Semi-automatic annotation process for procedural texts: An application on cooking recipes

Taaable is a case-based reasoning system that adapts cooking recipes to user constraints. Within it, the preparation part of recipes is formalised as a graph. This graph is a semantic representation of the sequence of instructions composing the cooking process and is used to compute the procedure adaptation, conjointly with the textual adaptation. It is composed of cooking actions and ingredients, among others, represented as vertices, and semantic relations between those, shown as arcs, and is built automatically thanks to natural language processing. The results of the automatic annotation process is often a disconnected graph, representing an incomplete annotation, or may contain errors. Therefore, a validating and correcting step is required. In this paper, we present an existing graphic tool named \kcatos, conceived for representing and editing decision trees, and show how it has been adapted and integrated in WikiTaaable, the semantic wiki in which the knowledge used by Taaable is stored. This interface provides the wiki users with a way to correct the case representation of the cooking process, improving at the same time the quality of the knowledge about cooking procedures stored in WikiTaaable

Automatic case acquisition from texts for process-oriented case-based reasoning

This paper introduces a method for the automatic acquisition of a rich case representation from free text for process-oriented case-based reasoning. Case engineering is among the most complicated and costly tasks in implementing a case-based reasoning system. This is especially so for process-oriented case-based reasoning, where more expressive case representations are generally used and, in our opinion, actually required for satisfactory case adaptation. In this context, the ability to acquire cases automatically from procedural texts is a major step forward in order to reason on processes. We therefore detail a methodology that makes case acquisition from processes described as free text possible, with special attention given to assembly instruction texts. This methodology extends the techniques we used to extract actions from cooking recipes. We argue that techniques taken from natural language processing are required for this task, and that they give satisfactory results. An evaluation based on our implemented prototype extracting workflows from recipe texts is provided.Comment: Sous presse, publication pr\'evue en 201

Towards the right assistance at the right time for using complex interfaces

Many users struggle when they have to use complex interfaces to complete everyday computing tasks. Offering intelligent, proactive assistance is becoming commonplace yet determining the right time to provide help is still difficult. We conducted an empirical study that aimed to uncover what user factors influenced following advice. Our results describe a user's background and expectations that appear to play a role in heeding assistance. Our work is a step towards understanding how to provide the right assistance at the right time and build proactive assistance systems that are personalized for individual users

Reasoning with organizational case bases in the absence of negative exemplars

In: L Lamontagne, J A Recio-Garcia (eds): Proceedings of the ICCBR 2012 Workshops. Lyon, France, 3-6 Sept 2012. p. 35-44.Organizational case bases are gathered based on the organization they serve; cases are not selected taking reasoning into account. Thus, organizational case bases may lack negative exemplars and have multiple solutions to one problem, making it difficult learn weights for reasoning. Case bases in typical Process-Oriented Case-Based Reasoning (POCBR) contexts are organizational, thus inheriting those problems. This paper describes an approach to identify a subset of cases from an organizational case base that meets the criterion that similar problems have similar solutions. This subset is then used to characterize classes, establishing positive and negative exemplars that are then used to learn weights for reasoning with the entire case base. We apply this approach to three organizational case bases, showing significant improvements in accuracy with weights learned with this approach in case bases without negative exemplars

Transferring Learned Models of Morphological Analogy

International audienc

Knowledge Continuous Integration Process (K-CIP)

