Man-machine collaboration to acquire adaptationknowledge for a case-based reasoning system

Amélie CordierUniversité de Lyon, CNRSUniversité Lyon 1, LIRIS,

UMR5205, F-69622, Lyon,France

Amelie.Cordier@liris.cnrs.fr

Emmanuelle GaillardLORIA, Université de Lorraine

BP 239, F-54506Vandœuvre-lès-Nancy,

CEDEX, FranceEmmanuelle.Gaillard@loria.fr

Emmanuel NauerUniversité de Lorraine, LORIA

BP 239, F-54506Vandœuvre-lès-Nancy

CEDEX, FranceEmmanuel.Nauer@loria.fr

ABSTRACTThis paper shows how humans and machines can bettercollaborate to acquire adaptation knowledge (AK) in theframework of a case-based reasoning (CBR) system whoseknowledge is encoded in a semantic wiki. Automatic pro-cesses like the CBR reasoning process itself, or specific toolsfor acquiring AK are integrated as wiki extensions. Thesetools and processes are combined on purpose to collect AK.Users are at the center of our approach, as they are in a clas-sical wiki, but they will now benefit from automatic tools forhelping them to feed the wiki. In particular, the CBR sys-tem, which is currently only a consumer for the knowledgeencoded in the semantic wiki, will also be used for producingknowledge for the wiki. A use case in the domain of cookingis given to exemplify the man-machine collaboration.

Categories and Subject DescriptorsI.2.6 [Artificial Intelligence]: Learning—knowledge acqui-sition; H.2.8 [Database Management]: Database Appli-cations—Data mining ; H.3.3 [Information Storage andRetrieval]: Information Storage and Retrieval—relevancefeedback

General TermsDesign, Management

KeywordsMan-machine collaboration, adaptation knowledge acquisi-tion, semantic wiki, case-based reasoning, knowledge discov-ery

1. INTRODUCTIONThis paper presents an architecture for improving the collab-oration between humans and machines in the framework ofthe system Taaable1. Taaable is a case-based reasoning

1http://taaable.fr/

(CBR) system which adapts cooking recipes to users con-straints [1], using cooking knowledge encoded in a semanticwiki, called WikiTaaable2 [7]. Storing the cooking knowl-edge into a wiki allows to better manage knowledge: usersare fully involved in the knowledge acquisition part of thesystem instead of only using the Taaable system. The ideais that if users increase the quantity and the quality of theknowledge in the wiki, the results of the Taaable adapta-tion process improves. For this, automated processes, likethe Taaable adaptation process and other knowledge ex-traction processes, can be used to feed the wiki with newknowledge. Our objective, in this paper, is to consider (1)the “knowledge” part (WikiTaaable) and the “reasoning”part (Taaable) of a CBR system as a whole, (2) that thesetwo parts can mutually and continuously benefits one fromthe other, and (3) that users play a key role for managingthe interactions between these two parts.

This paper proposes an architecture that improves the ac-quisition of adaptation knowledge (AK), a specific kind ofknowledge a CBR system can use to improve its results.The Taaable/WikiTaaable cooking application is chosenas concrete use case. Section 2 gives the minimal require-ments about this application context and the motivationsfor acquiring AK. Section 3 gives the architecture for thecollaboration between automatic processes and users for im-proving the AK acquisition and shows how this architecturehas been instantiate in our use case. Section 4 presents re-lated works and section 5 concludes the paper and discussesongoing works.

2. CONTEXT AND MOTIVATIONTaaable is a CBR system that has been designed for par-ticipating to the Computer Cooking Contest3, an interna-tional contest which aims at comparing CBR system resultson a common domain: cooking. Several challenges are pro-posed in this contest. Among them, two challenges (won byTaaable in 2010):

• the main challenge, asking CBR systems to returnrecipes satisfying a set of constraints given by the user,such as inclusion or rejection of ingredients, the typeor the origin of the dish, the compatibility with somediets (vegetarian, nut-free, etc.). For example: a usermay ask for “a dessert, with rice and fig”, as illustrated

2http://wikitaaable.loria.fr/3http://computercookingcontest.net/

in Figure 1. Systems have to search into a set of limited(approximately 1500) recipes for recipes satisfying theconstraints, and if there is no recipe satisfying all theconstraints, the systems have to adapt existing recipesinto new ones.

• the adaptation challenge, asking CBR systems to adapta given recipe to specific constraints. For example,“adapt the My strawberry pie recipe because I do nothave strawberry”.

