed.

REGRESSIVE REASONING

In mathematics, progressive reasoning alone is not exhaustive to fulfil the tasks of solving problems. Great mathematicians like Pappus, Descartes, Leibniz, in their discussions about analysis and synthesis, emphasize this fact [START_REF] Peckhaus | Regressive Analysis[END_REF]. Regressive reasoning is known by different denominations: regressive analysis, backward solution, method of analysis, etc. This process includes different ways of proceeding in problem solving: backward strategy, strategy of assuming the problem solved, Reductio ad Absurdum, beginning at the end of the problem, etc.

Pappus was the mathematician who has contributed substantially to the clarification and exemplification of the method. In the seventh book of his Collection he deals with the topic of Heuristics (methods to solve the problems).Where he exemplifies the method of analysis as the method of synthesis, therefore making the development of this reasoning clearer. Pappus defines the method of analysis as follows: "In analysis, we start from what is required, we take it for granted; and we draw correspondence (ακολουθον) from it and correspondence from the correspondence, till we reach a point that we can use as a starting point in synthesis. That is to say, in analysis we assume what is sought as already found (what we have to prove as true)." (elaboration by Polya, 1965 andby Hintikka and[START_REF] Hintikka | The Method of Analysis: Its Geometrical Origin and Its General Significance[END_REF]. Subsequently he points out: "This procedure we call analysis, or solution backward, or regressive reasoning." [START_REF] Hintikka | The Method of Analysis: Its Geometrical Origin and Its General Significance[END_REF] And on the Method of Synthesis: "In synthesis, on the other hand, we suppose that which was reached last in analysis to be already done, and arranging in their natural order as consequents the former antecedents and linking them one with another, we in the end arrive at the construction of the thing sought. This procedure we call synthesis, or constructive solution, or progressive reasoning." [START_REF] Hintikka | The Method of Analysis: Its Geometrical Origin and Its General Significance[END_REF] In summary, the following was considered backward reasoning: the practice that involves the making of a number of arguments from the bottom of the problem and proceeds through logical correspondences which allow to obtain something known or to be reached through other paths. The analytical method consists of a procedure that starts with the formulation of the problem and ends with the determination of the conditions for its solution.

FINER LOGIC OF INQUIRY MODEL (FLIM)

Trying to overcome the static approach of habitual logical mathematical reasoning, [START_REF] Hintikka | The Principles of mathematics revisited[END_REF][START_REF] Hintikka | Inquiry as Inquiry: A logic of Scientific Discovery[END_REF] developed what he calls Logic of Inquiry. The idea, already elaborated by ancient Greek philosophers, is building knowledge through a questioning process, implicit or explicit. The knowledge is the result of research generated by a specific question. The philosopher introduces it as the "logic of question and answer".

In his approach he considers Game Theory and game semantics to support formal epistemic logic. Hintikka overcomes the limitations and excessive abstractions of Tarski's Definitions of Truth [START_REF] Sher | What is Tarski's Theory of Truth?[END_REF], which leave the process used to reach the truth unexplained. He introduces a top-down definition of truth [START_REF] Hintikka | The Games of Logic and the Games of Inquiry[END_REF] unlike the classical and tarskian bottom-up view, highlighting the regressive way of proceeding in problem solving from an epistemological point of view. [START_REF] Hintikka | The Games of Logic and the Games of Inquiry[END_REF] retakes the idea of Wittgenstein's language games and some aspects of Game Theory, elaborating on a theory where the centre is "a path towards the formulation of a truth that, instead of proceeding recursively from atomic to complex formulas, reverses the approach and proceeds from the more complex ones to their simplest constituents". In this research, the study of games will try to explain this interlacing between game theory and strategic rules that allows the student to win.

The FLIM elaborated on by [START_REF] Arzarello | Notes for a seminar "A new structural approach to argumentation in mathematics: from Toulmin model to Hintikka Logic of Inquiry[END_REF] sought to propose a concretion of Hintikka`s proposal to be used in the Didactics of Mathematics. More specifically, he explained the elements needed to analyse the interactions between strategic and deductive components of students' resolution protocols. This model allows for the structuring of the resolution in two components: Inquiry Component (IC) and Deductive Component (DC).

In the Inquiry Component the subject alternates a series of questions, answers and explorations, according to Hintikka's Logic of Inquiry. Its purpose is to meet the aim of the problem, solving conjectures that gradually rise from results of two explorations:

• Exploration: in order to analyse and understand the situation in which the subject is involved

• Control: in order to verify the ideas or conjectures that came out during the development of the activity.

In the above Component, the cognitive dimension of reasoning is necessary. From a cognitive point of view, the progressive-regressive reasoning movement has been highlighted by studies such as those of [START_REF] Saada-Robert | La microgénèse de la representation d'un problem[END_REF]. The psychological model for solving mathematical problems focuses on the distinction between two phases of the resolution: investigate why things are like this (backwards, until reach a plausible hypothesis -abduction-or a known fact) and verify this investigation (forward, codified by the classical logic). Based on Saada-Robert's model, [START_REF] Arzarello | Notes for a seminar "A new structural approach to argumentation in mathematics: from Toulmin model to Hintikka Logic of Inquiry[END_REF] and [START_REF] Soldano | Learning with the logic of inquiry. A game-approach within DGE to improve geometric thinking[END_REF] characterized this cognitive dimension through the sequence of actions in three different modalities: ascending, neutral and descending.

Ascending modality (A) refers to the path towards the formation of ideas and conjectures after a phase of exploration. Descending modality (D) characterizes the transition from a conjecture to an investigation. The purpose of descending modality is to find an equivalence between the object of thought (the conjecture, the idea) and the object of work (the problem and its resolution). Neutral modality (N) marks the change between the ascendant and the descendent; it is the moment in which a conjecture is formulated. Observable actions in the subjects are: formulations (of questions, of resolutions plan, of conjectures), affirmations, explorations and controls.

In the Deductive Component the subject is not directly involved in the investigation and verification of conjectures and uses a language with a logical nature to formally formulate the truth. Three specific modalities are added: detached modality, logical control and deductive modality [START_REF] Arzarello | Notes for a seminar "A new structural approach to argumentation in mathematics: from Toulmin model to Hintikka Logic of Inquiry[END_REF][START_REF] Soldano | Learning with the logic of inquiry. A game-approach within DGE to improve geometric thinking[END_REF]. Detached modality is the moment in which a conjecture, which has not arisen immediately after an exploration, is formulated. Logical control is the time when an exploration-control is done without using instruments. It is characterised by the use of formal language.

Deductive modality characterises control phases where instruments are involved. Deductive Steps and Logical Chains are added to the Inquiry Component actions.

Inquiry and Deductive components are not often well differentiated during problem resolution where the subject passes from one component to another, even more than once. We can say that the typical components structure is nested in this way: (IC ~ (DC ~ (IC ...))) with "~" that expresses the passage from one component to the other. Table 1 summarizes some observable actions and their modalities according to the definitions given and that will be considered in the analysis.

Observable actions Modalities

AIM AND METHODOLOGY

Aim

The aim of this paper is to show an evaluation tool for examining how regressive reasoning develops in university students. In particular, how the FLIM can be a valid tool to analyse the interplay between cognitive and epistemic in the regressive reasoning.

Participants and instrument

Data were collected in 2014 from 32 (19 women and 13 men, aged between 21 and 23) Caucasian undergraduates working toward a BSc. in mathematics. All of the participants were in their last year of academic studies. They were following advanced courses in several areas of geometry, algebra, probability and analysis. With regard to solving problems, the students had been introduced to the problem solving heuristics. They had not received any special training about backtracking heuristics.

The work dynamic started with individuals being given paper and pencil with which they need to resolve two games, each lasting one and a half hours. Figure 1 shows the problem which we will analyse in the results section. Strategy games allow for the natural development of regressive reasoning. These games are disconnected from the mathematical content which forces the student to use their mathematical knowledge acquired in their university degree.

The Triangular Solitaire (Gómez-Chacón, 1992) is a game for a single person that requires a board with 15 boxes as the figure shows.

These are the rules:

1. Place the pegs in all boxes, except in the one marked in black.

The player can move as many pegs as they like as long as they are able to jump over an adjacent peg and onto an empty space (along the line)

. At the same time, he "eats" the peg that was jumped over and that peg gets taken out of the game. All pegs move in the same way. Pegs can move around the table in any direction.

Objective: The player wins when there is only one peg on the table.

Figure 1

Students were given the game and asked to describe their approaches to solving the problem on protocols including: thought processes in the resolution, explanations of the difficulties they might face, and strategies they would use in order to solve with paper and pencil. A qualitative analysis was chosen to examine the resolution protocols of the students through the "Finer Logic of Inquiry Model" [START_REF] Arzarello | Notes for a seminar "A new structural approach to argumentation in mathematics: from Toulmin model to Hintikka Logic of Inquiry[END_REF].

