Colloque avec actes et comité de lecture. internationale.International audienceThis paper presents a knowledge-based system for land use interpretation and prediction. We describe our needs for representing knowledge and data, and for reasoning. We explain our choices : case-based reasoning within the framework of the description logic system RACER. Then, we present the knowledge base and the data we are working with. Data about spatial entities are represented as graphs and represented in the DL system accordingly. An example of graph manipulation is used to illustrate our purpose. Then, we propose a first synthesis of our research work and present an extension of the DL system necessary for going further

Le Ber, Florence

Metzger, Jean-Luc

Napoli, Amedeo

INRIA a CCSD electronic archive server

HAL Id: inria-00107549https://hal.inria.fr/inria-00107549Submitted on 19 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.Using DL for a Case-Based Explanation SystemJean-Luc Metzger, Florence Le Ber, Amedeo NapoliTo cite this version:Jean-Luc Metzger, Florence Le Ber, Amedeo Napoli. Using DL for a Case-Based Explanation System.International Workshop on Description Logics - DL’2002, 2002, Toulouse, France, pp.203-210. ￿inria-00107549￿Using DL for a Case-Based Explanation SystemJ.-L. Metzger, F. Le Ber, A. NapoliLORIA - B.P. 23954506 Vandœuvre-lès-NancyFranceEmail: {metzger, leber, napoli}@loria.frAbstractThis paper presents a knowledge-based system for land use interpre-tation and prediction. We describe our needs for representing knowledgeand data, and for reasoning. We explain our choices : case-based reason-ing within the framework of the description logic system RACER. Then,we present the knowledge base and the data we are working with. Dataabout spatial entities are represented as graphs and represented in the DLsystem accordingly. An example of graph manipulation is used to illus-trate our purpose. Then, we propose a first synthesis of our research workand present an extension of the DL system necessary for going further.1 MotivationWe are developing a knowledge-based system aimed at helping agronomists toanalyze the management of farm territory, and to propose models about theevolution of farm territories and farmer practices. From the agronomist point ofview, there are relationships between the farm management and the farm spatialorganization. One of our goals is to build a system, named ROSA for Reasoningon Organization of Space in Agriculture, based on this hypothesis.Section 2 describes the architecture we plan to use for the system ROSA andexplains why we have chosen the description logic system RACER [7]. Section 3presents the real-world data on which we are working, while section 4 describeshow knowledge and data about farms are used for reasoning. In conclusion wepoint out the extensions on which we are working for fulfilling our needs.2 A Case-Based Explanation SystemOur research work mainly relies on case-based reasoning [11] and qualitative spa-tial reasoning [15] and especially reasoning with description logics (DL) [6, 14].Case-based reasoning (CBR) is based on the use of past experiences called casesthat are pairs (problem, solution) [12]. Our objective is to provide an explana-tion about the farm functioning while knowing its spatial organization. Thus,we consider that a problem is a farm management, a solution is an explanationon farm management. The explanation is based on the knowledge about thespatial organization of the farm.A CBR system aims at solving a target problem denoted by tgt by means of acase base which is a finite set of cases. A case from the case base is called sourcecase and denoted by srce-case = (srce, Sol(srce)). A source problem srceis a problem such that (srce, Sol(srce)) belongs to the case base, i.e. it is aproblem for which a solution Sol(srce) is known. CBR is based on three mainoperations: retrieval, adaptation and storage. The goal of retrieval is to find acase srce-case in the case base similar to the target problem tgt. Adaptationuses this retrieved case srce-case in order to build a solution Sol(tgt) to tgt.If the new case is of interest, it is stored in the case base.After an early work on the use of object-based