2.1 TAAABLE principles

Figure 1: The TAAABLE interface. Queried for adessert dish, with rice and fig, TAAABLE proposesto replace mango by fig in the “Glutinous rice withmangoes” recipe. After viewing the adapted recipe,the user can give feedback about the substitution(“OK” or “not OK”).

Like many CBR systems [15], Taaable uses an ontology toretrieve the source cases that are the most similar to a targetcase (i.e. the query). Taaable retrieves and creates cookingrecipes by adaptation. According to the user constraints, thesystem looks up, in the recipe base (which is a case base),whether some recipes satisfy these constraints. Recipes, ifthey exist, are returned to the user; otherwise the system isable to retrieve similar recipes (i.e. recipes that match thetarget query partially) and adapts these recipes, creatingnew ones. Searching similar recipes is guided by several on-tologies, i.e. hierarchies of classes (ingredient hierarchy, dishtype hierarchy, etc.), in order to relax constraints by gener-alising the user query. The goal is to find the most specificgeneralisation (with the minimal cost) for which recipes existin the case base. Adaptation consists of substituting someingredients of the source cases by the ones required by theuser. It is important to notice that Taaable is based onan hypothetical reasoning and not deductive one. So, it ispossible that some adaptations proposed by the system maybe irrelevant (i.e. not cookable).

For dealing with the adaptation of a specific recipe (which isthe adaptation challenge problem), Taaable uses the samehierarchy based generalisation/specialisation mechanism ona recipe base containing only the recipe that has to adapted.For example, when adapting the “My Strawberry Pie” recipe(in which strawberries are required) to the constraint “nostrawberry”, Strawberry is generalised on Berry, which isthen specialised in another berry (Raspberry, Blueberry,Blackberry, etc.). Substitutions (e.g substitute Strawberry

by Raspberry) are proposed to the user.

Again, this hierarchy-based adaptation can produce badsubstitutions. Indeed, adding a new ingredient in a recipemay be incompatible with some other ingredients(s) of therecipe and/or may require the addition of new ingredi-ent(s). Another problem, when adapting a given recipe isthat Taaable is not able to choose, during the specialisa-tion step, which ingredient is the best for substituting an-other one. In the previous example, Taaable is not ableto determine whether it is better to replace Raspberry byStrawberry, by Blueberry or by any other berry. This lackhas been corrected by building a special knowledge discoveryprocess which is detailed in section 2.4.

2.2 WikiTAAABLEWikiTaaable is a semantic wiki containing the set of re-sources required by the Taaable reasoning system. Wiki-Taaable contains an ontology of the domain of cooking,and recipes.

Figure 2: The Berry concept in WIKITAAABLE.

The cooking ontology is composed of 6 hierarchies: a foodhierarchy (related to ingredients used in recipes, e.g. Berry,Meat, etc.), a dish type hierarchy (related to the types ofrecipes, e.g. PieDish, Salad, etc.), a dish moment hierar-chy (related to when eating a dish, e.g. Snack, Starter,

Figure 3: Example of a WIKITAAABLE recipe.

Dessert, etc.), a location hierarchy (related to the originsof recipes, e.g. France, Asia, etc.), a diet hierarchy (relatedto food allowed or not for a specific diet, e.g Vegetarian,NutFree, etc.), an action hierarchy (related to cooking ac-tions used for preparing ingredients, toCut, toPeel, etc.). Inthe semantic wiki, each concept of a hierarchy is encoded asa category page Category : < conceptname >. For exam-ple, the concept of berry is encoded in the Category:Berry

page, as shown in Figure 2. Each concept is described bya short description, lexical variants (used by the annotationbot, for searching concepts in the full text of recipes), hissub-categories and super-categories. For berries, the wikipage indicates that Berry is a sub-concept of Fruit (corre-sponding to the Category : Fruit page), and sub-categoriesof Berry (e.g. Raspberry, Blueberry, etc.) are listed.

The set of recipes contained in WikiTaaable are those pro-vided by the contest, that have been semantically annotatedaccording to the domain ontology. Each recipe, as the onegiven in example in Figure 3, is encoded as a wiki page,composed of several sections: a title, which is the name ofthe recipe, an “Ingredients” section containing the list of in-gredients used in the recipe, each ingredient being linkedto its corresponding Category page in the food hierarchy,a “Textual Preparation” section describing the preparationprocess, some possible “substitutions” which are adaptationknowledge, and “other information” like the dish type, forexample.

