International journals

• Case-Based Prediction -- a Survey. F. Badra, and M. J. Lesot (2023)
  in International Journal of Approximate Reasoning 158: 108920.
  abstract | paper | bibtex

  @article{DBLP:journals/ijar/BadraL23,
  	author       = {Fadi Badra and Marie{-}Jeanne Lesot},
  	title        = {Case-based prediction - {A} survey},
  	journal      = {Int. J. Approx. Reason.},
  	volume       = {158},
  	pages        = {108920},
  	year         = {2023},
  	url          = {https://doi.org/10.1016/j.ijar.2023.108920},
  	doi          = {10.1016/j.ijar.2023.108920},
  	timestamp    = {Fri, 02 Jun 2023 21:22:50 +0200},
  	biburl       = {https://dblp.org/rec/journals/ijar/BadraL23.bib},
  	bibsource    = {dblp computer science bibliography, https://dblp.org}
  	}
  								

  This paper clarifies the relation between case-based prediction and analogical transfer. Case-based prediction consists in predicting the outcome associated with a new case directly from its comparison with a set of cases retrieved from a case base, by relying solely on a structured memory and some similarity measures. Analogical transfer is a cognitive process that allows to derive some new information about a target situation by applying a plausible inference principle, according to which if two situations are similar with respect to some criteria, then it is plausible that they are also similar with respect to other criteria. Case-based prediction algorithms are known to apply analogical transfer to make predictions, but the existing approaches are diverse, and developing a unified theory of case-based prediction remains a challenge. In this paper, we show that a common principle underlying case-based prediction methods is that they interpret the plausible inference as a transfer of similarity knowledge from a situation space to an outcome space. Among all potential outcomes, the predicted outcome is the one that optimizes this transfer, \emph{i.e.}, that makes the similarities in the outcome space most compatible with the observed similarities in the situation space. Based on this observation, a systematic analysis of the different theories of case-based prediction is presented, where the approaches are distinguished according to the type of knowledge used to measure the compatibility between the two sets of similarity relations.

• Modeling adaptation of breast cancer treatment decision protocols in the Kasimir project. J. Lieber, M. d'Aquin, F. Badra and A. Napoli (2008)
  in Applied Intelligence, 28(3):261--274.
  abstract | paper | bibtex

  	@article{apin-LieberdBN08,
  	  author    = {J. Lieber and M. d'Aquin and F. Badra and A. Napoli},
  	  title     = {Modeling adaptation of breast cancer treatment decision
  	               protocols in the Kasimir project},
  	  journal   = {Applied Intelligence},
  	  volume    = {28},
  	  number    = {3},
  	  year      = {2008},
  	  pages     = {261-274}
  	}
  				

  Medical decision protocols constitute theories for health-care decision making that are applicable for “ standard ” medical cases but have to be adapted for the other cases. This holds in particular for the breast cancer treatment protocol studied in the Kasimir research project. Protocol adaptations can be seen as knowledge-intensive case-based decision support processes. Some examples of adaptations that have been performed by oncologists are presented in this paper. Several issues are then identified that need to be addressed while trying to model such processes, namely: the complexity of adaptations, the lack of relevant information about the patient, the necessity to take into account the applicability and the consequences of a decision, the closeness to decision thresholds, and the necessity to consider some patients according to different viewpoints. As handling these issues requires some additional knowledge, which has to be acquired, different methods are presented that perform adaptation knowledge acquisition either from experts, or in a semi-automatic manner. A discussion and a conclusion end the paper.

International conferences

• Theoretical and Experimental Study of a Complexity Measure for Analogical Transfer. F. Badra, M.-J. Lesot, A. Barakat, C. Marsala (2022)
  30th International Conference on Case-Based Reasoning, ICCBR 2022, Nancy, France, September 12-15, 2022.
  abstract | paper | bibtex

  @inproceedings{Badra2022_ICCBR,
  	author    = {Fadi Badra and Marie-Jeanne Lesot and Aman Barakat and Christophe Marsala},
  	title     = {Theoretical and Experimental Study of a Complexity Measure for Analogical Transfer},
  	booktitle = {Case-Based Reasoning Research and Development - 30th International
  		Conference, {ICCBR} 2022, Nancy, France, September 12-15, 2022, Proceedings},
  	year      = {2022}
  }
  			

  Analogical transfer addresses classification and regression tasks, performing a plausible inference according to which similar instances are likely to be associated with similar labels. This paper proposes a detailed study of the ordinal implementation of this principle by the so-called CoAT algorithm, that is based on a data set complexity measure quantifying the number of inversions observed when ranking the data according to their instance or label similarities. At a theoretical level, it establishes an upper bound of the complexity measure, providing a reference value to which the observed one can be compared. At an algorithmic level, it proposes an optimization that allows decreasing the computational complexity by one order of magnitude. At an experimental level, it studies the correlation of the complexity measure with the accuracy of the conducted label inference, as well as with the classification task difficulty, as captured by the class overlapping degree.

• A Dataset Complexity Measure for Analogical Transfer. F. Badra (2020)
  International Joint Conference on Artificial Intelligence (IJCAI), Kyoto, Japan, January 2021.
  abstract | paper | bibtex

  @inproceedings{DBLP:conf/ijcai/Badra20,
  	author    = {Fadi Badra},
  	editor    = {Christian Bessiere},
  	title     = {A Dataset Complexity Measure for Analogical Transfer},
  	booktitle = {Proceedings of the Twenty-Ninth International Joint Conference on
   	Artificial Intelligence, {IJCAI} 2020},
  	pages     = {1601--1607},
  	publisher = {ijcai.org},
  	year      = {2020},
  	url       = {https://doi.org/10.24963/ijcai.2020/222},
  	doi       = {10.24963/ijcai.2020/222},
  	timestamp = {Mon, 20 Jul 2020 12:38:52 +0200},
  	biburl    = {https://dblp.org/rec/conf/ijcai/Badra20.bib},
  	bibsource = {dblp computer science bibliography, https://dblp.org}
  }
  										
  			

  Analogical transfer consists in leveraging a measure of similarity between two situations to predict the amount of similarity between their outcomes. Acquiring a suitable similarity measure for analogical transfer may be difficult, especially when the data is sparse or when the domain knowledge is incomplete. To alleviate this problem, this paper presents a dataset complexity measure that can be used either to select an optimal similarity measure, or if the similarity measure is given, to perform analogical transfer: among the potential outcomes of a new situation, the most plausible is the one which minimizes the dataset complexity.