representation system anddescription logics system [13], we have decided to choose a DL system andespecially the RACER system [7]. The expressivity of RACER, its well-definedsemantics, the capability of dealing with concepts as well as with individualsand the efficiency of the reasoner are the main reasons of our choice. DLs havebeen used previously in CBR [5, 8, 9]. All these works argue on the capabilitiesof DLs with respect to the need of CBR systems. With their formal semanticsand their ability to classify concepts and to recognize instances, DLs are wellsuited for managing knowledge and cases bases.The knowledge base of our system is composed of domain knowledge andcases. Domain knowledge is used for enhancing the CBR operations. In partic-ular, in our CBR system, we want to reason on domain knowledge and cases forclassifying spatial structures and spatial relations [10]. Like in [5] or [8], domainknowledge is modeled in the TBox, while cases are described as individuals inthe ABox. In RACER, we can take advantage of consistency checking (for theTBox) and classification (for querying the ABox).The architecture of our system is shown in figure 1. It is based on an interfacelayer and a reasoning layer. The interface is designed to manage bases, forintroducing new spatial description of farms and for displaying results. Thereasoning layer is based on RACER to retrieve cases and adapt explanations.     	           "!  	#"   $   %&%"' ('    )    * &''#+   ')  ", )  )   -%    ." "/ #"       "0       *    	)   1&2345   &  *	6  ", ) *7 ."Figure 1: Architecture of our system ROSA.3 Data and Domain KnowledgeIn our system, the knowledge base contains the description of concepts aboutagronomy, land use and spatial relations. Concepts are organized in a hierarchythat is partially shown in figure 2.Graphs are the base of our model for representing cases of farm spatial or-ganization and farm functioning knowledge. Moreover, they are well-suited forrepresenting complex real-world objects and for information sharing with do-main experts and farmers. Our model is also inspired from the conceptual graphtheory [3].Graphs are composed of vertices and edges. Our graphs are built with twokinds of vertices : entities and links. An entity is an agronomic spatial objectthat relies on a concept. It can be qualified by different attributes. A link is areification of a spatial relation. Like entities, links can be qualified by differentattributes. Edges that connect entities to links are labeled by the qualificationof entities in links. One graph represents the spatial organization about onefarm. The farm functioning is described by explanations associated with partsof the graph. Hence, the explanation about a farm can be seen as a ”sum” ofall explanations associated to the graph.The figure 3 shows two examples of parts of graphs from two instances ofreal-world graphs describing spatial organizations. These two graphs will beused in this article to explain our work.For example, the explanation associated with the first graph GrA of the fig-ure 3 is :”the farmer has let a strip of grass (the meadow) to protect cerealsfrom humidity, shadow and wild animals induced by the wood.” 	 	     	! " 	   	 # 	    	 $&%  ' (  )*+ , 	 	-.*" /	0 1&  2 3	  14, 	  *56 +"4, 	 7 *8	   %   %  	( 	 *,  	 	 *963 %  : ,$   7 	8	 56 +"	 " 377  **  ,Figure 2: Hierarchy of concepts divided in two sub-hierarchies issued from theagronomy domain and the spatial knowledge.;<6<6=> ? @ <A BC D&EF!<G6GD6FHC D6=> F!DI D6BA >JKDB7=6<;L>? @ <A BC D? @ <A BC D6=LM I <J? @ <A BC D6=LM I <J;<6<6=6N F!DI D6BA NO6<PBA <G!O>O6<&B6A <GO O7<&BA <GOQ I RQ I SFigure 3: graphs representing some spatial organization of two farms A and B.With the second graph GrB the explanation associated is:”The wood is a constraint because there are humidity and shadowthat are not good for cereals growth.”Finally a case is a graph that describes part of the spatial organization of afarm associated with an explanation about its functioning.4 Reasoning about spatial organizationActually, explanations associated with a graph is given in natural language sen-tences by agronomists. Thus, when a new explanation has to be attached to agraph, it has to be coherent with