In case-based reasoning, the adaptation step depends in general on
domain-dependent knowledge, which motivates studies on adaptation knowledge
acquisition (AKA). CABAMAKA is an AKA system based on principles of knowledge
discovery from databases. This system explores the variations within the case
base to elicit adaptation knowledge. It has been successfully tested in an
application of case-based decision support to breast cancer treatment

Badra, Fadi

D'Aquin, Mathieu

Lafrogne, Sandrine

Lieber, Jean

Napoli, Amedeo

Szathmary, Laszlo

English

arXiv

In case-based reasoning, the adaptation step depends in general on domain-dependent knowledge, which motivates studies on adaptation knowledge acquisition (AKA). CABAMAKA is an AKA system based on principles of knowledge discovery from databases. This system explores the variations within the case base to elicit adaptation knowledge. It has been successfully tested in an application of case-based decision support to breast cancer treatment

d'Aquin, Mathieu

INRIA a CCSD electronic archive server

HAL Id: hal-00110287https://hal.inria.fr/hal-00110287Submitted on 27 Oct 2006HAL is a multi-disciplinary open accessarchive for the deposit and dissemination of sci-entific research documents, whether they are pub-lished or not. The documents may come fromteaching and research institutions in France orabroad, or from public or private research centers.L’archive ouverte pluridisciplinaire HAL, estdestinée au dépôt et à la diffusion de documentsscientifiques de niveau recherche, publiés ou non,émanant des établissements d’enseignement et derecherche français ou étrangers, des laboratoirespublics ou privés.Adaptation Knowledge Discovery from a Case BaseMathieu d’Aquin, Fadi Badra, Sandrine Lafrogne, Jean Lieber, AmedeoNapoli, Laszlo SzathmaryTo cite this version:Mathieu d’Aquin, Fadi Badra, Sandrine Lafrogne, Jean Lieber, Amedeo Napoli, et al.. AdaptationKnowledge Discovery from a Case Base. 2006, pp.795–796. ￿hal-00110287￿hal-00110287, version 1 - 27 Oct 2006Adaptation Knowledge Discovery from a Case BaseM. d’Aquin1 and F. Badra1 and S. Lafrogne1 and J. Lieber1 and A. Napoli1 and L. Szathmary1Abstract. In case-based reasoning, the adaptation step depends ingeneral on domain-dependent knowledge, which motivates studieson adaptation knowledge acquisition (AKA ). CABAMAK A is anAKAsystem based on principles of knowledge discovery from databases.This system explores the variations within the case base to elicitadaptation knowledge. It has been successfully tested in anappli-cation of case-based decision support to breast cancer treatment.1 INTRODUCTIONCase-based reasoning (CBR [4]) aims at solving a target problemthanks to a case base. A case represents a previously solved prob-lem. A CBR system selects a case from the case base and then adaptsthe associated solution, requiring domain-dependent knowledge foradaptation. The goal of adaptation knowledge acquisition (AKA ) is toextract this knowledge. The system CABAMAK A applies principlesof knowledge discovery from databases (KDD) to AKA . The origi-nality of CABAMAK A lies essentially in the approach toAKA thatuses a powerful learning technique that is guided by a domainex-pert, according to the spirit ofKDD. This paper proposes an originaland working approach toAKA , based onKDD techniques.CBR and adaptation. A case in a givenCBR application is usuallyrepresented by a pair(pb, Sol(pb)) wherepb represents a problemstatement andSol(pb), a solution ofpb. CBR relies on thesourcecases(srce, Sol(srce)) that constitute thecase baseCB. In a par-ticular CBR session, the problem to be solved is calledtarget prob-lem, denoted bytgt. A case-based inference associates totg a so-lution Sol(tgt), with respect to the case baseCB and to additionalknowledge bases, in particularO, thedomain ontologythat usuallyintroduces the concepts and terms used to represent the cases.A classical decomposition ofCBR consists in the steps of retrievaland adaptation.Retrievalselects(srce, Sol(srce)) ∈ CB such thatsrce is judged to be similar totgt. The goal of adaptation is to solvetgt by modifyingSol(srce) accordingly.The work presented hereafter is based on the following modelfadaptation, similar totransformational analogy[1]:➀ (srce, tgt) 7→ ∆pb, where∆pb encodes the similarities anddissimilarities of the problemsrce andtgt.➁ (∆pb, AK) 7→ ∆sol, where AK is the adaptation knowledgeand where∆sol encodes the similarities and dissimilarities ofSol(srce) and the forthcomingSol(tgt).