• On the Role of Similarity in Analogical Transfer. F. Badra, K. Sedki, A. Ugon (2018)
  26th International Conference on Case-Based Reasoning, ICCBR 2018, Stockholm, Sweden, July 9-12, 2018.
  abstract | paper | bibtex

  @inproceedings{DBLP:conf/iccbr/BadraSU18,
  	author    = {Fadi Badra and Karima Sedki and Adrien Ugon},
  	title     = {On the Role of Similarity in Analogical Transfer},
  	booktitle = {Case-Based Reasoning Research and Development - 26th International
  		Conference, {ICCBR} 2018, Stockholm, Sweden, July 9-12, 2018, Proceedings},
  	pages     = {499--514},
  	year      = {2018},
  	crossref  = {DBLP:conf/iccbr/2018},
  	url       = {https://doi.org/10.1007/978-3-030-01081-2\_33},
  	doi       = {10.1007/978-3-030-01081-2\_33},
  	timestamp = {Thu, 11 Oct 2018 14:12:22 +0200},
  	biburl    = {https://dblp.org/rec/bib/conf/iccbr/BadraSU18},
  	bibsource = {dblp computer science bibliography, https://dblp.org}
  }
  			

  Analogical transfer consists in making the assumption that if two situations are alike in some respect, they may be alike in others. This article explores the links that exist between analogical transfer and the qualitative measurement of differences. The main idea is to formulate the similarity principle as a dependency between two measurements of difference. Analogical transfer is formulated as a similarity-based reasoning: it is plausible that equally different pairs in a certain dimension are also equally different in another dimension, at least for pairs that are not too (analogically) dissimilar.

• Reasoning with Co-variations. F. Badra (2016)
  The 17th International Conference on Artificial Intelligence: Methodology, Systems, Applications, AIMSA 2016, Varna, Bulgaria .
  (Best Paper Award)
  abstract | paper | bibtex

  @inproceedings{aimsa2016-badra,
  title={Reasoning with Co-variations},
  author={F. Badra},
  booktitle={The 17th International Conference on Artificial Intelligence:
  Methodology, Systems, Applications, AIMSA 2016, Varna, Bulgaria},
  year={2016},
  }
  			

  Adaptation is what allows a system to maintain consistent behavior across variations in operating environments. In some previous work, a symbolic representation of the variations between two or more elements of a set was proposed. This article goes one step further and defines co-variations as functional dependencies between variations. This gives us a natural deduction rule on variations, which we show can be easily extended to perform similarity-based reasoning. A method is also proposed to learn co-variations from the data. In this method, co-variations correspond to object implication rules in a pattern structure.

• Representing and Learning Variations. F. Badra (2015)
  The 27th IEEE Conference on Tools with Artificial Intelligence. 9-11 November 2015 - Vietri Sul Mare, Italy .
  abstract | paper | bibtex

  @inproceedings{ictai2015-badra,
  title={Representing and Learning Variations},
  author={F. Badra},
  booktitle={The 27th IEEE Conference on Tools with Artificial Intelligence.
  			9-11 November 2015 - Vietri Sul Mare, Italy},
  year={2015},
  }
  			

  In machine learning, objects are usually grouped according to similarities found in the objects descriptions. Re- cent works, however, suggest that representing the differences between object descriptions is also pertinent in many learning tasks. But not much study has been made on how to represent and learn from differences. This paper proposes a qualitative representation of inter-object variations that can be used as input of a learning task. The main idea is to define inter- objects variations as attributes of repetitions of objects, so that machine learning methods will be able to manipulate them in the same way as they manipulate object attributes. The approach is tested on both classification and a numerical value prediction tasks and shows encouraging results.

• Mining Sequential Patterns from MODIS time series for cultivated area mapping. Y. Pitarch, E. Vintrou, F. Badra, A. Bégué and M. Teisseire (2011)
  The 14th AGILE International Conference on Geographic Information Science. 18-21 April 2011 - Utrecht, The Netherlands .
  abstract | paper | bibtex

  @inproceedings{agile2011-badra,
  title={Mining Sequential Patterns from MODIS time series for cultivated area mapping},
  author={Y. Pitarch and E. Vintrou and F. Badra and A. B\'egu\'e and M. Teisseire},
  booktitle={The 14th AGILE International Conference on Geographic Information Science.
  			18-21 April 2011 - Utrecht, The Netherlands},
  year={2011},
  }
  			

  To predict and respond to famine and other forms of food insecurity, different early warning systems are using remote analyses of crop condition and agricultural production, using satellite-based infor- mation. To improve these predictions, a reliable estimation of the cul- tivated area at national scale must be carried out. In this study, we developed a datamining methodology for extracting cultivated domain patterns based on their temporal behavior as captured in time-series of moderate resolution remote sensing MODIS images.