A general analysis of 32 students took place before a case study was carried out. In this paper we describe an individual student case in order to show a deep understanding of the tendencies of the behaviour related to the sequences of actions and movement between modalities of reasoning. The protocols analysis, at a macroscopic level of this case, provides the identification of reasoning difficulties and way of using backward reasoning that determined success or failure in the resolution. It's worth noting that Student M (see section 5) is a key informant of the group because he belongs to 60% of students that use the backward strategy and incorporates graphical representations to achieve the transition between modalities.

REGRESSIVE REASONING USE (CASE STUDY)

Regressive The steps I'll take are: 14-9-5; 7-8-9; 12-13-14; 2-4-7; 11-7-4; 10-9-8; 3-6-10. 6

At this point, I note that the only way to eliminate 1 would be to move 8-5-3.

7

Here I notice that [with these movements] the game cannot be solved because the 4 cannot be eliminated and the remaining pegs cannot eliminate each other.

Figure 4 8 I realise that I can try to go backwards, that is, starting with just one peg in one position and undo the jumps trying to fill the table with the exception of a hole.

Looking at the board, I think that maybe the fact that the last piece stays on the board (the peg from which I start to move backwards), in a position that you can come up with many jumps, facilitates the strategy. These places are positions 4, 6 and 13 because you can get to them with 4 jumps.

10 To fill up the game table I will have to do 13 moves, because there are 15 holes, an initial peg and an empty final hole. 11 Let's start only with peg 13.

Figure 5 12 Here I already notice that I do not reach the solution because I will never fill the top corner due to the absence of a peg in the 3rd row; I should do 11-7-4 leaving corner 11 without a peg [so that the top corner will be filled]. 13 Let's start with the reason for the various steps:

13-14-15: I want to start filling the corners as soon as possible because these holes are the hardest to fill up (the peg is in hole 15 and I will not move it anymore). 14-13-12: Random movement. 12-8-5: I want to leave hole 12 free to get to the next step at corner 11. 8-9-10: I want to leave hole 8 free to retrieve peg 12 (to fill 13 and 14) in the next step, so I can complete it later [the row]. 12-13-14: I want to complete the row below. 5-8-12: I want to complete the row below. 14 I think trying to fill the centre was not a good strategy... 15 … so now I'm going to try to fill the outside of the triangle, that is, [I'll try to] undo the jumps to the corners and sides. (Playing normally would involve jumping to the centre avoiding corners and sides if possible.). 16 I also get stuck [on the fact] that by eating pegs or undoing the jumps, the movements that are made are triangular. So I will try to fill the smaller triangles contained in the big triangle. The following table shows actions and cognitive modalities associated with each protocol line and figure; a check ( ) indicates the lines where the regressive reasoning is used. The last column of the table shows different strategies involved. This cognitive analysis shows that the first two resolution phases are characterised by a continuous alternation of explorations and plan formulations together with an alternation of descending and ascending modalities. The second resolution phase involves the continuous use of the going backward strategy. Subdivision of the board into rows and then into triangles is fundamental to reach the solution. Student-M modifies the strategy slightly by adding new elements in the resolution (board subdivision into rows and triangles) typical of problem solving using regressive reasoning. Crucial points of backward reasoning are reached in the ascending modality (see in Table 2) where ideations occur. A routine that can be established regarding the use of modalities is A~N~D~(A~N~D~(A~…)). The neutral modality marks the transition between A and D and it is characterised by the incorporation of auxiliary constructions as generating tools of new knowledge (epistemic transaction).

In the third phase of the resolution, by writing and graphically representing the steps taken to reach the solution, Student-M (in detached modality) checks the result obtained by going backwards.

CONCLUSION

Analysis with the FLIM model allows to model student's cognitive movement in a logical concatenated way. The strategic aspects are more dominant in the ascending and descending modality, while the epistemic ones are prevailing in the neutral modality. Our study confirms results obtained by Soldano (2017) (with upper secondary school students in geometry): the ascending modality characterises the backward way of thinking, while descending is the cognitive modality that characterises the progressive way of reasoning. However, most likely, abductive reasoning has been used in the formulation of conjectures in ascending modality, but we cannot be sure of it by only analysing the protocol, we need to complete this information by interview. This is an open question for further research.

At a phenomenological level, this method allows us to analyse the development of strategic aspects within the cognitive modality movement to reach the solution. But it mainly focuses on cognitive modalities while it doesn't distinguish between the strategic principles that are used. Through this tool it's possible to emphasise that regressive reasoning involves auxiliary intuition elements that are necessary to achieve the solution; these aspects are developed by looking at the consequence and looking for the premises. A larger sample size with two different tasks, find the winning strategy and mathematically solving the game, would allow us to advance in the development of the tools for evaluating regressive reasoning.

  reasoning use varies among the group of students. Let us examine a case study. A student (Student-M) has combined regressive reasoning with different strategies and auxiliary constructions: drawings, graphical representations. Student-M indicates difficulties in creating the solution because of the actions which are needed for discovering the solution and because of the recognition of representational equivalences. The visualisation and representations which are used help during the resolution process; Student-M performs continuous control over its own resolution process. She is able to slightly modify the strategy or even change it completely to reach the solution. For analysis purposes, Student-M's protocol has been divided into the following phases: familiarisation, exploring and carrying out the strategy, results verification. According to the Finer Logic of Inquiry Model, this student's protocol is mainly characterised by the inquiry component. This begins with the first part of the protocol, corresponding to the familiarization phase. The entire protocol has been translated highlighting the parts where student M uses backward reasoning (in Italics).Student M protocol 1To accomplish the exercise, I'm going to number the holes on the board in order to leave a trace of the movements I'm doing. At the beginning, all the holes are filled except number 5.

  you can only start with two movements 14-9-5 or 12-8-5. 3 Since this is an equilateral triangle, I think it does not matter what the starting movement is because they should lead to "symmetrical" solutions. 4 I'll start to do it roughly. 5

Figure 6 17

 6 First, I will fill the lower right triangle. 18 Now I'm going to fill the upper triangle; to do so (Since i do not want to remove the peg I placed in position 1), I have to get some pegs in the 4th row that, undoing the jump fills the 2nd and 3rd row. I undo the jump with the 9. 19 Now you have to fill the lower left triangle.

Figure 7 Figure 8

 78 Figure 7

  Supported by NILS Program (ES07) (007-ABEL-IMI-2013), Project "Mathematical Working Space" (UCM-CmdeGuzman-2015-01), University of Torino doctoral scholarship and by special action IMI-INVEDUMAT_uni.


Laure Berti-Équille
email: laure.berti-equille@irisa.fr

Recommendation of XML Documents exploiting Quality Metadata and Views

In this paper, we propose to query XML documents with a qualitybased recommendation of the results. The document quality is modeled as a set of (criterion, value) pairs collected in metadata sets, and are associated with the indexed XML documents. We implemented four basic operations to achieve quality recommendation: 1) annotation with metadata describing the documents quality, 2) indexing the documents, 3) matching queries and quality requirements, and 4) viewing the recommended parts of the documents. The quality requirements of each user are kept as individual quality profiles (called XPS files). Every XML document in the document database refers to a quality style sheets (called XQS files) which allow the specification of several matching strategies with rules that associate parts (sub-trees) of XML documents to user profile quality requirements. An algorithm is described for evaluation of the quality style sheets and user profiles in order to build an "adaptive quality view" of the retrieved XML document. The paper describes the general architecture of our quality-based recommender system for XML documents.

Introduction

Finding relevant, high-quality information in the World Wide Web or even in a collection of semi-structured documents is a difficult task. Information quality has no consensual definition and its evaluation requires: i) the measurement, ii) the weighted combination of both objective and subjective quality criteria and iii) the matching between the relative perception of information quality and the users' profile in terms of quality requirements. The problem of high-quality information retrieval is becoming prevalent and not easy to solve today because of the growing, massive and quality-heterogeneous collections of documents now available on-line and also, because information quality in this context is very relative (depending on a topic, on a group of users, on a time period or on a focus of interest). As an introductory example, in intelligence gathering efforts for homeland security, information is collected from various sources with different degrees of trust and quality and then is corroborated (or not) by collaborating experts who need an automatic means to determine accurate and trustworthy information for decision making.