2.3 Adaptation knowledgeImproving the current results of Taaable could be done byacquiring adaptation knowledge (AK). In CBR systems, us-ing AK is a classical approach for producing more fine grainadaptations [14]. In the Taaable/WikiTaaable context,an AK is a substitution of some ingredients by other ones(e.g. in“My Strawberry Pie”recipe, Strawberry could be re-placed by Raspberry). Formally, an adaptation knowledgeis a 4-tuple (context, replace, by, origin), where:

• context represents the recipe or the class of recipes onwhich the substitution can be applied. An AK is spe-cific if its context is a single recipe and generic if itscontext is a class of recipes (a specific type of dish, forexample). In this paper, only specific AK are consid-ered.

• replace and by are respectively the set of ingredientsthat must be replaced and the set of replacing ingre-dients.

• origin is the source the AK comes from. Currently,four sources have been identified:

1. Taaable, when AK results from a propositionof adaptation given by the reasoning process ofTaaable.

2. AK extractor, when AK results from a specificknowledge discovery system called “AK Extrac-tor” (which is detailed in the next section).

3. user, when AK is given by a user by editingthe wiki, as it is usually done in cooking website, when users add comments about ingredi-ent substitution on a recipe. See, for exam-ple, http://en.wikibooks.org/wiki/Cookbook:

substitutions.

4. recipe, when the AK is directly given by the orig-inal recipe when a choice between ingredients ismentioned (e.g. “100g butter or margarine”). Thisparticular substitutions are taken into account bya wiki bot which runs through the wiki for auto-matically extracting them.

According to this definition, (“My Strawberry Pie”, Strawber-ry,Raspberry,Taaable), is an AK obtained from Taaable,meaning that strawberries can be replaced by raspberries inthe “My Strawberry Pie” recipe. In WikiTaaable, eachsubstitution is encoded as a wiki page like the one givenin Figure 4. A semantic query is used to feed automati-cally the Substitutions section of a recipe page, as visiblein Figure 3.

Figure 4: Example of a substitution page.

2.4 Adaptation knowledge extractorSemi-automatic extraction of AK for adapting cookingrecipe has been studied in [10]. An operational sys-tem that proposes ingredient substitutions for adaptinga given recipe to additional constraints has been imple-mented. Figure 5 shows an example of results for adapt-ing the “My Strawberry Pie” recipe without strawberry. AKExtractor proposes a set of substitutions like replacingStrawberry and CoolWhip by Raspberry and FoodColor, orby Peach and LemonJuice, etc.

Figure 5: An illustration of the AK extractor inter-face

AK Extractor was built in order to acquire AK, takinginto account incompatibly of ingredients or ingredients oftenassociated in recipes, overcoming the weakness of Taaable.The AK Extractor is founded on a typical knowledge dis-covery in databases (KDD) process. The main steps of aKDD process are data selection and preparation, datamin-ing, and interpretation of the extracted units of information[9]. The step of interpretation requires usually an expert ofthe domain for validating the extracted units for giving thema status of knowledge (for cooking, many web users couldbe considered as experts). In traditional KDD approaches,the KDD process produces a huge amount of informationunits which can be turned into knowledge. Experts havethen to process these information units in order to validatethe ”good” ones, which become knowledge. This is a tedioustask for experts. In addition, they face situations where theycannot decide if an information unit is appropriate or notbecause they lack context information to decide. By con-trast, the AK approach we describe in this paper is interac-tive and opportunistic [5]. Users use the same interface forquerying the system and for validating knowledge. In addi-tion, they validate knowledge on the fly, in context, which ismore convenient than dealing with a huge amount of candi-date knowledge (information units) out of any context. AKExtractor is based on a comparison of ingredients betweenthe recipe to adapt, and a set of similar recipes. The sys-tem selects first recipes which have a minimal number ofingredients in common and a minimal number of differentingredients with the recipe that has to be adapted. Closeditemsets [11] are extracted from variations between recipes,starting from variations between each selected recipe of theprevious step and the recipe to adapt. The closed item-sets are then filtered and ranked with specific rules (which

are detailed in [10]). The system displays the propositionsfor ingredient substitutions coming from the first five betterranked closed itemsets. Finally, user can give feedback forvalidating or not some of these propositions.

2.5 ObjectivesIn a semantic wiki, knowledge is in continuous evolution.Usually, this evolution results from users that feed the wiki.The main objective of this work is to increase the acquisitionof AK by establishing a collaborative environment betweenusers and automatic processes supported by machines. Aninteresting point is that the reasoning system (Taaable) isnot only seen as the consumer of the wiki knowledge butalso as the producer of new knowledge that will be added tothe wiki.