• Opportunistic Adaptation Knowledge Discovery. F. Badra, A. Cordier and J. Lieber (2009)
  8^th International Conference on Case-Based Reasoning, ICCBR'09.
  abstract | paper | slides | bibtex

  @inproceedings{iccbr-BadraCL09,
  author    = {Fadi Badra and Am{\'e}lie Cordier and Jean Lieber},
  title     = {Opportunistic Adaptation Knowledge Discovery},
  booktitle = {Case-Based Reasoning Research and Development, 8th International Conference on Case-Based Reasoning,
  	     ICCBR 2009, Seattle, WA, USA, July 20-23, 2009, Proceedings},
  year      = {2009},
  pages     = {60-74}
  }
  			

  Adaptation has long been considered as the Achilles' heel of case-based reasoning since it requires some domain-specific knowledge that is difficult to acquire. In this paper, two strategies are combined in order to reduce the knowledge engineering cost induced by the adaptation knowledge (CA) acquisition task: CA is learned from the case base by the means of knowledge discovery techniques, and the CA acquisition sessions are opportunistically triggered, i.e., at problem-solving time.

• Case Base Mining for Adaptation Knowledge Acquisition. M. d'Aquin, F. Badra, S. Lafrogne, J. Lieber, A. Napoli and L. Szathmary (2007)
  20^th International Joint Conference on Artificial Intelligence, IJCAI'07
  abstract | paper | bibtex

  @inproceedings{IJCAI07,
    author = {M. d'Aquin and F. Badra and S. Lafrogne and J. Lieber and A. Napoli and L. Szathmary},
    title = {Case Base Mining for Adaptation Knowledge Acquisition},
    booktitle = {Proceedings of the International Conference on Artificial Intelligence, IJCAI'07},
    year = {2007},
    pages = {750--756}
  }
  			

  In case-based reasoning, the adaptation of a source case in order to solve the target problem is at the same time crucial and difficult to implement. The reason for this difficulty is that, in general, adaptation strongly depends on domain-dependent knowledge. This fact motivates research on adaptation knowledge acquisition (AKA). This paper presents an approach to AKA based on the principles and techniques of knowledge discovery from databases and data-mining. It is implemented in CabamakA, a system that explores the variations within the case base to elicit adaptation knowledge. This system has been successfully tested in an application of case-based reasoning to decision support in the domain of breast cancer treatment.

International workshops

• Some perspectives on similarity learning for case-based reasoning and analogical transfer. F. Badra, M.-J. Lesot, E. Marquer, M. Couceiro (2023)
  IARML@IJCAI'2023: Workshop on the Interactions between Analogical Reasoning and Machine Learning, at IJCAI'2023, Macao, China.
  abstract | paper | bibtex

  @inproceedings{DBLP:conf/iccbr/MarquerBLCL23,
  	author       = {Fadi Badra and Marie{-}Jeanne Lesot and	Esteban Marquer and Miguel Couceiro},
  	title        = {Some perspectives on similarity learning for case-based reasoning and analogical transfer},
  	booktitle    = {Proceedings of the Workshop on the Interactions between Analogical Reasoning and Machine Learning, IARML@IJCAI'2023},
  	year         = {2023}
  }
  							

  In this paper we investigate interactions between recent advances in the modeling of analogical transfer and similarity learning. Indeed, a unifying principle of case-based prediction methods was recently established, according to which the plausible inference principle of analogical transfer can be interpreted as a transfer of similarity knowledge from a situation space to an outcome space. Following this principle, the task of analogical transfer can be addressed using a global indicator of the compatibility between two similarity measures. Such an indicator can also be used to assess the quality of the situation space similarity measure with respect to the case-based prediction task. We discuss several perspectives opened by such an interpretation of the task of analogical transfer as the optimisation of the compatibility criterion: we explore interactions with similarity learning, as well as with energy function optimisation

• Less is Better: An Energy-Based Approach to Case Base Competence. E. Marquer, F. Badra, M.-J. Lesot, M. Couceiro, D. Leake (2023)
  ICCBR Analogies'23: Workshop on Analogies: from Theory to Applications at ICCBR-2023, July, 2023, Aberdeen, Scotland.
  abstract | paper | bibtex

  @inproceedings{DBLP:conf/iccbr/MarquerBLCL23,
  	author       = {Esteban Marquer and
  					Fadi Badra and
  					Marie{-}Jeanne Lesot and
  					Miguel Couceiro and
  					David Leake},
  	editor       = {Lukas Malburg and
  					Deepika Verma},
  	title        = {Less is Better: An Energy-Based Approach to Case Base Competence},
  	booktitle    = {Proceedings of the Workshops at the 31st International Conference
  					on Case-Based Reasoning {(ICCBR-WS} 2023) co-located with the 31st
  					International Conference on Case-Based Reasoning {(ICCBR} 2023), Aberdeen,
  					Scotland, UK, July 17, 2023},
  	series       = {{CEUR} Workshop Proceedings},
  	volume       = {3438},
  	pages        = {27--42},
  	publisher    = {CEUR-WS.org},
  	year         = {2023},
  	url          = {https://ceur-ws.org/Vol-3438/paper\_03.pdf},
  	timestamp    = {Tue, 11 Jul 2023 17:14:10 +0200},
  	biburl       = {https://dblp.org/rec/conf/iccbr/MarquerBLCL23.bib},
  	bibsource    = {dblp computer science bibliography, https://dblp.org}
  								}
  							

  This paper revisits the notion of case base competence in the light of recent advances in the modeling of analogical reasoning, based on the idea of similarity transfer from a situation space to an outcome space. For that we consider the CoAT indicator, that measures the compatibility between two similarity measures on a case base, and use it to define an intrinsic measure of competence of a case base with respect to a reference set. Initial experimental results show that the proposed competence measure correlates with the performance of the CoAT prediction algorithm. In fact, our preliminary results seem to indicate that, under some initial conditions, our competence based model can fit any classification boundary. We then revisit the notions of case competence and locality, and show that some source cases may degrade the overall case base competence while others may improve it, and that a given source case may have disparate influence on different regions of the case space.