explanations attached with graphs of the samekind. The validation of explanation is done by comparing different farms forextracting their differences and similarities concerning spatial organization.To explain a new case, i.e. a given new farm described by its spatial orga-nization in a graph, the CBR system tries to associate explanations to parts of   	                    	         ! Figure 4: New graph GrA’ obtained by modification of the first one in figure 3.the graph. This is done with the help of a number of cases describing particularfarms whose functioning is well understood.The previous operations are based on the capabilities of comparing graphs.Two graphs, to be compared, have to be transformed in another one, i.e. akind of ”least common subsumer” of the two graphs [4, 1]. Three operations areallowed for transforming graphs : deletion, addition and substitution of vertices.Deletion or addition are based on specific rules of transformation based on do-main knowledge. These rules are mostly given by the knowledge on relationsaccording to the kind of vertices linked together. The substitution of a vertex isbased on the classification mechanism : an instance can be substituted by theconcept it belongs to; a concept can be substituted by its direct subsumer (inour case, at present, the concept of an instance and the subsumer of a conceptare unique).The ”path” storing the transformation operations from one graph to anotheris a similarity path, used in case-base reasoning [12]. The similarity is computedwith the help of an edition distance [2], giving the sum of costs of each trans-formations applied. Edition distance and similarity path are well adapted toour needs since they can be used in the adaptation process. [5] proposes thisapproach, but we need to extend it to deal with graphs.For example, we describe the comparison of the two graphs of the figure 3.These graphs are described in the ABox. First, each vertex is classified accordingto the concepts hierarchy. Then, these graphs are transformed by generalizationof their vertices. In the graph GrA, the vertex meadow1 can be substituted by theconcept Meadow and then by Agricultural-Land. In the same way, the conceptof the vertex cereal1 is Cereal and can be changed by Cropland. The relationisolate&connected1 can be substituted by Isolate&connected and then bythe Between&Connected relation. Finally, the instance wood1 is subsituted bythe concept Wood and the graph GrA is generalized into the graph GrA’, as shownfigure 4.In this particular case, we also know that cereal1 and meadow1 belong tothe same farm, and that they are connected. Then, cereal1 (generalized intoCropland) and meadow1 (generalized into Agricultural-Land) are merge intoa single vertex of type Agricultural-Land. Finally, beacause of the spatial  	   	  	       	  	Figure 5: New graph GrG of the graph GrA’ obtain by topological inference.   ! "$#&%&'&%&( )&* + ,)&- + .&'&/021&3&4 3576 8&92:&9&1<;>=&3&9&3& :&4 6 ?:5@6 8&9AB #&%&'&%&( )C* + ,)&- + .&'&/Figure 6: The similarity-path between graphs GrA and GrB.position of meadow1 and wood1, it can be deduced that the EC relation holdbetween these two instances. Thus, the graph GrA’ can be transformed againwith three other modifications (two substitutions and a deletion) to obtain themore general graph GrG shown in figure 5.The second graph GrB of the figure 3 can be transformed into a more generalone by substituting cereal2 with the more general concept Agricultural-Land,by substituting wood2 with the concept Wood and by changing the relationgo-Along1 to EC. The result is the same graph GrG described in figure 5.In this example, the sequence of operations used to transform the graphsGrA and GrB into a more general graph GrG defines a similarity-path with seventransformations (figure 6). For each transformation, a cost based on the editiondistance between the two graphs can be defined. The longer is the distancebetween the two graphs, more work has to be done for adapting explanations.Finally, the agronomists validate the fact that an adapted explanation makessense or not.5 ConclusionWe have given in this paper a first presentation of a system aimed at givingexplanations on farm functioning for helping