Automatic processes and collective intelligence must be co-ordinated. The better machines and users will collaborate,the better the acquisition process succeed. Indeed, machinesare able to compute lot of things (for example, substitutionpropositions for Taaable and AK Extractor) but theresults cannot be considered as knowledge before they arevalidated as such by a human expert. Indeed, the crucialstep of the KDD process [9] is that the results produced bythe machine must be interpreted and validated by a (human)expert. So, users will be asked to play the role of the expertfor validating automatic results produced by machine and aproposition of substitution becomes an AK only when it isvalidated by a user.

The next section details our approach for enhancing thecollaboration between humans and machines, by pluggingtogether automatic systems (Taaable, WikiTaaable andAK Extractor) under the control of users.

Increasing the quantity and quality of AK in the wiki willimprove the results of the reasoning system. However, thisrequires a smooth modification of the reasoning process fortaking into account the AK for computing adaptation (thisis a short-term work). Besides, to ensure the non-regressionof the system, the impact of the continuously new AK pro-duced on the system results must be evaluated by test sets.In this paper, we focus on AK acquisition. A way for evalu-ating how knowledge evolution can be managed for improv-ing the results of a reasoning process is studied in [17].

3. COLLABORATIVE ENVIRONMENTFOR ACQUIRING AK FROM VARIOUSSOURCES

3.1 The approachCurrently, users are already involved in Taaable, Wiki-Taaable and AK Extractor systems when they use them,but these three systems are separated. If they were inte-grated, each could benefit from the others. Some interac-tions between these systems have to be developed. For ex-ample, Taaable and AK Extractor return substitutionpropositions, and users can validate these propositions as be-ing relevant or not. Each system has is own storage for tak-ing into account the user feedbacks, but there is no commonspace for storing together all the AK that can be produced.The architecture proposed in Figure 6 aims at plugging to-

Figure 6: Architecture for a man-machine AK ac-quisition in the cooking domain.

gether the three systems (WikiTaaable, Taaable and AKExtractor) and WikiTaaable will be used to store theresults of AK acquisition produced by humans as well asproduced by machines.

Machines and humans will work together for a common ob-jective which is AK acquisition. In our approach, users areessential for controlling and integrating AK produced byautomatic processes in WikiTaaable. Indeed, additionallyto their wiki editing, users will trigger automatic processesand validate their results. They play the role of cooking ex-pert when validating substitution propositions as AK beingstored in the wiki. Besides, different users can, in parallel,start Taaable, AK Extractor on a specific recipe page,or edit new substitution pages in the wiki. The new AKintegrated in the wiki will be used, at short term, by thereasoning process of Taaable. Thus, this loop ensures thecontinuous knowledge building and the improvement of theresults in Taaable.

Figure 6 shows the interaction between the different re-sources and their integration into a global system. In thissystem, a user can (1) directly edit a wiki substitution pagefor adding, removing, modifying AK.

When a user (2) queries Taaable, (3) Taaable computesadaptation propositions thanks to (4) knowledge stored inWikiTaaable. These propositions are presented to the userwho has (5) to evaluate whether a proposition is relevantor not. If the proposition is relevant (i.e. “OK” has beenactivated), this proposition becomes an AK which will au-tomatically (6) feed a new substitution page, which contextis the recipe concerned by the adaptation.

A similar process holds when a user (7) queries the AKExtractor, which (8) computes substitution propositions,submitted to the user (5) evaluation and which will (6) feedthe wiki with AK if relevant.

Finally, the possibility of (9) trigger the AK Extractor

for a given recipe is added in all recipe pages. In that case,the sequence (8,5,6) takes place again.

Even if the architecture is presented in a specific domainwith specific systems, this kind of organisation can be re-produced in various domain, because it is a generic organ-isation. Indeed, inference engines, KDD engines and wikiscan collaborate together under the control of users, who playa key role in the organisation.

3.2 Implementation of the architecture inTAAABLE/WIKITAAABLE

The proposed architecture has been instantiated to integrateTaaable feedback and the AK Extractor process in Wi-kiTaaable. Some screenshots are given to show how itlooks like in the wiki. Figures 7, 8, and 9 illustrate howthe AK Extractor process has been integrated as a wikiextension. Figure 7 shows the first step of the AK Extrac-tor process in which the user has to give constraints on therecipe to adapt. This interface is triggered by a special linkthat has been added on each recipe page. The interface givenin example was obtained from the“My Strawberry Pie”recipepage. The user enters constraints (e.g. −Strawberry meansthat the user does not want strawberries).