Content-based and collaborative recommender systems [START_REF] Resnick | Recommender systems[END_REF] work by automatically recognizing, tallying and redistributing recommendations of the web resources. The multi-confirmed recommendations appear to be significant resources for the relevant community and finally the number of distinct recommenders of a resource is a plausible measure of resource quality. But, many collaborative recommender systems particularly ratings-based systems are built on the assumption of role uniformity: they expect all users to do the same types of work in return for the same type of benefits. And the notion of user satisfaction and the evaluation task (rating) are very relative and should be considered in a flexible and adaptive way. Our approach consists in taking into account the information quality evaluations and requirements in the context of collaborative annotation of XML documents for quality-driven information retrieval (IR) and information filtering (IF). We propose a modeling of document quality with various objective and subjective quality criteria. The objective criteria can be quantitative and calculated by statistical methods. The subjective criteria are defined and evaluated by a group of reviewers (or curators) collaborating on the qualitative annotation of the documents. The selection of the documents is both content-based and quality-based (i.e., depending on the content and structure of the documents relevant to the query but also depending on both objective and subjective quality aspects required by the user). Our objective is to propose a multi-criteria adaptive recommendation of XML documents, and to refine the traditional selection of documents by exploiting embedded or linked metadata describing the resource quality. From these specifications, we developed the XDARE system (XML-Documents Annotation and Recommendation Environment) for qualitydriven annotation and recommendation of XML documents.

Motivation

Among these various research propositions concerning on one hand, information quality, and on the other hand, recommender, blocking and adaptive hypermedia systems, we came to the point that there is no proposition in the literature nor system that combines resource quality for alternatively recommending/blocking and adapting digital resources on demand (in a way driven by users' profiles). Actually, in the current research works in IF or CF, the quality of the content is not considered as a key element for the users' decision in the recommendation process and we think this problem is not yet sufficiently addressed by existing approaches. Our motivation was to propose the three services (i.e., blocking/recommending and adapting information) able to take into account in a flexible way the quality dimensions of the queried documents. Two principles guided our approach: -in order to improve quality of a search result, it is necessary to evaluate the quality of the retrieved documents and to exploit it for the query processing, -it's necessary that the definition of document quality remains flexible. The use of quality labels and metadata allow this flexibility for both specification and interrogation.

Compared to existing approaches for collaborative or content-based recommender systems, the innovative aspect of our approach is to include constraints and requisites on content quality that is complementary for better recommendation services.

Outline

The rest of the paper is organized as follows: section 2 presents the previous works on information quality and adaptive hypermedia and recommender systems. Section 3 describes our quality metadata model and presents the recommendation process for XML documents. Section 4 describes the architecture of our system. Lastly, Section 5 concludes the paper and presents our perspectives of research and development.

Related Works

Metadata and Data Quality

In the context of distributed information environments, metadata harvesting refers to the automatic collection of descriptive information from distributed resources. Recently, one particular way of accomplishing this collection of distributed metadata has been the subject of considerable attention in museums, archives and e-learning communities (e.g., the metadata collection proposed by the Open Archives Initiative Metadata Harvesting Protocol (OAI-MHP) [START_REF]The Open Archives Initiative Protocol for Metadata Harvesting[END_REF]). In the domains of geographical information systems [START_REF] Goodchild | Data quality in geographic information : from error to uncertainty[END_REF] also produced a detailed list of quality standards to be used for the selection of the Web resources with various categories of quality criteria: 1) criteria related on the policy of diffusion and the range of the resource, 2) criteria related to the content, 3) criteria related to the form, 4) criteria related to the management of the documentation quality.

In the context of the TIPS European project [TIPS99], several services have been developed related to the reuse of evaluations performed by humans on scientific publications. The first one, called QCT (Quality Control Tools) aims at collecting human detailed evaluations of documents in order to enrich the traditional topical indexing of documents with quality-related information (see Table 1 for the quality features used in the QCT-TIPS project for document quality). The second one, called SF (Social Filtering) integrates push functionalities as the alternate and complementary tool to traditional pull services such as information retrieval. Documents are pushed to users with respect to the evaluations they have made in the past, and compared to other users' evaluations.

Adaptive Hypermedia and Recommender Systems

A number of adaptive hypermedia systems have appeared as impersonalized systems, recommender systems with a common goal: to learn about the implicit preferences of individual users and to use this information to serve the entire community of these users better. The early recommender systems mainly used Information Filtering (IF) techniques and individual previous behavior to produce recommendations. To cope with the main drawback of IF techniques, Collaborative Filtering (CF) techniques have been proposed in order to recommend items based on the opinion (rating) of other users who have similar tastes. GroupLens [START_REF] Resnick | GroupLens: An Open Architecture for Collaborative Filtering of Netnews[END_REF] is a server-side recommendation engine for Usenet news. A user's profile is created by recording the user's explicit ratings of various articles. Automatic collaborative filtering is used to statistically compare one user's likes and dislikes with another user and to recommend articles from other similar users' profiles. Various recommender systems have been created to assist users for selecting potentially interesting information and for filtering out what users may not be interested in (such as PHOAKS [START_REF] Terveen | PHOAKS: A System for Sharing Recommendations[END_REF] for recommendation of Web resources mined from Usenet news messages, or Ringo [START_REF] Shardanand | Social Information Filtering: Algorithms for Automating "Word of Mouth[END_REF], a music recommender system). But two major limits of the CF-based techniques are:

the "early-rater" problem occurring for the first rating of documents without benefit of other previous recommendations, the "sparsity rating" problem occurring when the overlap between user's ratings (or number of co-rated items) is small or null and as a consequence that the recommendation results may be not accurate or cannot be produced. The next level of recommender system is hybrid systems combining IF and CF techniques, such as MovieLens [GS+99], a movie recommender system using filterbots (IF agents) as rating robots which participate as members of the CF system. Both Personal Web Watcher [START_REF] Mladenic | Personal WebWatcher: Implementation and Design[END_REF] are content-based systems that recommend web-page hyperlinks by comparing them with a history of previous pages visited by the user. Personal Web Watcher [START_REF] Mladenic | Personal WebWatcher: Implementation and Design[END_REF] uses an offline period to generate a bag of words style profile for each of the pages visited during the previous browsing session. Hyperlinks on new pages can then be compared to this profile and graded accordingly. Most of the current hybrid systems still use co-rated items among users in finding correlated neighbors for an active user, and co-rated items between user and filterbot to find agreed filterbots.

On the opposite, PICS (Platform for Internet Content Selection) [START_REF] Resnick | PICS: Internet access controls without censorship[END_REF] is an infrastructure which associates labels to the contents of the documents available on the Internet in order to block the access for non-authorized users. Originally conceived to help the parents and the professors to control the navigation of their children/pupils on the Internet, it makes it possible to affect any criterion on the labels which the system interprets to authorize or block the access to the documents. Complementary to the recommendation, the advantage of this approach is that the structure of a document can be enriched by adding labels which define the conditions of viewability (blocking or full access). Another aspect of information personalization is to adapt web content to users' preferences and also to the variations of the client environment, so that web pages can be prepared suitable for the client. Adaptive hypermedia systems [START_REF]Adaptive Hypermedia and Adaptive Web-Based Systems[END_REF] can learn about the implicit and explicit preferences of individual users and using this information to personalize information retrieval processes. In this context many adaptive hypermedia systems have been proposed, such as OnlineAnywhere 1 , SpyGlass 2 , FastLane3 , QuickWeb4 , ProxyNet5 , Digestor [START_REF] Bickmore | Digestor: Device-independent access to the World Wide Web[END_REF], TranSend [START_REF] Fox | Adapting to Network and Client Variation Using Infrastructural Proxies: Lessons and Perspectives[END_REF], and Mobiware [START_REF] Angin | The Mobiware Toolkit: Programmable support for adaptive mobile networking[END_REF]. However, most of them only make adaptation of the web content under special conditions due to the lack of structural information of HTML content, and many of them focus on image conversion. a comment (comment). A consensus can be calculated for a given criterion and a date if several notations have been proposed by several reviewers.

Figure 1. Metadata XML Schema Representation

Example 1. Figure 2 gives an example of quality metadata instances that can be associated to a document with both subjective criteria (originality, credibility) and objective criteria computed by specific programs (citing popularity, reading popularity). In this example, the notations are values in [0,10] ; the originality and the credibility of the document are evaluated by the reviewer A1 and the citing and reading popularity computed by programs similar to the one used in CiteSeer (http://citeseer.nj.nec.com/). <metadataset qid="q1" type="ContentMD" scheme=" " sortkey=" " index="noindex" show="noshow" dIdref="dId"> <metadata mid="m1

Annotation and Recommendation Processing

Collaborative annotation and recommendation of the documents can be decomposed into four main operations (Figure 3): 1) harvesting and generating the quality metadata (annotating), 2) indexing the documents and their meta-descriptions (indexing), 3) matching the query (including the quality requirements of the users) with the representation of the indexed documents (matching), and 4) scoring and viewing the documents (viewing) with different recommendation strategies. Each step constitutes an elementary operation on the XML document collection. First, at the annotation step, each document of the collection is enriched by several quantitative and qualitative quality metadata (respectively, objective measures and subjective evaluations of the document quality) such as, in the example given in Figure 2. Metadata, document contents and structures are then used for the indexing process in order to represent each document as a multidimensional vector on these three axes: quality (criteria), content (terms) and structure (XML elements, attributes). For the query processing, the system finds the documents that best match the query terms and it applies to their respective metadata:

the quality rules specified into the so-called XQS file (XML Quality Style sheet) the quality requirements specified into the so-called XPS file (XML Profile Style sheet) that correspond to the quality profile of the user (or of the group of users). At this stage, the system has to use the recommendation strategy defined into the XQS file, to verify and to adapt (i.e., soften) the quality constraints (given in the XPS profile) for building the quality-driven view of the query result.