• CoAT-APC: When Analogical Proportion-based Classification Meets Case-based Prediction. F. Badra and M.-J. Lesot (2022)
  ICCBR Analogies'22: Workshop on Analogies: from Theory to Applications at ICCBR-2022, September, 2022, Nancy,France.
  abstract | paper | bibtex

  							@inproceedings{Badra2022_ATA,
  							author    = {Fadi Badra and Marie-Jeanne Lesot},
  							title     = {CoAT-APC: When Analogical Proportion-based Classification Meets Case-based Prediction},
  							booktitle = {Case-Based Reasoning Research and Development - 30th International
  							Conference, {ICCBR} 2022, Nancy, France, September 12-15, 2022, Workshop Proceedings},
  							year      = {2022}
  							}
  							

  This paper proposes to view analogical proportion-based classification as a special type of case-basedprediction algorithm, in which (i) cases are differences between two instances, and (ii) only maximallysimilar cases are compared. It then proposes to tweak the CoAT case-based prediction algorithm inorder to implement these two key design principles. The resulting analogical proportion-based classifierCoAT-APC shows a performance comparable to state-of-the-art analogical proportion-based classifiers,while implementing a different transfer strategy, based on the minimization of a dataset complexitymeasure, as opposed to a rule-based approach. Experimental results show the usefulness of combiningthese two design principles and suggest that the rule-based transfer strategy of analogical proportion-based classifiers has comparatively little impact on the performance of the system.

• A Language of Case Differences. F. Badra (2017)
  Computational Analogy Workshop, co-located with the 25th International Conference on Case-Based Reasoning (ICCBR 2017), Trondheim, Norway, June 26-28, 2017.
  abstract | paper | bibtex

  @inproceedings{DBLP:conf/iccbr/Badra17,
  	author    = {Fadi Badra},
  	title     = {A Language of Case Differences},
  	booktitle = {Proceedings of {ICCBR} 2017 Workshops (CAW, CBRDL, PO-CBR), Doctoral
  		Consortium, and Competitions co-located with the 25th International
  		Conference on Case-Based Reasoning {(ICCBR} 2017), Trondheim, Norway,
  		June 26-28, 2017.},
  	pages     = {63--72},
  	year      = {2017},
  	crossref  = {DBLP:conf/iccbr/2017w},
  	url       = {http://ceur-ws.org/Vol-2028/paper6.pdf},
  	timestamp = {Tue, 19 Dec 2017 18:21:41 +0100},
  	biburl    = {https://dblp.org/rec/bib/conf/iccbr/Badra17},
  	bibsource = {dblp computer science bibliography, https://dblp.org}
  }
  			

  This paper contributes to a line of research that consists in applying qualitative reasoning techniques to the formalization of the case-based inference, and in particular, to its adaptation phase. The importance of capturing case differences has long been acknowledged in adaptation research, but research is still needed to properly represent and reason upon case differences. Assuming that case differences can be expressed as a set of feature differences, we show that Category Theory can be used as a mathematical framework to design a qualitative language in which both case differences, similarity paths and adaptation rules can be represented and reasoned upon symbolically.

• Elements of a Data-Driven Approach to Adaptation. F. Badra (2016)
  Computational Analogy Workshop, co-located with the 24th International Conference on Case-Based Reasoning (ICCBR 2016), Atlanta, Georgia, United States of America, Oct 31st, 2016..
  abstract | paper | bibtex

  @inproceedings{ICCBR_Badra16,
  	author    = {Fadi Badra},
  	title     = {Elements of a Data-Driven Approach to Adaptation},
  	booktitle = {Proceedings of the Computational Analogy Workshop, co-located with the 24th International
  		Conference on Case-Based Reasoning {(ICCBR} 2016), Atlanta, Georgia, United States of America, Oct 31st, 2016.},
  	year      = {2016},
  }
  			

  Adaptation is what enables a system to respond to perturbations in its environment. Even though case-based reasoning is commonly thought of as a data-driven problem-solving paradigm, adaptation methods often follow a model-driven pattern. This paper discusses what a purely data-driven approach to adaptation might look like. The proposed method uses co-variations to extrapolate to the target case some parts of the relational structure underlying the case base. One of the outcomes of this work is to show that adaptation can be (at least partially) included in the case-based inference process, which is known to be a special case of analogical reasoning.

• Case Adaptation with Modal Logic: the Modal Adaptation. F. Badra (2014)
  22^th International Conference on Case-Based Reasoning, ICCBR'14
  abstract | paper | bibtex

  @inproceedings{ICCBR14,
    author = {F. Badra},
    title = {Case Adaptation with Modal Logic: the Modal Adaptation},
    booktitle = {Proceedings of the International Conference on Case-Based Reasoning, ICCBR'14},
    year = {2014}
  }
  			

  In the CBR cycle, adaptation is the process that aims at filling the "gap"" between the current situation and a previous situation retrieved from mem- ory. In this paper, tools from modal logic are investigated to come up with a "modal" modeling of adaptation. The intuition is that adaptation dynamically ex- tends an agent's beliefs about the world so that it can envision possible worlds in which the target constraints are satisfied. Following this idea, the adaptation process is formulated as a model construction problem in the S4 modal logic.