Figure 7: The AK Extractor query interface.

When clicking on “Compute adaptations”, the AK discoveryprocess is run. This process produces a wiki page with someAK propositions, as it is illustrated in Figure 8. The usermay validate some proposition(s) as being relevant by click-ing on “OK”. Optionally, some adjustments could be madethanks to the checkboxes: if not checked, an ingredient willnot take part of the substitution4. Figure 9 shows the pageresulting from the validation of the first proposition of sub-stitution. This page announces that a substitution page, likethe one presented in Figure 4, has been created and that thissubstitution will automatically appear in the recipe page asan AK, as illustrated in Figure 10.

4. RELATED WORKPrevious works deal with man-machine collaboration whereknowledge is obtained by a knowledge extraction processwhich is guided or/and validated by human. In this section,we present several systems based on KDD or on CBR oreven both that demonstrate this collaboration.

4the “not OK” button does not produce anything yet.

Figure 8: AK propositions computed by the AKExtractor.

4.1 KDD systemsThe KDD process requires to be supervised by an expert,who could interact at various levels. One of the most usualproblems in a KDD is to control the over-abundance of re-sults generated by the KDD process. The expert could, forexample, interact for better selecting the data that will bemined as it is described in [4] which proposes an approachfor optimising the formulation of the problem to solve. An-other approach consists in filtering and ranking of numerousresults obtained by the data mining algorithms. For exam-ple, [16] proposes subjective measures of interestingness forevaluating the datamining results. These measures dependon the user profile and a result is considered as interestingfor a user according to two major reasons unexpectedness(if the user is “surprised” by the results) and actionability(if the user can exploit the results). The paper focuses onunexpected results which are results in contradiction withbeliefs of the user. So, a result which may revise beliefs of auser is relevant.

Another usual problem of the KDD process is the selectionof relevant information units among the large set of infor-mation units produced, for transforming them into knowl-edge. Many approaches for taking into account this stepof the KDD process have been proposed. For example, [3]presents a methodology for KDD in the context of build-ing a semi-automatic ontology from heterogeneous textualresources. [3] uses formal concept analysis (FCA) [11] forclassifying objects according to their properties which areextracted from various textual resources (e.g thesaurus, full

Figure 9: Generating the AK substitution page.

texts, dictionaries, etc.). The results of the FCA process istranslated in description logics for representing the ontologyconcepts. Experts are involved at each step of the KDDprocess. For example, when the properties describing theobjects are produced by an automatic extraction process,experts have to validate and filter the most representativeproperties, i.e. that described the best the objects. At thelast step of the process, experts have to validate the formalconcepts that have been produced, by selecting those whichmakes sense in their domain.

The integration of the AK Extractor follows the sameprinciple. AK Extractor implements a KDD processwhich produces a set of substitution propositions. Thesesubstitutions have to be validated in order to be stored asAK.

4.2 CBR systemsCBR [15] is a method for solving new problems thanks toadaptation of previously solved problems. However, the stepof adaptation may fail. Consequently, CBR systems inte-grating users are built in order to repair irrelevant adapta-tions. The systems presented in the following involve user inan opportunistic way, the goal is to repair adaptations thathave failed, by acquiring AK or by increasing the domainknowledge (on which the reasoning is based) itself.

DIAL [13] focuses on an interactive acquisition of AK in thedomain of disaster response planning. When the system re-turns a solution that is inconsistent, the response planning is

Figure 10: “My Strawberry Pie” recipe after the ac-quisition of a new substitution.

returned with a description of the elements that need to beadjusted in the planning. For example, a response planningfor an earthquake in Los Angeles indicates that NationalGuard must be called. When this plan is used for an earth-quake in Indonesia, a problem arises because there is notNational Guard in Indonesia. So, the response plan mustbe adapted. DIAL is composed of three kinds of adaptationprocess:

• case-based adaptation: which reuses adaptations whichpreviously performed,

• rule-based adaptation: which determines elementsthat have to be adapted and the transformation toapply (e.g substitute an element) by the generation ofa generic rule. The generic rule is then instantiated bysearching relevant knowledge in the knowledge base.In the previous example, it consists in building a rulemeaning that national guard must be substituted, andinstantiate this rule by a substituting element searchedin the knowledge base,

• manual adaptation: from an interface, a user may se-lect a generic transformation to apply and navigate inthe knowledge base to search relevant knowledge forinstantiating the generic transformation. This thirdadaptation method is a man-machine collaboration forAK acquisition triggered opportunistically. Indeed, incase of failure of the case-based and rule-based adap-tations, the system proposes the user to guide theadaptation process. After being solved, the AK is

memorised. An AK corresponds to the transforma-tion applied (e.g substitute an element) with the linksin the knowledge base toward relevant knowledge usesto make the transformation.