Definition 1. XML Quality Style Sheet (XQS)

A quality style sheet (XQS) is an XML file used for the query processing. It references the profile to use as an attribute and is composed of the rules to apply for each document node. The DTD of a quality style sheet is given in Figure 4. . DTD for XQS sheet Each rule is specifically applied to one document node with a specified user profile, priority (between 1-lowest and 5-highest), access and strategy attributes. The access defines the mode with or without recommendation or blocking access. The strategy defines how the quality constraints should be checked: exact matching, approximate matching or negotiation of the quality constraints. The exact strategy means that all the constraints must be verified on the value of each quality criterion (i.e., metadata values) of the targeted document node. The approximate strategy allows the approximate and flexible matching between the constraints values and the effective quality criteria values using nearest neighbor algorithm on Euclidean distances between the multidimensional quality vectors. The negotiation strategy allows softening interactively the quality constraints in order to match the document nodes that answer the query with the best quality. The consistency of an XQS file is checked in order to avoid the definition of rules that give access (e.g. with recommendation) to the children nodes of a node whose access is forbidden (cf. infra Heuristics 2).

Example 2. Quality Style Sheet Example

In Figure 5, four rules are defined in the quality style sheet file concerning respectively three different users: the chief of the editorial board of a scientific journal, the secretary and the authors who submitted a paper for the special issue of the journal. The DTDs of the journal and the metadata set are given in Figure 7. These users will be allowed (or not) to access information according to the following rules: R1 -the secretary access all the submitted articles; R2 -the authors only access their own article; R3 -the authors are not allowed to access the papers and reviewers' comments of other authors; R4 -the editorial chief access the best submitted papers.

Definition 2. XML Profile Style Sheet (XPS)

A profile style sheet (XPS) is a file that references the user or the group of users of the profile and defines the constraints on the document quality dimensions. The set of quality constraints is defined as a quality contract.

<xqs DefaultProfileFile="profiles.xps"> <!--Rule 1 --> <rule Profile="users/secretary" DocumentNode="article[@id=$user]//node()" Priority="2" Access="default" Strategy="exact"/> <!--Rule 2 --> <rule Profile="users/*[name()='Authors']" DocumentNode=" article[@dId=$user]//node()" Priority="5" Access="default" Strategy="exact"/> <!--Rule 3 --> <rule Profile=" users/*[name()='Authors']" DocumentNode="article[@dId!=$user]/@qIdref//node() | article[@dId!=$user]//node" Priority="5" Access="blocking" Strategy="default"/> <!--Rule 4 --> <rule Profile="users/EditorialChief" DocumentNode="article//node() | article/@qIdref//node()" Priority="5" Access="recommending" Strategy="negotiated"/> <!--Rule 5 --> <rule Profile="users/secretary" DocumentNode="article/@qIdref//node()" Priority="5" Access="blocking" Strategy="exact"/> </xqs>

Figure 5. Example of XQS sheet

For a compact and simplified presentation, Figure 6 shows an extract of the XPS in the BNF-style grammar and an example corresponding to the user profile of the Editorial Chief considering the quality metadata given previously in Figure 2.

Profile Style Sheet Example

The example given in Figure 6 presents the constraints required by the editorial chief on the document collection for what concerns the originality, the credibility, the citing and reading popularity of the articles. In particular, this user is interested in docu-ments with a certain level of originality and accuracy (higher than 6 in the interval [0,10]), with citing popularity higher than 7 and reading popularity between 6 and 9. Figure 6 presents the user profile in a BNF-style grammar for simplification but, actually, the user profiles are stored in XML files using the RuleML6 DTD.

Recommending High-Quality XML Documents

The key elements of the recommendation processing are the quality-based recommendation rules (previously presented in XQS file) and the quality view that is generated according to two main heuristics we define in this section.

Definition 3. Quality-based Recommendation Rule

A quality-based recommendation rule is the following quadruplet: Rule : < Profile, DocumentNode, Access, Strategy > with:

-Profile: the path expression related to the profiles of the XPS style sheet, -DocumentNode: the Xpath expression evaluated inside the targeted XML document, -Access: the value for recommending or for blocking the access to the information items (as document nodes), -Strategy: the multi-criteria selection algorithm used to recommend the targeted node of the document. We use the following four methods for multicriteria selection defined and compared in [START_REF] Fritz | A classification and summary of software evaluation and selection methodologies[END_REF]: Weighted Linear Assignment (WLA), Elimination aspect (EA), Anderson method (AND) [START_REF] Berti-Équille | Choice models for evaluation and selection of software packages[END_REF] and Subramanian et Gershon (SG). The specification of a quality-based recommendation rule is applied to node types (element, attribute, text...). Several integrity constraints may be defined in order to maintain consistency between the rules. Instead of recommendation of entire documents based on general quality requirements, we suggest the approach of filtering XML-document nodes based on quality criteria in the personal profile of the requestor. Our approach proposes document sub-trees recommendation that is used for complete document recommendation by aggregating quality scores of every document node.

Definition 4. Quality View

A quality view of a document is the result (as the fragment(s) of the XML document) that corresponds to the query and satisfies the quality constraints and rules defined in the user's profile. Two heuristics are used in order to build the quality view of each retrieved document in conformance with the quality constraints and the recommendation strategy chosen for the user who sent the query to the system. The quality view of the XML document is built node by node (i.e., XML element by element). Heuristic 1. If the access to a node n of a document is allowed for recommendation for the user u, then u can see the recommended sub-tree of the XML document whose n is the root node if it satisfies the quality constraints defined as quality requisites into the user profile with the chosen strategy (exact or approximate or negotiated recommendation).

Heuristic 2. If the access to a node n is forbidden for the user u, then u cannot access the sub-tree of the document whose n is the root node. Quality views are then built according to:

-the quality-based recommendation rules given in the XQS file, -the user's profiles given in the XPS file. Example 4. Querying XML Documents and Quality Metadata Following the Example 2, consider the edition of a scientific journal and the associated review comments of the submitted papers. The reviewers' comments are stored as metadata files. The content of the special issue of the journal has the following DTD (Figure 7): Result 1. Secretary's quality view. This query sent by the secretary will create as a result a flat list of all the author-title pairs of the submitted paper. Her quality requirements are given in the XQS file of Figure 5. Result 2. Author's quality view. This query sent by an author will show only the title and the author name and the reviewers' comments of his own submitted article, but this author will not be allowed to see the papers and the comments of other authors.

<!ELEMENT
Result 3. Editorial chief quality view. This query sent by the Editorial Chief will show the title, the author names and the reviewers' comment of the best articles.

System Architecture

We used standard tools for implementing the quality recommendation processor of our system called XDARE (XML-Documents Annotation and Recommendation Environment). The description of the architecture is connected to the on-line processing steps showed in Figure 3 (numbered 3, 4 and 5). The prototype has been developed in Java and the following operations are implemented (Figure 9):

1 XML Document Analysis: The document file is parsed (Xerces Apache) and syntactically analyzed (SAX API) and the corresponding internal representation is created and stored. The internal representation and manipulation has been developed with DOM API: the events produced by the SAX API during the first step of the document analysis are used to build the corresponding DOM tree. The XDARE operators use the instances of this internal representation by recopy and each query produces a new tree.

2 Query and Xpath Analysis: The grammar is an extended XML Query syntax.

Jlex and Cup are here used to produce the corresponding Java syntactical analyzer including the Xpath expression analysis. The query tree is explicitly instantiated for future optimization.

X M L D o c u m e n t s

A n a ly s e r ( J le x + C u p ) X M L P a r s e r ( S a x X e r c e s ) X M L q u a lit y v ie w r e s u lt 4 Quality View Generation: When the user wants to browse a XML document, he actually obtains a view of this document in conformance with his quality requirements.