agronomists in the interpretationof land use. The system is based both on classification and case-based reasoning.There are a knowledge base and a case base. We have chosen the RACER DLfor representing knowledge and reasoning. RACER DL is well suited for the con-struction of complex queries to retrieve instances. Reasoning mechanisms basedon classification and consistency checking are powerful tools for the maintenanceof the knowledge base. However more work remains to be done, especially withthe graph transformations to ensure the existence to a least common subsumerof two graphs.Our aim is to develop functionalities for the manipulation of graphs inRACER. For the moment we can deal with relations, i.e. a small graph withthree or four vertices. But we need to compare larger graphs and define a clas-sification mechanism for graphs. Such a functionality could be reused in manyapplications concerning spatial reasoning and other real-world tasks.References[1] F. Baader, R. Küsters, and R. Molitor. Computing least common subsumersin description logics with existential restrictions. In T. Dean, editor, Pro-ceedings of the 16th International Joint Conference on Artificial Intelligence(IJCAI’99), pages 96–101. Morgan Kaufmann, 1999.[2] H. Bunke and B.T. Messmer. Similarity measures for structured represen-tations. In M.M. Richter, S. Wess, K.-D. Althoff, and F. Maurer, editors,ewcbr93, pages 26–31, 1993.[3] M. Chein and M.-L. Mugnier. Conceptual graphs: Fundamental notions.Revue d’intelligence artificielle, 6(4):365–406, 1992.[4] William W. Cohen, Alex Borgida, and Haym Hirsh. Computing leastcommon subsumers in description logics. In Paul Rosenbloom and PeterSzolovits, editors, Proceedings of the Tenth National Conference on Artifi-cial Intelligence, pages 754–761, Menlo Park, California, 1993. AAAI Press.[5] P. A. González-Calero, B. Dı́az-Agudo, and M. Gómez-Albarrán. ApplyingDLs for retrieval in case-based reasoning. In Description Logics, pages 51–55, july 1999.[6] V. Haarslev, C. Lutz, and R. Möller. Foundations of spatioterminologicalreasoning with description logics. In A.G. Cohn, L. Schubert, and S.C.Shapiro, editors, KR’98: Principles of Knowledge Representation and Rea-soning, pages 112–123. Morgan Kaufmann, San Francisco, California, 1998.[7] V. Haarslev and R. Möller. Racer system description. In R. Goré,A. Leitsch, and T. Nipkow, editors, IJCAR, volume 2083 of Lecture Notes inComputer Science, pages 701–706, Siena, Italy, june 18-23 2001. Springer.[8] G. Kamp. Using description logics for knowledge intensive case-based rea-soning. In B. Faltings and I. Smith, editors, Third European Workshop onCase-Based Reasoning (EWCBR’96), Lausanne, Switzerland, pages 204–218. Springer-Verlag, Berlin, 1996.[9] Jana Koehler. An application of terminological logics to case-based reason-ing. In J. Doyle, E. Sandewall, and P. Torasso, editors, KR’94: Principlesof Knowledge Representation and Reasoning, pages 351–362. Morgan Kauf-mann, 1994.[10] F. Le Ber, L. Mangelinck, and A. Napoli. Design and composition of latticesof topological relations for spatial representation and reasoning. Rapportde Recherche RR 4321, INRIA, 2001.[11] D. B. Leake, editor. Case-Based Reasoning. Experiences, Lessons and Fu-ture Direction. MIT Press, 1996.[12] J. Lieber and A. Napoli. Correct and complete retrieval for case-basedproblem-solving. In ECAI’98, pages 68–72, 1998.[13] J.-L. Metzger. Acquisition, modélisation et représentation de connaissancespour le raisonnement à partir de cas : Application à l’étude d’organisationsspatiales agricoles. Mémoire de dea informatique, Université Henri Poincaré- Nancy I, juillet 2000.[14] R. Möller and M. Wessel. Terminological default reasoning about spatialinformation: A first step. In Spatial Information Theory, pages 189–204,1999.[15] L. Vieu. In Spatial and Temporal Reasoning, chapter Spatial representationand reasoning in artificial intelligence, pages 5–41. Kluwer Academic, 1997.

Using DL for a Case-Based Explanation System

Hal-Diderot

HAL Descartes

https://hal.inria.fr/inria-00107549/document

Using DL for a Case-Based Explanation System

Abstract

Similar works

Full text

Available Versions

INRIA a CCSD electronic archive server

Hal-Diderot

HAL Descartes