In DIAL, adaptation is manually transformed if automaticadaptations fail. Conversely, WebAdapt [12] proposes twoindependent adaptations modes: one automatic and onemanual. WebAdapt is a system for personalising adapta-tions based on learned information about user preferences.WebAdapt is used in the domain of sightseeing itineraryplanning. The system adapts a proposed itinerary, corre-sponding to a list of locations, with one of these two inde-pendent adaptation modes. Either, the user of WebAdaptindicates a location to substitute and if the location mustbe substitute by criteria of similarity or criteria of nearness.Then, WebAdapt computes an automatic adaptation with-out intervention of the user. Either, the itinerary proposedby the process is customized by user preferences. The userselects constraints (e.g. “I would like to visit building of the16th century”) in order to adapt locations proposed by thesystem.

The FRAKAS system [5] is also such an opportunistic sys-tem, which integrates interactions with an expert during thereasoning for acquiring missing domain knowledge and en-sure a better adaptation. The adaptation proposed by thesystem is evaluated by an expert. From an interface, the ex-pert may detect some inconsistencies between the proposedadaptation and its own knowledge. If an inconsistency isdetected, the adaptation failed. At this moment, user com-pletes the domain knowledge by selecting incompatible prob-lem variables and/or solutions variables. Adapted cases anddomain knowledge are acquired at the same time during theinteractive part between the expert and the system.

Like [6], [2] uses an opportunistic approach for AK acquisi-tion. In the second version of Taaable, which implementsthe approach proposed in [2], users may give some feedbackon substitutions for adapting recipes. If a proposition of sub-stitution is judged irrelevant by the user, an interface allowsto guide the system for repairing the adaptation. The usermay indicate that some ingredient(s) is/are missing whenadding an ingredient, or that some ingredients of the recipeare not compatible with an ingredient that must be added.In the case where is ingredient(s) missing, a system of AKacquisition, called Cabamaka [8] based on KDD, is triggered.This last step allows to repair the bad adaptation and mem-orise the AK. The AK is a rule composed of ingredient(s)that have to be removed and ingredient(s) that have to beadded, similar to a substitution AK used in WikiTaaable.

In each of the previous systems, knowledge acquisition istriggered when an adaptation failed. The originality of ap-proach is that AK can be triggered in parallel of the adap-tation process and that this knowledge can be acquired atany time in a semantic wiki collaborative space.

5. CONCLUSIONThis paper proposes an architecture for improving the man-machine collaboration for collecting AK for a CBR sys-tem. Automatic processes are integrated under the con-trol of users for feeding knowledge in a semantic wiki. In

the specific context of a CBR system, this approach allowsto better collect adaptation knowledge. The CBR engine,which was initially only a consumer of the wiki knowledge,has been turned as a producer as well. Other tools, andespecially knowledge discovery ones, can be plugged as wikiextension for helping users to feed the wiki. A single inter-face is used for viewing, querying, enriching and correctingthe knowledge. Moreover, as it is a collaborative wiki inter-face, everyone will benefit from the experience of the othermembers of the community.

As our approach produces specific AK (i.e. for adaptinga specific recipe), an extension of this work concerns thestudy and the development of a KDD process for extractinggeneric adaptations (i.e. for a set of recipes, for a given dishtype, for example). Another short-term work concerns theexploitation of irrelevant adaptation propositions. It wasshown that when a user validates a adaptation propositionas being relevant, this adaptation becomes an AK and isstored in the wiki. However, when a proposition is irrele-vant (i.e. the user disagrees with an automatic adaptation),nothing is currently done for collecting them nor for exploit-ing them. Some specific interactions with the user could bedeveloped for integrating a process for dealing with rejectedadaptations proposed by Taaable or AK Extractor.

6. ACKNOWLEDGEMENTSThis work is supported by French National Agency for Re-search (ANR), program Contint 2011. More informationabout Kolflow is available on the project website: http:

//kolflow.univ-nantes.fr/.

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