• Ontology and Lexicon: the Missing Link. F. Badra, S. Després and R. Djedidi (2011)
  Proceedings of the Workshop "Ontology and Lexicon: New Insights", Workshop at TIA 2011 .
  paper | bibtex

  @inproceedings{tia11-badra,
  title={Ontology and Lexicon: the Missing Link},
  author={F. Badra, S. Després and R. Djedidi},
  booktitle={Proceedings of the Workshop "Ontology and Lexicon: New Insights", Workshop at TIA 2011.},
  year={2011},
  }
  			

• Squeezing Lemon With GATE. B. Davis, F. Badra, P. Buitelaar, T. Wunner and S. Handschuh (2011)
  Proceedings of the 2nd International Workshop on the Multilingual Semantic Web (MSW 2011), Workshop at ISWC 2011 .
  abstract | paper | bibtex

  @inproceedings{msw11-badra,
  title={Squeezing Lemon With GATE},
  author={B. Davis, F. Badra, P. Buitelaar, T. Wunner and S. Handschuh},
  booktitle={Proceedings of the 2nd International Workshop on the Multilingual Semantic Web (MSW 2011), Workshop at ISWC 2011.},
  year={2011},
  }
  			

  An increasing number of enterprises are beginning to include ontologies into Text Analytics (TA) applications. This can be challenging for a TA group wishing to avail of such technologies due to the manual effort needed to map language resources within a TA system for a new domain. Ontology lexicalization offers a solution to this problem by seeking to automatically generate lexical resources in order to shrink the manual effort of this concept-to-text mapping process. However, conventional approaches are limited in that they often can only generate term mentions of proper noun, personal noun or fixed key phrases from concept labels in ontologies. Such approaches do not generalize to cope with more complex concept mentions such as nominal compounds or multi-word expressions. An alternative consideration is lemon - Lexicon Model for Ontologies which offers a more sophisticated solution to this problem. We describe a simple use case for exploiting lemon within a widely used open-source TA framework and demonstrate how lemon generated lexical resources are at least comparable in agreement to OntoRootGazeteer, a conventional ontology lexicalization approach.

• A Semantic Web Representation of Product Range Specification based on Constraint Satisfaction Problem in the Automotive Industry. F. Badra, F.-P. Servant and A. Passant (2011)
  ESWC Workshop on Ontology and Semantic Web for Manufacturing (OSEMA'11), Heraklion, Greece, 29 may, 2011 .
  abstract | paper | bibtex

  @inproceedings{osema11-badra,
  title={A Semantic Web Representation of Product Range Specification based on Constraint Satisfaction Problem in the Automotive Industry},
  author={F. Badra and F.-P. Servant and A. Passant},
  booktitle={Proceedings of the ESWC Workshop on Ontology and Semantic Web for Manufacturing (OSEMA'11), Heraklion, Greece, 29 may, 2011.},
  year={2011},
  }
  			

  Product Range Specification (PRS) in the automotive world is one of the most complex PRS that exists in industrial contexts. PRS plays therefore a key role in the information system of an automaker: related data pervades many systems, and numerous applications are using it. This is the case at Renault, where PRS is modelled as a Constraint Satisfaction Problem. In this paper, we study how to represent the objects, concepts and services related to such a PRS using Semantic Web standards. Plugging them into a Linked Data based architecture enables with new ways to access corresponding data and tools in the whole car manufacturing and selling process.

• Knowledge Acquisition and Discovery for the Textual Case-Based Cooking system WikiTaaable. F. Badra, J. Cojan, A. Cordier, J. Lieber, T. Meilender, A. Mille, P. Molli, E. Nauer, A. Napoli, H. Skaf-Molli, Y. Toussaint (2009)
  Computer Cooking Contest Workshop at the 8^th International Conference on Case-Based Reasoning, ICCBR'09.
  abstract | paper | bibtex

  @inproceedings{iccbr-wikitaaable,
  title={Knowledge Acquisition and Discovery for the Textual Case-Based Cooking system \textsc{WikiTaaable}},
  author={F. Badra and J. Cojan and A. Cordier and J. Lieber and T. Meilender and A. Mille and P. Molli and E. Nauer
          and A. Napoli and H. Skaf-Molli and Y. Toussaint},
  booktitle={8th {I}nternational {C}onference on {C}ase-{B}ased {R}easoning - {ICCBR} 2009, Workshop Proceedings },
  editor = {Lorraine McGinty and David C. Wilson},
  year=2009
  }
  			

  The textual case-based cooking system WikiTaaable participates to the second Computer cooking contest (CCC). It is an extension of the Taaable system that has participated to the first CCC. Wikitaaable's architecture is composed of a semantic wiki used for the collaborative acquisition of knowledge (recipe, ontology, adaptation knowledge) and of a case-based inference engine using this knowledge for retrieving and adapting recipes. This architecture allows various modes of knowledge acquisition for case-based reasoning that are studied within the Taaable project. In particular, opportunistic adaptation knowledge discovery is an approach for interactive and semi-automatic learning of adaptation knowledge triggered by a feedback from the user.

• Taaable: Text Mining, Ontology Engineering, and Hierarchical Classification for Textual Case-Based Cooking. F. Badra, R. Bendaoud, R. Bentebibel, P.-A. Champin, J. Cojan, A. Cordier, S. Desprès, S. Jean-Daubias, J. Lieber, T. Meilender, A. Mille, E. Nauer, A. Napoli and Y. Toussaint (2008)
  Computer Cooking Contest Workshop at the 9^th European Conference on Case-Based Reasoning, ECCBR'08.
  abstract | paper | bibtex

  @inproceedings{ewcbr-taaable,
    author    = {Fadi Badra and Rokia Bendaoud and Rim Bentebibel and Pierre-Antoine Champin and Julien Cojan and
                 Am{\'e}lie Cordier and Sylvie Despr{\`e}s and  St{\'e}phanie Jean-Daubias and Jean Lieber and
                 Thomas Meilender and Alain Mille and Emmanuel Nauer and Amedeo Napoli and Yannick Toussaint},
    title     = {\textsc{Taaable}: Text Mining, Ontology Engineering, and Hierarchical Classification for Textual Case-Based Cooking},
    booktitle = {ECCBR 2008, The 9th European Conference on Case-Based Reasoning,
                 Trier, Germany, September 1-4, 2008, Workshop Proceedings},
    year      = {2008},
    pages     = {219-228},
    editor    = {Martin Schaaf}
  }
  			

  This paper presents how the Taaable project addresses the textual case-based reasoning challenge of the CCC, thanks to a combination of principles, methods, and technologies of various fields of knowledge-based system technologies, namely CBR, ontology engineering (manual and semi-automatic), data and text-mining using textual resources of the Web, text annotation (used as an indexing technique), knowledge representation, and hierarchical classification. Indeed, to be able to reason on textual cases, indexing them by a formal representation language using a formal vocabulary has proven to be useful.