Conclusion and Perspectives

In this paper, we present a general architecture for quality-based recommendation of XML documents. The document quality is modeled as a set of (criterion, value) pairs collected in metadata sets, and are associated with XML documents. We implemented four basic operations to achieve quality recommendation: 1) annotation with metadata describing the documents quality, 2) indexing the documents, 3) matching queries and quality requirements, and 4) viewing the recommended parts of the documents. The quality requirements of each user are kept as individual quality profiles (XPS files). Every XML document in the document database refers to a quality style sheets (XQS files) which allow for specification of several matching strategies and contain matching rules relating parts (sub-trees) of XML documents to user profiles. An algorithm is described for evaluation of the quality style sheets and user profiles in order to build an "adaptive quality view" of the retrieved XML document. We sketched the architecture of the XDARE system, which implements the proposed data structures to support quality-based retrieval and adaptive quality views of XML documents. The specification and the exploitation of metadata describing the quality of documents can improve the system effectiveness for information searching and filtering including quality-driven recommendation. The innovative aspect of our work is to propose the three services (blocking/recommending and adapting information) in a flexible way and to combine content-based and quality-based recommendation with considering the quality dimensions of queried XML documents.

Figure 2 .

 2 Figure 2. Example of quality metadata linked to the document identified by "dId"

Figure 3 .

 3 Figure 3. Operations for Retrieving Recommended XML Documents

<

  Figure 4. DTD for XQS sheetEach rule is specifically applied to one document node with a specified user profile, priority (between 1-lowest and 5-highest), access and strategy attributes. The access defines the mode with or without recommendation or blocking access. The strategy defines how the quality constraints should be checked: exact matching, approximate matching or negotiation of the quality constraints. The exact strategy means that all the constraints must be verified on the value of each quality criterion (i.e., metadata values) of the targeted document node. The approximate strategy allows the approximate and flexible matching between the constraints values and the effective quality criteria values using nearest neighbor algorithm on Euclidean distances between the multidimensional quality vectors. The negotiation strategy allows softening interactively the quality constraints in order to match the document nodes that answer the query with the best quality. The consistency of an XQS file is checked in order to avoid the definition of rules that give access (e.g. with recommendation) to the children nodes of a node whose access is forbidden (cf. infra Heuristics 2).

Figure 6 .

 6 Figure 6. Extract of the XPS grammar and an example in the BNF-styleExample 3. Profile Style Sheet ExampleThe example given in Figure6presents the constraints required by the editorial chief on the document collection for what concerns the originality, the credibility, the citing and reading popularity of the articles. In particular, this user is interested in docu-

Figure 7 .Figure 8 .

 78 Figure 7. Example of DTDs for the journal and the reviewers' commentsSuppose the query given in Figure8sent by the three following users: the secretary, the authors of submitted papers and the editorial chief in order to get as a result XML element the name of authors, the title and the reviewers' comment of the submitted papers to the journal. The query language used is XQuery 7 [FM01][START_REF]XQuery 1.0 and XPath 2.0 Functions and Operators Version 1.0. W3C Working Draft[END_REF]. <results> {for $b in document /journal/article, $a in $b/author, $t in $b/title, $r in document($b/@qIdref)/metadataset return <result> { $a } { $t } { $r } </result>} </results> Figure 8. Example of query Then, the recommender system will return respectively the following results:

C o c o o n S e r vXFigure 9 .

 9 Figure 9. Quality Recommendation Processor of XDARE from the document processing perspective

Information Quality Metadata Modeling and Processing3.1. Metadata for Document QualityWe proposed a simple XML document quality metadata schema (Figure1) that: 1) allows a rigorous but flexible definition of each dimension of the quality of documents, 2) re-uses the existing standards of metadata proposed in the literature. We developed the corresponding system that 1) assists the user who can be a reviewer into the collaborative annotation process, and so, who should be able to define and evaluate himself the quality of the documents, 2) assists the user who's searching high quality information with providing him an adaptive recommendation (as quality view) of the retrieved documents depending both on his query and also on his quality requirements. The quality of a document is defined by combining quantitative measurements (computed by the system) and qualitative evaluation made by one (or several) reviewer(s); these metadata stored in XML files are embedded or linked to the content of the XML documents. As Figure1shows, the metadata type associated to a document (metadatasetType) can be a set of metadata (metadataset) or one metadata (metadata) which is composed of a criterion, a scoring value for the criterion given by a human reviewer (annotator) or computed by a program (program), a creation date and 1 OnLineAnyWhere, FlashMap, http://www.onlineanywhere.com 2 Spyglass, "White Paper of Prism 2.2", http://www.spyglass.com/images/Prism22.pdf 3 FastLane, http://stage.acunet.net/spectrum/

index.html 4 QuickWeb, http://www.intel.

com/quickweb 5 ProxiNet, ProxiWare, http://www.proxinet.com/products_n_serv/proxiware/

RuleML, http://www.dfki.uni-kl.de/ruleml/

XQuery, http://www.w3.org/TR/xquery-semantics/


Laure Berti-Equille
email: laure.berti-equille@irisa.fr

Quality-Based Recommendation of XML Documents

Keywords: Information Quality, XML, Metadata, Recommender System

Information quality is multidimensional and can be modeled as an ordered set of weighted criteria. For a collection of XML documents, our approach consists firstly in harvesting and generating information quality indicators and enriching the meta-description of XML documents. Quality metadata are then exploited within the query processing for metadata-driven information retrieval and filtering in order to propose quality-adaptive recommendation strategies (with a quality view as a result) of the queried XML documents. This quality view depends on the user's profile and on his specific information quality requirements. The paper describes the architecture of a quality-based recommender system for XML documents.

Introduction

Finding relevant, high-quality information in the world wide web or even in a collection of documents is a difficult task. Information quality has no consensual definition and its evaluation requires the measurement and the weighted combination of both objective and subjective quality criteria and, then, the matching between the relative perception of information quality and the users' profile in terms of quality requirements.

In this context, content-based and collaborative recommender systems [START_REF] Resnick | Recommender systems[END_REF] work by automatically recognizing, tallying and redistributing recommendations of the web resources. The multi-confirmed recommendations appear to be significant resources for the relevant community and finally the number of distinct recommenders of a resource is a plausible measure of resource quality. But, many collaborative recommender systems particularly ratings-based systems are built on the assumptions of role uniformity : they expect all users to do the same types of work in return for the same type of benefits. And the notion of user satisfaction and the evaluation task (rating) are very relative and should be considered in a flexible and adaptive way.

Our approach consists in taking into account the information quality evaluations and requirements in the context of collaborative annotation and quality-driven information retrieval and filtering of XML documents. We propose a modeling of document quality with various objective and subjective quality criteria. The objective crite-ria can be quantitative and are calculated by statistical methods. The subjective criteria are defined and evaluated by a group of reviewers collaborating with the aim of reaching a consensus on the annotation of the documents. The selection of the documents is both content-based and quality-based, i.e. depending on the content and the structure of the documents relevant to the query and depending on the qualitative aspects required by the user. Our objective is to propose a multi-criteria adaptive recommendation of XML documents, and to refine the traditional selection of documents by exploiting metadata on the resource quality which are embedded into or linked to them, computed or generated automatically, or built as a result of a collaboration between individuals. From these specifications, we developed the XDARE system (XML-Documents Annotation and Recommendation Environment) for qualitydriven annotation and recommendation of XML documents.

The rest of the paper is organized as follows: section 2 presents the previous works on information quality and adaptive hypermedia systems. Section 3 describes our quality metadata model and presents the recommendation process for XML documents. Section 4 describes the architecture of our system. Lastly, Section 5 concludes the paper and presents our perspectives of research and development.

Related Works

Metadata and Data Quality

In the context of a distributed information environments generally, metadata harvesting refers to the automatic collection of descriptive information from distributed resources. Recently, one particular way of accomplishing this collection of distributed metadata has been the subject of considerable attention in museums, archives and elearning communities (e.g. the metadata collection proposed by the Open Archives Initiative Metadata Harvesting Protocol (OAI-MHP) [START_REF]The Open Archives Initiative Protocol for Metadata Harvesting[END_REF]). In the domains of geographical information systems [START_REF] Goodchild | Data quality in geographic information : from error to uncertainty[END_REF] (ISO 15046-13, CEN prEN 287-008, FGDC) and digital libraries (Dublin Core, Bib-1, GILS, STARTS, Z39.50 ANSI/NISO, etc.), most of the exchange standards propose metadata specifications for information quality, which are either automatically extracted or measured by sampling from data sets.

Many research works on information quality also proposed various definitions, conceptual models and methodologies to improve data quality in databases or in information systems [START_REF] Naumann | Quality-Driven Query Answering for Integrated Information Systems[END_REF] [WSF95] [Wan98] [START_REF] Redman | Data quality for the information age[END_REF]. The data quality dimensions most frequently mentioned in the literature are: accuracy, completeness, actuality and consistency. But many others dimensions, metrics and measurement techniques have been proposed in the literature [START_REF] Redman | Data quality for the information age[END_REF] The DESIRE Project [HBP00] also produced a detailed list of quality standards to be used for the selection of the Web resources with various categories of quality criteria: 1) criteria related on the policy of diffusion and the range of the resource, 2) criteria related to the content, 3) criteria related to the form, 4) criteria related to the management of the documentation quality.