International audienceSocial semantic web creates read/write spaces where users and smart agents collaborate to produce knowledge readable by humans and machines. An important issue concerns the ontology evolution and evaluation in man-machine collaboration. How to perform a change on ontologies in a social semantic space that currently uses these ontologies through requests ? In this paper, we propose to implement a continuous knowledge integration process named K-CIP. We take advantage of man-machine collaboration to transform feedback of people into tests. This paper presents how K-CIP can be deployed to allow fruitful man-machine collaboration in the context of the WikiTaaable system.Le web sémantique social crée des espaces partagés dans lesquels des utilisateurs et des agents logiciels collabore pour produire de la connaissance utilisable par les humains et les machines. Un problème important est celui de l'évolution et l'évaluation des ontologies dans la collaboration : comment réaliser un changement sur une ontologie dans un espace qui utilise cette ontologie. Dans ce papier, nous proposons de réaliser un processus d'intégration continue de la connaissance nommé K-CIP. Nous tirons profit des retours des utilisateurs dans la collaboration pour construire des tests. Cet article montre comment K-CIP peut être mis en oeuvre pour améliorer la collaboration humain-machine dans le contexte du système WikiTaaable

Differential adaptation: An operational approach to adaptation for solving numerical problems with CBR

International audienceCase-based reasoning relies on four main steps: retrieval, adaptation, revision and retention. This article focuses on the adaptation step; we propose differential adaptation as an operational formalization of adaptation for numerical problems. The solution to a target problem is designed on the basis of relations existing between a source case (problem and solution) and a target case. Differential adaptation relies on the metaphor of differential calculus where small variations on variable values are related to variations of function values. Accordingly, variations between problems correspond to variations between variable values and variations between solutions to variations between function values. Operators inspired from differential calculus are able to manipulate the variations and to support the whole adaptation process. Differential adaptation is operational and provides generic operators that can be reused for different real-world numerical situations

Man-Machine Collaboration to Acquire Cooking Adaptation Knowledge for the TAAABLE Case-Based Reasoning System

International audienceThis paper shows how humans and machines can better collaborate to acquire adaptation knowledge (AK) in the framework of a case-based reasoning (CBR) system whose knowledge is encoded in a semantic wiki. Automatic processes like the CBR reasoning process itself, or speci c tools for acquiring AK are integrated as wiki extensions. These tools and processes are combined on purpose to collect AK. Users are at the center of our approach, as they are in a classical wiki, but they will now bene t from automatic tools for helping them to feed the wiki. In particular, the CBR system, which is currently only a consumer for the knowledge encoded in the semantic wiki, will also be used for producing knowledge for the wiki. A use case in the domain of cooking is given to exemplify the man-machine collaboration

PETRA: Process Evolution using a TRAce-based system on a maintenance platform

To meet increasing needs in the field of maintenance, we studied the dynamic aspect of process and services on a maintenance platform, a major challenge in process mining and knowledge engineering. Hence, we propose a dynamic experience feedback approach to exploit maintenance process behaviors in real execution of the maintenance platform. An active learning process exploiting event log is introduced by taking into account the dynamic aspect of knowledge using trace engineering. Our proposal makes explicit the underlying knowledge of platform users by means of a trace-based system called “PETRA”. The goal of this system is to extract new knowledge rules about transitions and activities in maintenance processes from previous platform executions as well as its user (i.e. maintenance operators) interactions. While following a Knowledge Traces Discovery process and handling the maintenance ontology IMAMO, “PETRA” is composed of three main subsystems: tracking, learning and knowledge capitalization. The capitalized rules are shared in the platform knowledge base in order to be reused in future process executions. The feasibility of this method is proven through concrete use cases involving four maintenance processes and their simulation

Révisor : un ensemble de moteurs d'adaptation de cas par révision des croyances

National audienceL'adaptation par révision est une approche de l'adaptation de cas fondée sur l'utilisation d'un opérateur de révision des croyances. Le principe de cette adaptation est de réviser le cas source par le problème cible, autrement dit, elle consiste à modifier minimalement le premier pour qu'il soit cohérent avec le second. Plusieurs implantations de l'adaptation par révision ont été développées dans plusieurs formalismes. Trois d'entre eux sont actuellement disponibles dans Révisor, qui contient donc trois moteurs d'adaptation : Révisor/PL en logique propositionnelle, Révisor/CLC pour un formalisme de conjonction de contraintes linéaires et Révisor/QA pour une algèbre qualitative (notamment, l'algèbre de Allen, INDU et RCC8)