• Representing Case Variations for Learning General and Specific Adaptation Rules. F. Badra and J. Lieber (2008)
  STAIRS 2008- Proceedings of the Fourth Starting AI Researchers' Symposium, Patras, Greece, 21-25 July, 2008
  abstract | paper | slides | bibtex

  @inproceedings{stairs-BadraL08,
    author    = {Fadi Badra and Jean Lieber},
    title     = {Representing Case Variations for Learning General and Specific Adaptation Rules},
    booktitle = {STAIRS 2008   - Proceedings of the Fourth Starting AI Researchers'
                 Symposium, Patras, Greece, 21-25 July, 2008},
    year      = {2008},
    publisher = {IOS Press},
    editor    = {Amedeo Cesta and Nikos Fakotakis},
    series    = {Frontiers in Artificial Intelligence and Applications},
    volume    = {179},
    pages     = {1-11}
  }
  			

  Adaptation is a task of case-based reasoning systems that is largely domain-dependant. This motivates the study of adaptation knowledge acquisition (AKA) that can be carried out thanks to learning processes on the variations between cases of the case base. This paper studies the representation of these variations and the impact of this representation on the AKA process, through experiments in an oncology domain.

International posters/demos

• EdHibou: A Customizable Interface for Decision Support in a Semantic Portal F. Badra, M. d'Aquin, J. Lieber and T. Meilender (2008)
  Poster/Demo at the 7^th International Semantic Web Conference, ISWC'08
  abstract | paper | poster | bibtex

  @inproceedings{semweb-BadradLM08,
    author    = {F. Badra and M. d'Aquin and J. Lieber and T. Meilender},
    title     = {EdHibou: a Customizable Interface for Decision Support in
                 a Semantic Portal},
    booktitle = {Proceedings of the Poster and Demonstration Session at the
                 7th International Semantic Web Conference (ISWC2008), Karlsruhe,
                 Germany, October 28},
    year      = {2008}
  }
  			

  The Semantic Web is becoming more and more a reality, as the required technologies have reached an appropriate level of maturity. However, at this stage, it is important to provide tools facilitating the use and deployment of these technologies by end-users. In this paper, we describe EdHibou, an automatically generated, ontology-based graphical user interface that integrates in a semantic portal. The particularity of EdHibou is that it makes use of OWL reasoning capabilities to provide intelligent features, such as decision support, upon the underlying ontology. We present an application of EdHibou to medical decision support based on a formalization of clinical guidelines in OWL and show how it can be customized thanks to an ontology of graphical components.

• Adaptation Knowledge Discovery from a Case Base. M. d'Aquin, F. Badra, S. Lafrogne, J. Lieber, A. Napoli and L. Szathmary (2006)
  17^th European Conference on Artificial Intelligence, ECAI'06
  abstract | paper | poster | bibtex

  @article{aquin-ecai06,
    author    = {Mathieu d'Aquin and Fadi Badra and Sandrine Lafrogne and Jean Lieber and Amedeo Napoli and Laszlo Szathmary},
    title     = {Adaptation Knowledge Discovery from a Case Base},
    journal   = {ECAI 2006, 17th European Conference on Artificial Intelligence,
                 August 29 - September 1, 2006, Riva del Garda, Italy, Including
                 Prestigious Applications of Intelligent Systems (PAIS 2006),
                 Proceedings},
    publisher = {IOS Press},
    editor    = {Gerhard Brewka and Silvia Coradeschi and Anna Perini and Paolo Traverso},
    series    = {Frontiers in Artificial Intelligence and Applications},
    volume    = {141},
    year      = {2006}
  }
  			

  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.

National (french) conferences

• Fouille de données complexes : des relevés terrain aux données satellitaires pour la cartographie des paysages agricoles. F. Badra, E. Vintrou, A. Bégué and M. Teisseire (2010)
  Conférence Internationale de Géomatique et Analyse Spatiale SAGEO'10, Toulouse 17-19 novembre 2010 .
  abstract | paper | bibtex

  @inproceedings{sageo2010-badra,
  title={Fouille de donn\'ees complexes : des relev\'es terrain aux donn\'ees satellitaires pour la cartographie des paysages agricoles},
  author={F. Badra and E. Vintrou and A. B\'egu\'e and M. Teisseire},
  booktitle={Conf\'erence Internationale de G\'eomatique et Analyse Spatiale SAGEO'10, Toulouse 17-19 novembre 2010},
  year={2010},
  }
  			

  Dans cet article, nous présentons une approche préliminaire de caractérisation des paysages ruraux et de leurs systèmes de culture à partir de techniques de fouille de données (recherche d'itemsets fréquents). Cette méthode permet de coupler des données de relevé terrain aux indicateurs de texture extraits des images satellites. Sa mise en oeuvre sur des données associées au Mali pose les premières bases d’une méthode originale d'extraction de motifs séquentiels à partir de données complexes.

• Familles génératrices de règles d'adaptation pour assister leur acquisition semi-automatique. M. Tixier, F. Badra and J. Lieber (2008)
  19^èmesJournées Francophones d'Ingénierie des Connaissances (IC'08).
  abstract | paper | bibtex

  @inproceedings{f-ic-TixierBL08,
    author    = {M. Tixier and F. Badra and J. Lieber},
    title     = {Familles g{\'e}n{\'e}ratrices de r{\`e}gles d'adaptation
                 pour assister leur acquisition semi-automatique},
    booktitle = {Actes d'IC  2008: Ing{\'e}nierie des connaissances 2008 (Proceedings
                 of the 19th French Knowledge Engineering Conference), Nancy,
                 France, June 18-20, 2008},
    year      = {2008},
    pages     = {225-236}
  }
  			

  L'acquisition de connaissances d'adaptation, notamment dans sa dimension automatique, ouvre de grandes perspectives pour l'étape critique en raisonnement à partir de cas que constitue l'adaptation. Le système CabamakA s'appuie sur des techniques symboliques de fouille de données pour extraire des règles d'adaptation candidates. Cet article présente une approche pour réduire le nombre de telles règles à présenter à l'analyste pour validation. L'idée est de s'appuyer sur une opération de composition de ces règles et de chercher une famille génératrice pour cette composition.

