Construction de réponses coopératives : du corpus à la modélisation informatique

Les stratégies utilisées pour la recherche d’information dans le cadre du Web diffèrent d’un moteur de recherche à un autre, mais en général, les résultats obtenus ne répondent pas directement et simplement à la question posée. Nous présentons une stratégie qui vise à définir les fondements linguistiques et de communication d’un système d’interrogation du Web qui soit coopératif avec l’usager et qui tente de lui fournir la réponse la plus appropriée possible dans sa forme et dans son contenu. Nous avons constitué et analysé un corpus de questions-réponses coopératives construites à partir des sections Foire Aux Questions (FAQ) de différents services Web aux usagers. Cela constitue à notre sens une bonne expérimentation de ce que pourrait être une communication directe en langue naturelle sur le Web. Cette analyse de corpus a permis d’extraire les caractéristiques majeures du comportement coopératif et de construire l’architecture de notre système informatique webcoop, que nous présentons à la fin de cet article.Algorithms and strategies used on the Web for information retrieval differ from one search engine to another, but, in general, results do not lead to very accurate and informative answers. In this paper, we describe our strategy for designing a cooperative question answering system that aims at producing the most appropriate answers to natural language questions. To characterize these answers, we collected a corpus of cooperative question in our opinion answer pairs extracted from Frequently Asked Questions. The analysis of this corpus constitutes a good experiment on what a cooperative natural language communication on the Web could be. This analysis allows for the elaboration of a general architecture for our cooperative question answering system webcoop, which we present at the end of this paper

COOPERATIVE QUERY ANSWERING FOR APPROXIMATE ANSWERS WITH NEARNESS MEASURE IN HIERARCHICAL STRUCTURE INFORMATION SYSTEMS

Cooperative query answering for approximate answers has been utilized in various problem domains. Many challenges in manufacturing information retrieval, such as: classifying parts into families in group technology implementation, choosing the closest alternatives or substitutions for an out-of-stock part, or finding similar existing parts for rapid prototyping, could be alleviated using the concept of cooperative query answering. Most cooperative query answering techniques proposed by researchers so far concentrate on simple queries or single table information retrieval. Query relaxations in searching for approximate answers are mostly limited to attribute value substitutions. Many hierarchical structure information systems, such as manufacturing information systems, store their data in multiple tables that are connected to each other using hierarchical relationships - "aggregation", "generalization/specialization", "classification", and "category". Due to the nature of hierarchical structure information systems, information retrieval in such domains usually involves nested or jointed queries. In addition, searching for approximate answers in hierarchical structure databases not only considers attribute value substitutions, but also must take into account attribute or relation substitutions (i.e., WIDTH to DIAMETER, HOLE to GROOVE). For example, shape transformations of parts or features are possible and commonly practiced. A bar could be transformed to a rod. Such characteristics of hierarchical information systems, simple query or single-relation query relaxation techniques used in most cooperative query answering systems are not adequate. In this research, we proposed techniques for neighbor knowledge constructions, and complex query relaxations. We enhanced the original Pattern-based Knowledge Induction (PKI) and Distribution Sensitive Clustering (DISC) so that they can be used in neighbor hierarchy constructions at both tuple and attribute levels. We developed a cooperative query answering model to facilitate the approximate answer searching for complex queries. Our cooperative query answering model is comprised of algorithms for determining the causes of null answer, expanding qualified tuple set, expanding intersected tuple set, and relaxing multiple condition simultaneously. To calculate the semantic nearness between exact-match answers and approximate answers, we also proposed a nearness measuring function, called "Block Nearness", that is appropriate for the query relaxation methods proposed in this research

Explanation in information systems: A design rationale approach.

This dissertation investigates the relationship between the information systems (IS) development context, and the context in which such systems are used. Misunderstandings and ambiguities emerge in the space between these contexts and often result in construction of systems that fail to meet the requirements and expectations of their intended users. This study explores this problem using an approach derived from three largely separate and distinct fields: explanation facilities in information systems, theories of explanation, and design rationale. Explanation facilities are typically included in knowledge-based information systems, where their purpose is to provide system users with the underlying reasons for why the system reaches a particular conclusion or makes a particular recommendation. Prior research suggests that the presence of an explanation facility leads to increased acceptance of these conclusions and recommendations, therefore enhancing system usability. Theory of explanation is a field of study in which philosophers attempt to describe the unique nature of explanation and to identify criteria for explanation evaluation. Design rationale research is concerned with the capture, representation, and use of the deep domain and artefact knowledge that emerges from the design process. The design rationale approach goes beyond specification and suggests that to understand a system requires knowledge of the arguments that led to its realisation. This study proposes a model of IS explanation structure and content derived from formal theories of explanation with a method for obtaining this content based on design rationale. The study has four goals: to derive a theory of explanation specific to the domain of information systems; to examine this definition empirically through a study involving IS development and management professionals; to investigate in a case study whether the information needed to populate the explanation model can be captured using design rationale techniques; and construction of prototype software to deliver explanations per the proposed framework

Explanation for Cooperative Information Systems

: Cooperative Information Systems provide approximate answers to a user's query when exact answers are unavailable and summary answers when answer sets are too large. Yet without an explanation of how and why such answers were derived, it is hard to estimate their usefulness. Further, the timing and level of detail of such explanations should be user and context dependent. In this paper, we present the architecture, representation, and process of an explanation system which provides such explanations of approximate answers. This explanation system has been implemented and integrated into the Cooperative Information System CoBase (UCLA) for a transportation planning and an electronic warfare domain. Our experience reveals that explanation generation is efficient and receives positive feedback from users. Keywords: Intelligent Information Systems, Cooperative Information Systems, Explanation, Approximate Answers, Dialogue Management, Knowledge Integration, CoBase. 1.0 Introduction Tra..

Generation, Refinement, and Extension of Explanation for Cooperative Information Systems

ion Hierarchies) are the principle knowledge structure in CoBase used to guide the query transformation process. and answers (Figure 2.1). A client sends the explanation system a simple explanation request on pieces of this information (e.g. describe query, explain answers) and receives back an explanation reply consisting of natural language and recommended visualizations that solve the request. The client presents the explanation on their GUI and a simple protocol enables the user to interact with initial explanations, causing the explanation system to generate follow up explanations. The explanation system maintains a model of query processing, consisting of concepts and classification rules, by which it interprets CoBase queries, TAHs, execution traces and answers. The explanation system applies generation rules to produce explanation replies from explanation requests. Concepts Interpreted Instances Uninterpreted Instances Explanation Request Subgoal Primitive Communication Actio..