[FLR94] [Vas00] [MR00] [BP02] [Nau02] [NFL99] (see
In the context of the TIPS European project [TIPS99], several services have been developed related to the reuse of evaluations performed by humans on scientific publications. The first one, called QCT (Quality Control Tools) aims at collecting human detailed evaluations of documents in order to enrich the traditional topical indexing of documents with quality-related information (see Table 1 for the quality features used in the QCT-TIPS project for document quality). The second one, called SF (Social Filtering) integrates a push functionality as a alternate and complementary tool to traditional pull services such as information retrieval ; documents are pushed to users with respect to the evaluations they have made in the past, and compared to other users' evaluations.

Actually, several dimensions and metrics of information/resource quality have been proposed, but no methodologies give specific or pragmatic solutions concerning:

-what are the good/appropriate quality metrics to use, -how to measure/collect these quality metrics, -how to verify their goodness.

A bench mark for metadata quality assessment would be an important and useful effort for the research progress in this area.

Adaptive Hypermedia and Recommender Systems

Recently a number of adaptive hypermedia systems have appeared as impersonalized systems, recommender systems |RV97] with a common goal: to learn about the implicit preferences of individual users and to use this information to serve the entire community of these users better. The early recommender systems mainly used Information Filtering (IF) techniques and individual previous behavior to produce recommendation. The major drawback of IF techniques is that they do not provide much in the way of serendipitous discovery. To cope with this drawback, Collaborative Filtering (CF) techniques have been proposed in order to recommend items based on the opinion (rating) of other users who have similar tastes. GroupLens [START_REF] Resnick | GroupLens: An Open Architecture for Collaborative Filtering of Netnews[END_REF] is a serverside recommendation engine for Usenet news. A user's profile is created by recording the user's explicit ratings of various articles. Automatic collaborative filtering is used to statistically compare one user's likes and dislikes with another user and to recommend articles from other similar users profiles. Various recommender systems have been created to assist users for selecting potentially interesting information and for filtering out what users may not be interested in (PHOAKS [START_REF] Terveen | PHOAKS: A System for Sharing Recommendations[END_REF], recommendation of Web resources mined from Usenet news messages, Ringo [START_REF] Shardanand | Social Information Filtering: Algorithms for Automating "Word of Mouth[END_REF], a music recommender system). But two major limits of the CF-based techniques are:

-the "early-rater" problem occurring for the first rating of documents without benefit of other previous recommendations, -the "sparsity rating" problem occurring when the overlap between user's ratings (or number of co-rated items) is small or null and as a consequence that the recommendation results may be not accurate or cannot be produced. The next level of recommender system is hybrid systems combining IF and CF techniques, such as MovieLens [GS+99], a movie recommender system using filterbots (IF agents) as rating robots which participate as members of the CF system. Both Personal Web Watcher [START_REF] Mladenic | Personal WebWatcher: Implementation and Design[END_REF] and Letizia [START_REF] Letizia | An Agent That Assists Web Browsing[END_REF] are content-based systems that recommend web-page hyperlinks by comparing them with a history of previous pages visited by the user. Personal Web Watcher [START_REF] Mladenic | Personal WebWatcher: Implementation and Design[END_REF] uses an offline period to generate a bag of words style profile for each of the pages visited during the previous browsing session. Hyperlinks on new pages can then be compared to this profile and graded accordingly. Letizia [START_REF] Letizia | An Agent That Assists Web Browsing[END_REF] uses the unused online time when the user is actually reading a web page to search the adjoining hyperlinks. The user's profile is composed of keywords extracted from the various pages visited.

Most of the current hybrid systems still use co-rated items among users in finding correlated neighbors for an active user, and co-rated items between user and filterbot to find agreed filterbots.

On the opposite, PICS (Platform Content for Internet Selection) [START_REF] Resnick | PICS: Internet access controls without censorship[END_REF] is an infrastructure which associates labels to the contents of the documents available on the Internet in order to block the access for non-authorized users. Originally conceived to help the parents and the professors to control the navigation of their children/pupils on the Internet, it makes it possible to affect any criterion on the labels which the system interprets to authorize or block the access to the documents. Complementary to the recommendation, the advantage of this approach is that the structure of a document can be enriched by adding labels which define the conditions of viewability (blocking or full access).

S Y ST E M T Y P E M O D E L P FIT , ifW eb

. Information Filtering and Collaborative Filtering Systems

Another aspect of information personalization is to adapt web content to users' preferences and also to the variations of the client environment, so that web pages can be prepared suitable for the client. Adaptive hypermedia systems [START_REF]Adaptive Hypermedia and Adaptive Web-Based Systems[END_REF] can learn about the implicit and explicit preferences of individual users and using this information to personalize information retrieval processes. In this context many adaptive hypermedia systems have been proposed, such as OnlineAnywhere1 , SpyGlass2 , Fast-Lane3 , QuickWeb4 , ProxyNet5 , Digestor [START_REF] Bickmore | Digestor: Device-independent access to the World Wide Web[END_REF], TranSend [START_REF] Fox | Adapting to Network and Client Variation Using Infrastructural Proxies: Lessons and Perspectives[END_REF], and Mobiware [START_REF] Angin | The Mobiware Toolkit: Programmable support for adaptive mobile networking[END_REF]. However, most of them only make adaptation of the web content under special conditions due to the lack of structural information of HTML content, and many of them focus on image conversion.

Motivation

Among these various research propositions concerning on one hand, information quality, and on the other hand, recommender, blocking and adaptive hypermedia systems, we came to the point that there is no proposition in the literature nor system that combines resource quality for alternatively recommending/blocking and adapting digital resources on demand (in a way driven by users' profiles). Actually, in the current research works in IF or CF, the quality of the content is not considered as a key element for the users' decision in the recommendation process and we think this problem is not yet sufficiently addressed by existing approaches. Our motivation was to propose the three services (blocking/recommending and adapting information) in a flexible way and to consider the quality dimensions of the queried documents. We retained several principles which guided our approach: -to improve quality of a search result, it is necessary to evaluate the quality of the documents and information and to exploit it in the query processing, -it's necessary that the definition of the document quality remains flexible and the use of labels and metadata allow this flexibility for both specification and interrogation. Compared to existing approaches for collaborative or content-based recommender systems, the innovative aspect of our approach is to include constraints and requisites on content quality that is complementary for best recommendation services.

Modeling Information Quality and Processing Quality Metadata

Metadata for Document Quality

The choice of modeling for document quality has been made in order: 1) to allow a rigorous but flexible definition of each dimension of the quality of documents, as its measurement protocol, 2) to re-use the existing standards of metadata proposed in the literature including elements of quality, 3) to propose an assistance to the user who can be a reviewer into the collaborative annotation process, and so, should be able to define and evaluate himself the quality of the documents choosing and combining several specific programs for the generation/extraction of metadata, 4) to propose an assistance to the user who's searching high quality information with providing him an adaptive recommendation (as a quality view) of the retrieved documents depending both on his query and also on his quality requirements.

Figure 1. Metadata description

The quality of a document is defined by combining quantitative measurements (computed by the system) and qualitative evaluation made by one (or several) reviewer(s) ; these metadata stored in XML files are embedded or linked to the content of the XML documents. As Figure 1 shows, the metadata type associated to a document (metadatasetType) can be a set of metadata (metadataset) or one metadata (metadata) which is composed of a criterion, a scoring value for the criterion given by a human reviewer (annotator) or computed by a program (program), a creation date and a comment (comment). A consensus can be calculated for a given criterion and a date if several notations have been proposed by several reviewers. The instantiation of the quality standards for a document can be based on an adaptation of the non-exhaustive list of criteria given in

[WSF95] [Red96][MR00][TIPS99].
Example 1. Figure 2 gives an example of quality metadata instances that can be associated to a document with both subjective criteria (originality, accuracy) and objective criteria computed by specific programs (citing popularity, reading popularity). In this example, the notations are values in [0,10] ; the originality and the accuracy of the document are evaluated by the reviewer A1 and the citing and reading popularity computed by programs similar to the one used in CiteSeer (http://citeseer.nj.nec.com/). <metadataset qid="q1" type="ContentMD" scheme=" " sortkey=" " index="noindex" show="noshow" dIdref="dId"> <metadata mid="m1

Annotation and Recommendation Process

The process of collaborative annotation and recommendation of the documents can be decomposed into four main operations (Figure 3): 1) harvesting and generating the quality metadata (annotating), 2) indexing the documents and their meta-descriptions (indexing), 3) matching the query (including the quality requirements of the users) with the representation of the indexed documents (matching), and 4) scoring and viewing the documents (viewing) with different recommendation strategies.