• Extraction de connaissances d'adaptation par analyse de la base de cas. F. Badra and J. Lieber (2007)
  7^èmes Journées Extraction et Gestion des Connaissances (EGC'07).
  abstract | paper | slides | bibtex

  @inproceedings{EGC07,
  title =	{Extraction de connaissances d'adaptation par l'analyse de la base de cas},
  author =	"F. Badra and J. Lieber",
  Booktitle =	"Extraction et gestion des connaissances ({EGC}'2007),
  		 Actes des septi{\`e}mes journ{\'e}es Extraction et
  		 Gestion des Connaissances, Namur, Belgique, 23-26 janvier
  		 2007, 2 Volumes",
  publisher =	"C{\'e}padu{\`e}s-{\'E}ditions",
  year = 	"2007",
  pages =	"751--760",
  series =	"Revue des Nouvelles Technologies de l'Information"
  }
  
  			

  En raisonnement à partir de cas, l'adaptation d'un cas source pour résoudre un problème cible est une étape à la fois cruciale et difficile à réaliser. Une des raisons de cette difficulté tient au fait que les connaissances d'adaptation sont généralement dépendantes du domaine d'application. C'est ce qui motive la recherche sur l'acquisition de connaissances d'adaptation (ACA). Cet article propose une approche originale de l'ACA fondée sur des techniques d'extraction de connaissances dans des bases de données (ECBD). Nous présentons CabamakA, une application qui réalise l'ACA par analyse de la base de cas, en utilisant comme technique d'apprentissage l'extraction de motifs fermés fréquents. L'ensemble du processus d'extraction des connaissances est détaillé, puis nous examinons comment organiser les résultats obtenus de façon à faciliter la validation des connaissances extraites par l'analyste.

National (french) workshops

• Sur la représentation des co-variations. F. Badra (2016)
  10^ème journées Intelligence Artificielle Fondamentale (IAF'2016).
  abstract | paper | slides | bibtex

  @inproceedings{iaf16-badra,
  title={Sur la représentation des co-variations},
  author={F. Badra},
  booktitle={10{\`e}me journées Intelligence Artificielle Fondamentale},
  address={{M}ontpellier, {F}rance},
  year={2016},
  }
  			

  L'abondance actuelle des données disponibles motive la recherche pour représenter les différences qui existent entre plusieurs traces numériques d’un même processus. Dans des travaux précédents, nous avons proposé une représentation symbolique des variations entre plusieurs éléments d’un même ensemble. Dans cet article, nous allons plus loin et définissons la notion de co-variation comme une dépendance fonctionnelle entre variations. Nous montrons que cette définition permet d’obtenir une règle de déduction naturelle sur les variations, qui peut être facilement étendue pour définir un raisonnement par similarité. Une méthode est ensuite proposée pour apprendre des co-variations à partir de données. Dans cette méthode, les co-variations correspondent aux règles d’implication d’objets dans une structure de motifs.

• Découverte opportuniste de connaissances d'adaptation. F. Badra, A. Cordier and J. Lieber (2009)
  17^ème atelier sur le raisonnement à partir de cas - RàPC'09.
  abstract | paper | slides | bibtex

  @inproceedings{rapc09-badra,
  title={D\'ecouverte opportuniste de connaissances d'adaptation},
  author={F. Badra and A. Cordier and J. Lieber},
  booktitle={17{\`e}me atelier sur le raisonnement {\`a} partir de cas - {R}{\`a}{PC}-09 },
  pages={23--34},
  address={{P}aris {F}rance },
  editor={B\'eatrice Fuchs and Amedeo Napoli},
  year={2009},
  }
  			

  L'étape d'adaptation est souvent considérée comme le talon d'Achille du raisonnement à partir de cas car elle nécessite des connaissances spécifiques au domaine d'application qui sont difficiles à acquérir. Dans ce papier, deux stratégies sont combinées pour faciliter la tâche d'acquisition de connaissances d'adaptation : les connaissances d'adaptation sont apprises à partir de la base de cas par des techniques d'extraction de connaissances, et l'acquisition de connaissances d'adaptation est déclenchée de manière opportuniste au cours d'une session particulière de résolution de problèmes.

• Une approche pour représenter les variations entre cas - Vers une application à l'extraction de connaissances d'adaptation. F. Badra and J. Lieber (2007)
  15^ème atelier sur le raisonnement à partir de cas - RàPC'07.
  abstract | paper | bibtex

  @inproceedings{rapc07-badra,
  title={{U}ne approche pour repr{\'e}senter les variations entre cas
                    --- {V}ers une application {\`a} l'extraction de
                    connaissances d'adaptation},
  author={F. {B}adra and J. {L}ieber},
  booktitle={15{\`e}me atelier sur le raisonnement {\`a} partir de cas - {R}{\`a}{PC}-07 },
  pages={47--56 },
  address={{G}renoble {F}rance },
  editor={{A}m{\'e}lie {C}ordier },
  year={2007},
  }
  			

  Cet article propose une méthode pour représenter les variations entre deux cas dans le cadre d'une représentation des cas par des couples attributs-ensembles de valeurs (ce qui généralise le formalisme, classique dans le cadre du raisonnement à partir de cas, des couples attributs-valeurs). Pour effectuer une telle représentation, on met en évidence tout d'abord les attributs qui ne sont pas communs aux deux cas puis, pour les autres, on compare les ensembles de valeurs par des ensembles de relations binaires. De telles relations sont spécifiées par le concepteur du système de raisonnement à partir de cas sur la base de ce qui est pertinent pour « passer » d'un cas à un autre. Des exemples de telles relations sont proposés. Ce travail doit être appliqué à l'extraction de connaissances d'adaptation par fouille de la base de cas : une telle fouille s'appuie sur la représentation des variations entre cas de la base. L'expressivité des règles d'adaptation ainsi extraites sera fonction de l'expressivité de la représentation des variations entre cas.

