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

Collaborative CBR-based agents in the preparation of varied training lessons

International audienceCase‐Based Reasoning (CBR) is widely used as a means of intelligent tutoring and elearning systems. Indeed, course lessons are elaborated by analogy: this kind of system produces sets of exercises with respect to student level and class objective. Nevertheless, CBR systems always result in the same solution to a given problem description, whereas teaching requires that monotony be broken in order to maintain student motivation and attention. This is particularly true for sports where trainers must propose different exercises to practice the same skills for many weeks. We designed a system based on CBR that takes into account any previous lessons offered and designs new ones so as to vary the exercises each time: this system takes into account the solutions previously proposed so as to avoid giving the same lesson twice. In addition, this system is based on collaborative agents, each taking into account the exercises proposed by others so that each activity is proposed only once during a lesson. A sports trainer tested and evaluated the ability of this system as a means to design varied aïkido training lessons and proved that our system is capable of creating classroom activities that are diverse, changing, pertinent and consistent

Evaluating a textual adaptation system

International audienceThis paper presents a CBR method to retrieve and adapt processes represented as instruction texts, as well as the evaluation methodology that we developed to evaluate it. The evaluation process is user-based, blind and comparative. It is less labour intensive than most existing approaches and is more open to a variety of possible solutions to the same query, among other benefits. It also makes it possible to evaluate separately the textual adaptation process and the underlying formal adaptation process

A Roadmap for Natural Language Processing Research in Information Systems

Natural Language Processing (NLP) is now widely integrated into web and mobile applications, enabling natural interactions between human and computers. Although many NLP studies have been published, none have comprehensively reviewed or synthesized tasks most commonly addressed in NLP research. We conduct a thorough review of IS literature to assess the current state of NLP research, and identify 12 prototypical tasks that are widely researched. Our analysis of 238 articles in Information Systems (IS) journals between 2004 and 2015 shows an increasing trend in NLP research, especially since 2011. Based on our analysis, we propose a roadmap for NLP research, and detail how it may be useful to guide future NLP research in IS. In addition, we employ Association Rules (AR) mining for data analysis to investigate co-occurrence of prototypical tasks and discuss insights from the findings

A new strategy for case-based reasoning retrieval using classification based on association

Cased Based Reasoning (CBR) is an important area of research in the field of Artificial Intelli-gence. It aims to solve new problems by adapting solutions, that were used to solve previous similar ones. Among the four typical phases - retrieval, reuse, revise and retain, retrieval is a key phase in CBR approach, as the retrieval of wrong cases can lead to wrong decisions. To ac-complish the retrieval process, a CBR system exploits Similarity-Based Retrieval (SBR). How-ever, SBR tends to depend strongly on similarity knowledge, ignoring other forms of knowledge, that can further improve retrieval performance.The aim of this study is to integrate class association rules (CARs) as a special case of associa-tion rules (ARs), to discover a set (of rules) that can form an accurate classifier in a database. It is an efficient method when used to build a classifier, where the target is pre-determined. The proposition for this research is to answer the question of whether CARs can be integrated into a CBR system. A new strategy is proposed that suggests and uses mining class association rules from previous cases, which could strengthen similarity based retrieval (SBR). The propo-sition question can be answered by adapting the pattern of CARs, to be compared with the end of the Retrieval phase. Previous experiments and their results to date, show a link between CARs and CBR cases. This link has been developed to achieve the aim and objectives.A novel strategy, Case-Based Reasoning using Association Rules (CBRAR) is proposed to improve the performance of the SBR and to disambiguate wrongly retrieved cases in CBR. CBRAR uses CARs to generate an optimum frequent pattern tree (FP-tree) which holds a val-ue of each node. The possible advantage offered is that more efficient results can be gained, when SBR returns uncertain answers. In addition, CBRAR has been evaluated using two sources of CBR frameworks - Jcolibri and Free CBR. With the experimental evaluation on real datasets indicating that the proposed CBRAR is a better approach when compared to CBR systems, offering higher accuracy and lower error rate