In this process, each step constitutes an elementary operation on the XML document collection. First, at the annotation step, each document of the collection is enriched by several quantitative and qualitative quality metadata (respectively, objective measures and subjective evaluations of the document quality) such as, in the example of Figure 2. Metadata, document contents and structures are then used for the indexing process in order to represent each document as a multidimensional vector on these three axes : quality (criteria), content (terms) and structure (XML elements, attributes). For processing a query, the system finds the documents that best match the query terms and it applies to their respective metadata : -the quality rules specified into the XQS file (XML Quality Style sheet) -and the quality requirements specified into the XPS file (XML Profile Style sheet) that correspond to the profile of the user (or of the group of users). At this stage, the system has to use the recommendation strategy defined into the XQS file, to verify and to adapt (i.e. soften) the quality constraints given into the XPS profile and for building the quality-driven view of the query result.

Definition 1. XML Quality Style Sheet (XQS)

A quality style sheet (XQS) (whose DTD is given in Figure 4) is a file used in the query processing that references the profile to use and the rules that have to be applied to each document node for each user profile ; each rule applied to a document node according to a user profile has a priority (between 1-lowest and 5-highest priority) and defines the access mode (access without recommendation (default), with recommendation or blocking access) and the strategy for verifying the quality constraints on the effective document quality (exact, approximate or negotiated). The exact strategy means that all the constraints must be verified on the value of every quality criteria (i.e., metadata values) of the targeted document node ; the approximate strategy allows the approximate and flexible matching between the constraints values and the effective quality criteria values (with nearest neighborhood) ; the negotiation strategy allows to soften interactively the quality constraints in order to match the document nodes that answer the query with the best quality. The DTDs of the journal and the metadata set are given in Figure 8. These users will be allowed (or not) to access information according to the following rules : R1 -the secretary access all the submitted articles ; R2 -the authors only access their own article; R3 -the authors are not allowed to access the papers and reviewers' comments of other authors ; R4 -the editorial chief access the best submitted papers.

<xqs DefaultProfileFile="profiles.xps"> <!--Rule 1 --> <rule Profile="users/secretary" DocumentNode="article[@id=$user]//node()" Priority="2" Access="default" Strategy="exact"/> <!--Rule 2 --> <rule Profile="users/*[name()='Authors']" DocumentNode=" article[@dId=$user]//node()" Priority="5" Access="default" Strategy="exact"/> <!--Rule 3 --> <rule Profile=" users/*[name()='Authors']" DocumentNode="article[@dId!=$user]/@qIdref//node() | article[@dId!=$user]//node" Priority="5" Access="blocking" Strategy="default"/> <!--Rule 4 --> <rule Profile="users/EditorialChief" DocumentNode="article//node() | article/@qIdref//node()" Priority="5" Access="recommending" Strategy="negotiated"/> </xqs>

Figure 5. Example of XQS sheet Definition 2. XML Profile Style Sheet (XPS)

A profile style sheet (XPS) is a file that references the user or group of users of the profile and defines the constraints on the document quality dimensions. The set of quality constraints is defined as a quality contract. For a compact and simplified presentation, Figure 6 shows an extract of XPS in the BNF-style grammar and an example corresponding to the user profile of the Editorial Chief considering the quality metadata given previously in Figure 2. The example in Figure 6 presents the constraints required by the editorial chief on the document collection for what concerns the originality, the accuracy, the citing and reading popularity of the articles. In particular, this user is interested in documents with a certain level of originality and accuracy (higher than 6 in the interval [0,10]), with citing popularity higher than 7 and reading popularity between 6 and 9. Figure 6 presents the user profile in a BNF-style grammar for simplification but, actually, the user profiles are stored in XML-files using the RuleML6 DTD.

Recommending High-Quality XML Documents

The key elements of the recommendation process are the quality-based recommendation rules (previously presented in XQS file see Figure 4) and the quality view that is generated according to two main heuristics.

Definition 3. Quality-based Recommendation Rule

A quality-based recommendation rule is the following quadruplet : < Profile, Docu-mentNode, Access, Strategy > with :

-Profile : the path expression related to the profiles of the XPS style sheet, -DocumentNode : the Xpath expression evaluated inside the targeted XML document, -Access : the value for recommending or for blocking the access to the information items (as document nodes), -Strategy : the multi-criteria selection algorithm used to recommend the targeted node of the document. This algorithm uses Multiple Attribute Decision Making (MADM) approach [KZ00] that will not be discussed in the paper. The semantics of the quality-based recommendation rule is not unique and depends on the type of the considered node (element, attribute, text...). Several integrity constraints may be defined in order to maintain consistency between the rules. Instead of recommendation of entire documents based on general quality requirements, we suggest the approach of filtering XML-documents based on quality criteria in the personal profile of the requestor.

Definition 4. Quality View

A quality view of a document is the result (as the fragment(s) of the XML document) that corresponds to the query and satisfies the quality constraints and rules defined in the user's profile. Two heuristics are used in order to build the quality view of each retrieved document in conformance with the quality constraints and the recommendation strategy chosen for the user who sent the query to the system. The quality view of the XML document is built node by node (i.e., XML element by element).

Heuristic 1. If the access to a node n of a document is allowed for recommendation for a user u, then u can see the recommended sub-tree of the XML document whose n is the root node if it satisfies the quality constraints defined as quality requisites into the user profile with the chosen strategy (exact or approximate or negotiated recommendation for quality requirements).

Heuristic 2. If the access to a node n is forbidden for a user u, then u cannot see the sub-tree of the document whose n is the root node.

The algorithm given in Annexes (Figure 10) is used to build the quality views according to:

-the quality-based recommendation rules given in the XQS file, -and the user's profiles given in the XPS file.

Example 4. Query Following the Example 2, consider the edition of a scientific journal and the associated review comments of the submitted papers. The reviewers comments are stored as metadata files. The content of the special issue of the journal has the following DTD (Figure 7) : Suppose the following query sent by the three users : the secretary, the authors of submitted papers and the editorial chief in order to get as a result XML element the name of authors, the title and the reviewers' comment of the submitted papers to the journal (Figure 8). The query language used is XQuery Result 2. Author's quality view. This query sent by an author will show only the title and the author name and the reviewers' comments of his own submitted article, but this author will not be allowed to see the papers and the comments of other authors.

Result 3. Editorial chief quality view.

This query sent by the Editorial Chief will show the title, the author names and the reviewers' comment of the best articles.

System Architecture

Document and Quality Processing

We used standard tools for implementing the quality recommendation processor of our system XDARE (XML-Documents Annotation and Recommendation Environment). The prototype has been developed in Java and implement the following operations (Figure 9 ):

1 XML Document Analysis: The document file is parsed (Xerces Apache) and syntactically analyzed (SAX API) and the corresponding internal representation is created and stored. The internal representation and manipulation has been developed with DOM API: the events produced by the SAX API during the first step of the document analysis are used to build the corresponding DOM tree. The XDARE operators use the instances of this internal representation by recopy and each query produces a new tree.

2 Query and Xpath Analysis: The grammar is an extended XML Query syntax.

Jlex and Cup are here used to produce the corresponding Java syntactical analyzer including the Xpath expression analysis. The query tree is explicitly instantiated for future optimization. 4 Quality View Generation: When the user wants to browse a XML document, he actually obtains a view of this document in conformance with his quality requirements.

Quality-based Recommendation Processor

Our quality processor is based upon the Cocoon java servlet. The initial documents and the quality recommendation rules (XQS) and the user profiles (XPS) are in XML format. The quality view generated in conformance with the user profile is produced with XSLT sheet that is generated from the XQS sheet using XSLT. Cocoon enables the automatic processing shown in Figure 10. An identified user asks for the access to a document d to the XDARE server. The document d refers to a XQS sheet. If the XSLT sheet corresponding to the translation of the XQS sheet is not yet in the cache, it is produced dynamically. Otherwise, the XSLT sheet is applied to d for producing the view of the document according to the user quality profile (XPS). Cocoon utilization offers lots of advantages : in particular the multi-level cache system. This mechanism enables to process only once the transformation from XQS to XSLT sheet. Moreover, the produced views are also put into the cache memory and the multiple access to the same filtered document quality doesn't overload the server. On the other hand, views (that are also XML documents) can be naturally retransformed with XSLT in order to be produced in HTML or PDF formats.

Conclusion and Perspectives

In this paper, we suggest a general architecture for quality-based recommendation of XML documents. The document quality is modeled as a set of (criteria, value) pairs collected in metadata sets, which are associated with XML documents.

A metadata schema is proposed, capable of capturing arbitrary (criterion, value) sets along with general metadata such as date and generator of the (criterion, value) assignment. We identify four basic operations to achieve quality recommendation: 1) annotation with metadata describing the documents quality, 2) indexing the documents, 3) matching queries, and 4) viewing the recommended parts of the documents. The quality requirements of each user are kept in individual user profiles (so called XPS files).