Phd Thesis

• Extraction de connaissances d'adaptation en raisonnement à partir de cas
  résumé (Fr) | abstract (En) | paper | slides | bibtex

  @phdthesis{phdThesisBADRA,
  	title = { {E}xtraction de connaissances d'adaptation en raisonnement {\`a} partir de cas},
  	author = {{B}adra, {F}adi},
  	school = {{U}niversit{\'e} {H}enri {P}oincar{\'e} - {N}ancy {I}},
  	day = {20},
  	month = {11},
  	year = {2009},
  }
  			

  Cette thèse se situe à l'intersection de trois domaines de recherche : le raisonnement à partir de cas, l'extraction de connaissances et la représentation des connaissances. Raisonner à partir de cas consiste à résoudre un nouveau problème en utilisant un ensemble de problèmes déjà résolus, appelés cas. Dans cette thèse, un langage de représentation des variations entre cas est introduit. Nous montrons comment ce langage peut être utilisé pour représenter les connaissances d'adaptation et pour modéliser la phase d'adaptation en raisonnement à partir de cas. Ce langage est ensuite appliqué à la tâche d'apprentissage de connaissances d'adaptation. Un processus d'extraction de connaissances, appelé CabamakA, est mis au point. Ce processus permet d'apprendre des connaissances d'adaptation par généralisation à partir d'une représentation des variations entre cas. Une discussion est ensuite menée sur les conditions d'opérationnalisation de CabamakA au sein d'un processus d'acquisition de connaissances. L'étude aboutit à la proposition d'un nouveau type d'approche pour l'acquisition de connaissances d'adaptation dans lequel le processus d'extraction de connaissances est déclenché de manière opportuniste au cours d'une session particulière de résolution de probl&egrav;mes. Les différents concepts introduits dans la thèse sont illustrés dans le domaine culinaire à travers leur application au système de raisonnement à partir de cas Taaable, qui constitue le contexte applicatif de l'étude.

  This thesis presents some contributions in three research domains : case-based reasoning, knowledge discovery and knowledge representation. Case-based reasoning consists in solving new problems by reusing a set of previous problem-solving experiences, called cases. In this thesis, a language is introduced to represent variations between cases. We first show how this language can be used to represent adaptation knowledge and to model the adaptation phase in case-based reasoning. This language is then applied to the task of adaptation knowledge learning. A knowledge discovery process, called CabamakA, is proposed, that learns adaptation knowledge by generalization from a representation of variations between cases. A discussion follows on how to make this knowledge discovery process operational in a knowledge acquisition process. The discussion leads to the proposition of a new approach for adaptation knowledge acquisition, in which the knowledge discovery process is triggered in an opportunistic manner at problem-solving time. The concepts introduced in the thesis are illustrated in the cooking domain through their application in the case-based reasoning system Taaable, that constitutes the application domain of the study.

Other publications

• Utilisation de techniques de fouille de données pour enrichir une ontologie d'objets géographiques. F. Badra (2005)
  Rapport de Master Recherche informatique spécialité Extraction de connaissances à partir de Données (Equipe de Recherche en Ingénierie des Connaissances, Lyon).
  abstract | manuscrit | site Web du laboratoire ERIC

  La recherche sur les ontologies suscite un intérêt croissant dans le domaine du traitement de l'information géographique. Celles-ci apparaissent comme un composant essentiel pour réaliser l'intégration de sources de données hétérogènes et faire face à la considérable augmentation de la quantité de données d'observation de la Terre aujourd'hui disponibles. Cette étude s'inscrit dans le projet multidisciplinaire FoDoMuST, qui vise à extraire et qualifier des objets urbains à partir d'images satellites multi-spectrales et multi-résolution. Un objet urbain est un concept de la géographie urbaine, discipline scientifique qui étudie les dimensions spatiales du phénomène urbain et dont l'objet d'étude est la ville. Nous nous sommes concentrés essentiellement sur les aspects conception et implémentation d'une ontologie d'objets urbains. Notre démarche a abouti à la proposition d'un modèle conceptuel de l'ontologie ainsi qu'à une étude de son opérationnalisation et des perspectives de son enrichissement par des processus d'extraction de connaissances à partir de données.

• Comparative Study on Multi-Class Composite N-grams Applied to English and Japanese. F. Badra (2003)
  Rapport de projet de fin d'études de la formation d'ingénieur en Télécommunications (Advanced Telecommunications Research Institute, Kyoto, Japon).
  abstract | manuscrit | site Web du laboratoire ATR

  This document will present the research work I conducted in ATR for five months from april to august 2003. My work mainly consisted in an application of Multi-Class Composite N-gram language models, proposed recently by ATR researchers H.Yamamoto, S.Isogai and Y.Sagisaka (Yamamoto, 2001) and only applied to Japanese language so far, to the English language. The purpose of the experiments I conducted was to provide experimental data on such a model for the two languages, and determine to which extend this new technique, which showed good results for Japanese, can be applied as well to the English model. These tests were performed for training corpora of dif- ferent sizes, extracted from the B.T.E.C., and running these experiments on the two languages in the same conditions enabled us to make a comparison between them. The results showed that Multi-Class language models improve conventional Class-Based ones for English too, but with different optimal connectivity information and only for small training corpora.