➂ (Sol(srce), ∆sol) 7→ Sol(tgt), Sol(srce) is modified intoSol(tgt) according to∆sol.Adaptation is generally supposed to be domain-dependent inthesense that it relies on domain-specific adaptation knowledge. There-1 LORIA (UMR 7503 CNRS–INPL–INRIA-Nancy 2–UHP), BP 239, 54506Vandœuvre-lès-Nancy, FRANCEfore, this knowledge has to be acquired. This is the purpose of adap-tation knowledge acquisition(AKA ).A related work in AKA. The idea of the research pre-sented in [3] is to exploit the variations between source cases tolearn adaptation rules. These rules compute variations on solu-tions from variations on problems. More precisely, orderedpairs(srce-case1, srce-case2) of similar source cases are formed.Then, for each of these pairs, the variations between the problemssrce1 and srce2 and the solutionsSol(srce1) and Sol(srce2)are represented (∆pb and ∆sol). Finally, the adaptation rulesare learned, using as training set the set of the input-output pairs(∆pb, ∆sol). The experiments have shown that theCBR system us-ing the adaptation knowledge acquired from the automatic systemof AKA shows a better performance compared to theCBR systemworking without adaptation. This research has strongly influencedour work that is globally based on similar ideas.2 CABAMAKAPrinciples. CABAMAK A deals with case base mining for AKA .Although the main ideas underlying CABAMAK A are shared withthose presented in [3], the followings are original ones. The adap-tation knowledge that is mined has to be validated by expertsandhas to be associated with explanations that make it understand-able by the user. In this way, CABAMAK A may be considered asa semi-automated (or interactive) learning system. Another differ-ence with [3] lies in the volume of the cases that are examined:given a case baseCB where |CB| = n, the CABAMAK A systemtakes into account every ordered pair(s ce-case1, srce-case2)with srce-case1 6= srce-case2 (whereas in [3], only the pairs ofsimilar source cases are considered, according to a fixed criterion).Thus, the CABAMAK A system has to cope withn(n − 1) pairs, arather large number of elements, since in our applicationn ≃ 750.(n(n − 1) ≃ 5 · 105). This is why efficient techniques of knowl-edge discovery from databases (KDD [2]) have been chosen for thissystem.Principles of KDD. The goal ofKDD is to discover knowledgefrom databases, with the supervision of an analyst (expert of the do-main). A KDD session usually relies on three main steps: data prepa-ration, data-mining and interpretation.Data preparationis based on formatting and filtering operations.The formatting operations transform the data into a form allowingthe application of the chosen data-mining operations. The filteringoperations are used for removing noisy data and for focusingthedata-mining operation on special subsets of objects and/orattributes.Data-miningmethods are applied to extract pieces of informa-tion from the data. These pieces of information have some regularproperties allowing their extraction. For example, CHARM [5] is adata-mining algorithm that performs efficiently the extracion of fre-quent closed itemsets(FCIs). CHARM inputs a database in the formof a set of transactions, eachtransactionT being a set of booleanproperties oritems. An itemsetI is a set of items. The support ofI , support(I), is the proportion of transactionsT of the databasepossessingI (I ⊆ T ). I is frequent, with respect to a thresholdσ ∈ [0; 1], wheneversupport(I) ≥ σ. I is closed if it has no propersupersetJ (I ( J) with the same support.Interpretationaims at interpretating the output of data-mining i.e.the FCIs in the present case, with the help of an analyst. In this way,the interpretation step produces new knowledge units (e.g.rules).Formatting. The formatting step of CABAMAK A inputs thecase baseCB and outputs a set of transactions obtained from thepairs(srce-case1, srce-case2). It is composed of two substeps.During the first substep, eachsrce-case = (srce, Sol(srce)) ∈CB is formatted in two sets of boolean properties:Φ(srce) andΦ(Sol(srce)). The computation ofΦ(srce) consists in translatingsrce from the problem representation formalism to2P , P being aset of boolean properties. Possibly, some information may be lostduring this translation, but this loss has to be minimized. Now, thistranslation formats an expressionsrce expressed in the frameworkof the domain ontologyO to an expressionΦ(srce) that will be ma-nipulated as data, i.e. without the use of a reasoning process. There-fore, in order to minimize the translation loss, it is assumed that ifp ∈ Φ(srce) andp entailsq (givenO) thenq ∈ Φ(srce). In otherwords,Φ(srce) is assumed to be deductively closed givenO in thesetP . The same assumption is made forΦ(Sol(srce)). How thisfirst substep of formatting is computed in practice depends heavilyon the representation formalism of the cases.The second substep of