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

Improving Ingredient Substitution using Formal Concept Analysis and Adaptation of Ingredient Quantities with Mixed Linear Optimization

International audienceThis paper presents the participation of the Taaable team to the 2015 Computer Cooking Contest. The Taaable system addresses the mixology and the sandwich challenges. For the mixology challenge, the 2014 Taaable system was extended in two ways. First, a formal concept analysis approach is used to improve the ingredient substitution, which must take into account a limited set of available foods. Second, the adaptation of the ingredient quantities has also been improved in order to be more realistic with a real cooking setting. The adaptation of the ingredient quantities is based on a mixed linear optimization. The team also applied Taaable to the sandwich challenge

Spartan Daily, April 4, 2017

Volume 148, Issue 26https://scholarworks.sjsu.edu/spartan_daily_2017/1024/thumbnail.jp

Case Adaptation with Qualitative Algebras

This paper proposes an approach for the adaptation of spatial or temporal cases in a case-based reasoning system. Qualitative algebras are used as spatial and temporal knowledge representation languages. The intuition behind this adaptation approach is to apply a substitution and then repair potential inconsistencies, thanks to belief revision on qualitative algebras. A temporal example from the cooking domain is given. (The paper on which this extended abstract is based was the recipient of the best paper award of the 2012 International Conference on Case-Based Reasoning.

Knowledge extraction for improving case retrieval and recipe adaptation

International audienceTaaable 4 is a case-based cooking system which is a contestant in the fourth "Computer Cooking Contest", inheriting most of the features of its previous versions as well as adding new ones. Two new features both concerning knowledge acquisition using formal concept analysis (FCA) are proposed this year. The first feature uses FCA in order to enrich the domain ontology (especially the ingredient hierarchy), making the case retrieval more progressive and more precise. The second feature addresses explicitly the adaptation challenge: given a recipe R and some constraints, how can R be adapted. To compute the best way to adapt R, we propose a FCA-based method for extracting adaptation knowledge. These two knowledge extraction processes exploit additional data (73795 recipes) from the "Recipe Source" database

Spartan Daily, May 2, 1983

Volume 80, Issue 58https://scholarworks.sjsu.edu/spartandaily/7040/thumbnail.jp