Every XML document in the document base refers to a quality style sheets (so called XQS files) which allow for specification of several matching strategies and contain matching rules relating parts (sub-trees) of XML documents to user profiles. An algorithm is described for evaluation of the quality style sheets and user profiles in order to build an "adaptive quality view" of the retrieved XML document. Finally, we sketched the architecture of the XDARE system, which implements the proposed data structures and algorithm to support quality-based retrieval and adaptive quality views of XML documents. The system is built on standard Java components for XML processing (such as XERCES and XALAN).

The specification and the exploitation of metadata describing the quality of documents can improve the system effectiveness for information filtering and recommending. The user profiles may include requirements for quality of results.

The innovative aspect of our work is to propose the three services (blocking/recommending and adapting information) in a flexible way and to combine content-based and quality-based recommendation with considering the quality dimensions of queried XML documents.

The perspectives of our work mainly concern now: the automatic learning of user profiles (and quality requisites) by inductive logic programming (ILP module for user profiling) and the operational validation of our XDARE system with communities of users.

  [A T 97 ] IF system B ayesian netw orks Q uery-based dialogue w ith user Inform a tio n L ens S yste m [M G T +8 7] IF system R ule-b ased co nstructio n of tem plates by the user S yskill & W eb ert [P M B 97 ] IF A gents N aïve (sim ple) B ayesian C lassificatio n on B oo lean vecto rs, N earest N eighbo rs, Sym b olic P ro file Learning o n w eb p age o f interest fo r the user and link suggestion using decision trees A m althaea [M o u96 ] IF A gents D iscovery A gents E co no m ic M od el w ith an ecosystem of agents F A B [R V 9 7][B al97 ] IF A gents H yb rid reco m m end er system G ro up Lens [R I94 ] C F system P H O A K S [T H + 9 7] C F system P ersonal W eb W atcher [M la9 6] C F /IF system N aïve (sim ple) B ayesian C lassificatio n on B oo lean frequency vecto rs L etizia [L ie95] C F /IF system H yb rid reco m m end er system Inference from the user's b ro w sing b ehavio r Table 2

Figure 3 .

 3 Figure 3. Operations for Retrieving Recommended XML Documents

<Figure 4 .

 4 Figure 4. DTD for XQS sheet Example 2. Quality Style Sheet ExampleIn Figure5, four rules are defined in the quality style sheet file concerning respectively three different users : the chief of the editorial board of a scientific journal, the secretary and the authors who submitted a paper for the special issue of the journal. The DTDs of the journal and the metadata set are given in Figure8. These users will be allowed (or not) to access information according to the following rules : R1 -the secretary access all the submitted articles ; R2 -the authors only access their own article; R3 -the authors are not allowed to access the papers and reviewers' comments of other authors ; R4 -the editorial chief access the best submitted papers.

Figure 6 .

 6 Figure 6. Extract of the XPS grammar and an example in the BNF-styleExample 3. Profile Style Sheet ExampleThe example in Figure6presents the constraints required by the editorial chief on the document collection for what concerns the originality, the accuracy, the citing and reading popularity of the articles. In particular, this user is interested in documents with a certain level of originality and accuracy (higher than 6 in the interval [0,10]), with citing popularity higher than 7 and reading popularity between 6 and 9. Figure6presents the user profile in a BNF-style grammar for simplification but, actually, the user profiles are stored in XML-files using the RuleML 6 DTD.

<Figure 7 .

 7 Figure 7. The DTDs of the journal example and the reviewers comments

Figure 8 .

 8 Figure 8. Example of query Result 1. Secretary's quality view. This query sent by the secretary will create as a result a flat list of all the author-title pairs of the submitted paper. Her quality requirements are given in the XQS file of Figure 5 without recommendation.

QFigure 9 .

 9 Figure 9. Quality Recommendation Processor of XDARE from the document processing perspective

Quality of identification: citabilityTable 1 . Some definitions of information quality

 1 Table 1 for a non-exhaustive list of quality dimensions). Most of the techniques of quality measurement are centered on various methods of imputation such as inferring missing data from statistical patterns of available data, predicting accuracy estimations based on the given data, data editing (automating detection and handling of outliers in data), error control [Red96][FLR94].

	Authors		Quality Dimensions
	Brodie [Bro80]	6 concepts	Integrity, Abstraction Level, Semantic Expression Power, Validity, Maintenance, Resource Use Efficiency
	Delen, Rijsenbrij [DR92]	-4 dimensions -21 aspects -40 attributes	Development and control of Information Systems, Static Properties for maintenance, Dynamic Function, Information importance : correctness of data, completeness,
			up-to-dateness, accuracy, verifiability
	Wang et al. [Wan98] [WSF95]	-4 categories -179 attributes	Intrinsic Quality, Accessibility Quality, Contextual Quality, Representation Quality
	Redman [Red96]	-4 dimensions for values -8 dimensions for	-Accuracy, completeness, actuality, consistency -Appropriateness, interpretability, portability, format
		representation format	precision, format flexibility, null value, efficient use,
			consistency
	QCT-TIPS [TIPS99]	8 quality features for document quality
			reputation of journal or
			conference ;
			Availability: durability, printability ;
			Popularity: citing/reading popularity ;

Scientific quality: correctness, completeness, originality, accuracy, currency, quality of demonstration, quality of references list, quality of methodology ; Readability: quality of the writing style, quality of the logical structure, ad-equation of illustrations, absence of repetitions, clarity of expression of ideas ; Intended audience: technical or educational level ;

Recency: publication date Authority: reputation of author,

  Example of quality metadata linked to the document identified by "dId"

	">
	<criterion>Originality</criterion>
	<score>6</score>	Document
	<date>12/03/2003</date>	Identifier
	<generator><annotator>A1</annotator></generator>
	</metadata>
	<metadata mid="m2">
	<criterion>Accuracy</criterion>
	<score>7</score>
	<date>15/03/2003</date>
	<generator><annotator>A2</annotator></generator>
	</metadata>
	<metadata mid="m3">
	<criterion>Citing Popularity</criterion>
	<score>7</score>
	<date>16/03/2003</date>
	<generator><program>Citation_Index_Program</program></generator>
	</metadata>
	<metadata mid="m4">
	<criterion>Reading Popularity</criterion>
	<score>6</score>
	<date>18/03/2003</date>
	<generator><program> Nb_Download_Program </program></generator>
	</metadata>
	</metadataset>
	Figure 2.

OnLineAnyWhere, FlashMap, http://www.onlineanywhere.com

Spyglass, "White Paper of Prism 2.2", http://www.spyglass.com/images/Prism22.pdf

FastLane, http://stage.acunet.net/spectrum/index.html

QuickWeb, http://www.intel.com/quickweb

ProxiNet, ProxiWare, http://www.proxinet.com/products_n_serv/proxiware/

RuleML, http://www.dfki.uni-kl.de/ruleml/

XQuery, http://www.w3.org/TR/xquery-semantics/

Annexes

Algorithm Quality View Generation declarations input :

xqs : an XQS file ; xps : an XPS file ; U : user of the view V ; N : a document Node (element, attribute, text); L : the list of XML Nodes corresponding to the considered document; R : the list of XML Nodes corresponding to the document view; output :

V : quality view corresponding to the query of the user U Begin Initialize R and L such that R = [] and L = [] ; Add the root element of the answered document to L ; While L is not empty;

N ← the first node of L ; select every quality-based recommendation rule of xqs such as :

-the node N satisfies the pattern into the attribute DocumentNode ; -the user U is located with the path of the attribute Profile ; Apply the policy such as: -for the node N and the user U, if it exists a conflict between a set of rules then choose the rules with the highest priority ; -if there is more than one selected rule with the same priority, then the last rule is selected;

if the value of the attribute "access" of the selected rule is "blocking", then delete N from the list L else add N to R ; Replace N by its children nodes (attributes, elements) into L ;

if the value of the attribute "access" of the selected rule is "recommending", then case 1: the value of the attribute "strategy" is "exact": each quality criterion associated as metadata to the node N is compared to the quality constraints of the user profile xps ; if all the constraints are exactly satisfied by the criteria values then add N to the list R and delete N to L ;

case 2: the value of the attribute "strategy" is "approximate": each quality criterion associated as metadata to the node N is weighted and compared to the quality constraints of the user profile xps ; if a quality constraints Ci (∀i) is satisfied with a weighted approximation ±ε i then add N from the list R and delete N to L ; case 3: the value of the attribute "strategy" is "approximate": each quality criterion associated as metadata to the node N is weighted and compared to the quality constraints of the user profile xps ; while each quality constraint is not satisfied -soften interactively the constraint (reduce its values); end-while add N from the list R and delete N to L ; Replace N by its children nodes (attributes, elements,...) into L ; end-while V ← R ; display the quality view V corresponding to xps and xqs; end.

Figure 10. Algorithm for quality-based recommendation of XML documents