formatting produces a transactionT =Φ((srce-case1, srce-case2)) for each ordered pair of distinctsource cases, based on the sets of itemsΦ( rce1), Φ(srce2),Φ(Sol(srce1)) andΦ(Sol(srce2)). Following the model of adap-tation presented in introduction (items➀, ➁ and➂), T has to encodethe properties of∆pb and∆sol. ∆pb encodes the similarities anddissimilarities ofsrce1 andsrce2, i.e.:• The properties common tosrce1 andsrce2 (marked by “=”),• The properties ofsrce1 thatsrce2 does not share (“-”) and• The properties ofsrce2 thatsrce1 does not share (“+”).All these properties are related to problems and thus are marked bypb. ∆sol is computed in a similar way andΦ(T ) = ∆pb ∪ ∆sol.For example,if{Φ(srce1) = {a, b, c} Φ(Sol(srce1)) = {A, B}Φ(srce2) = {b, c, d} Φ(Sol(srce2)) = {B, C}then T ={a-pb, b=pb, c=pb, d+pb, A-sol, B=sol, C+sol}(1)Mining. The extraction of FCIs is computed thanks toCHARM (in fact, thanks to a tool based on a CHARM-likealgorithm) from the set of transactions. A transactionT =Φ((srce-case1, srce-case2)) encodes a specific adaptation((srce1, Sol(srce1)), srce2) 7→ Sol(srce2). An FCI extractedmay be considered as a generalization of a set of transactions. Forexample, ifIex ={a-pb, c=pb, d+pb, A-sol, B=sol, C+sol}is an FCI,Iex isa generalization of a subset of the transactions including the transac-tion T of equation (1):Iex ⊆ T . The interpretation of this FCI as anadaptation rule is explained below.Interpretation. The interpretation step is supervised by the ana-lyst. The CABAMAK A system provides the analyst with the extractedFCIs and facilities for navigating among them. The analyst may se-lect an FCI, sayI , and interpretI as an adaptation rule. For example,the FCIIex may be interpreted in the following terms:if a is a property ofsrce but is not a property oftgt,c is a property of bothsrce andtgt,d is not a property ofsrce but is a property oftgt,A andB are properties ofSol(srce) andC is not a property ofSol(srce)then the properties ofSol(tgt) areΦ(Sol(tgt)) = (Φ(Sol(srce)) \ {A}) ∪ {C}.This has to be translated as an adaptation ruler of the CBR system.Then the analyst correctsr and associates an explanation with it.Implementation. The application domain of theCBR system weare developing is breast cancer treatment: in this application, a prob-lempb describes a class of patients with a set of attributes and asso-ciated constraints (holding on the age of the patient, the siz and thelocalization of the tumor, etc.). A solutionSol(pb) of pb is a set oftherapeutic decisions (in surgery, chemotherapy, etc.). The requestedbehavior of theCBR system is to provide a treatment and explana-tions on this treatment proposal. This is why the analyst is requiredto associate an explanation to a discovered adaptation rule.The problems, solutions and the domain ontology of the applica-tion are represented in OWL DL (recommendation of the W3C).3 CONCLUSIONThe CABAMAK A system presented in this paper is inspired by theresearch presented in [3] and by the principles ofKDD for the pur-pose of semi-automatic adaptation knowledge discovery. Ithas en-abled to discover several useful adaptation rules for a medical CBRapplication. It has been designed to be reusable for otherCBR appli-cations: only a few modules of CABAMAK A are dependent on theformalism of the cases and of the domain ontology, and this formal-ism, OWL DL, is a well-known standard. One element of future workconsists in searching for ways of simplifying the presentation of thenumerous extracted FCIs to the analyst. This involves an organiza-tion of these FCIs for the purpose of navigation among them. Suchan organization can be a hierarchy of FCIs according to theirspeci-ficities or a clustering of the FCIs in themes.REFERENCES[1] J. G. Carbonell, ‘Learning by analogy: Formulating and generalizingplans from past experience’, inMachine Learning, An Artificial Intel-ligence Approach, ed., R. S. Michalski and J. G. Carbonell and T. M.Mitchell, chapter 5, 137–161, Morgan Kaufmann, Inc., (1983).[2] M.H. Dunham,Data Mining – Introductory and Advanced Topics, Pren-tice Hall, Upper Saddle River, NJ, 2003.[3] K. Hanney and M. T. Keane, ‘Learning Adaptation Rules From a Case-Base’, inAdvances in Case-Based Reasoning – Third European Work-shop, EWCBR’96, eds., I. Smith and B. Faltings, LNAI 1168, pp. 179–192. Springer Verlag, Berlin, (1996).[4] C. K. Riesbeck and R. C. Schank,Inside Case-Based Reasoning,Lawrence Erlbaum Associates, Inc., Hillsdale, New Jersey,1989.[5] M. J. Zaki and C.-J. Hsiao, ‘CHARM: An Efficient Algorithmfor ClosedItemset Mining’, in SIAM International Conference on Data MiningSDM’02, pp. 33–43, (Apr 2002).

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