Cooperative answers in database systems

A major concern of researchers who seek to improve human-computer communication involves how to move beyond literal interpretations of queries to a level of responsiveness that takes the user's misconceptions, expectations, desires, and interests into consideration. At Maryland, we are investigating how to better meet a user's needs within the framework of the cooperative answering system of Gal and Minker. We have been exploring how to use semantic information about the database to formulate coherent and informative answers. The work has two main thrusts: (1) the construction of a logic formula which embodies the content of a cooperative answer; and (2) the presentation of the logic formula to the user in a natural language form. The information that is available in a deductive database system for building cooperative answers includes integrity constraints, user constraints, the search tree for answers to the query, and false presuppositions that are present in the query. The basic cooperative answering theory of Gal and Minker forms the foundation of a cooperative answering system that integrates the new construction and presentation methods. This paper provides an overview of the cooperative answering strategies used in the CARMIN cooperative answering system, an ongoing research effort at Maryland. Section 2 gives some useful background definitions. Section 3 describes techniques for collecting cooperative logical formulae. Section 4 discusses which natural language generation techniques are useful for presenting the logic formula in natural language text. Section 5 presents a diagram of the system

Arabic Cooperative Answer Generation via Wikipedia Article Infoboxes

[EN] The typical question-answering system is facing many challenges related to the processing of questions and information resources in the extraction and generation of adequate answers. These challenges increase when the requested answer is cooperative and its language is Arabic. In this paper, we propose an original approach to generate cooperative answers for user-definitional questions designed to be integrated in a question-answering system. This approach is mainly based on the exploitation of the semi-structured Web knowledge which consists in using features derived from Wikipedia article infoboxes to generate cooperative answers. It is globally independent of a particular language, which gives it the ability to be integrated in any definitional question-answering system. We have chosen to integrate and experiment it in a definitional question-answering system dealing with the Arabic language entitled DefArabicQA. The results showed that this system has a significant impact on the approach efficiency regarding the improvement of the quality of the answer.The work of the third author was partially funded by the Spanish Ministry of Economy, Industry and Competitiveness (MINECO) under the SomEMBED research project (TIN2015-71147-C2-1-P) and by the Generalitat Valenciana under the grant ALMAMATER (PrometeoII/2014/030).Trigui, O.; Belguith, L.; Rosso, P. (2017). Arabic Cooperative Answer Generation via Wikipedia Article Infoboxes. Research in Computing Science. 132:129-153. http://hdl.handle.net/10251/103731S12915313

The Architecture of a Cooperative Respondent

If natural language question-answering (NLQA) systems are to be truly effective and useful, they must respond to queries cooperatively, recognizing and accommodating in their replies a questioner\u27s goals, plans, and needs. Transcripts of natural dialogue demonstrate that cooperative responses typically combine several communicative acts: a question may be answered, a misconception identified, an alternative course of action described and justified. This project concerns the design of cooperative response generation systems, NLQA systems that are able to provide integrated cooperative responses. Two questions must be answered before a cooperative NLQA system can be built. First, what are the reasoning mechanisms that underlie cooperative response generation? In partial reply, I argue that plan evaluation is an important step in the process of selecting a cooperative response, and describe several tests that may usefully be applied to inferred plans. The second question is this: what is an appropriate architecture for cooperative NLQA (CNLQA) systems? I propose a four-level decomposition of the cooperative response generation process and then present a suitable CNLQA system architecture based on the blackboard model of problem solving

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

Attribute-level Neighbor Hierarchy Construction Using Evolved Pattern-based Knowledge Induction

Neighbor knowledge construction is the foundation for the development of cooperative query answering systems capable of searching for close match or approximate answers when exact match answers are not available. This paper presents a technique for developing neighbor hierarchies at the attribute level. The proposed technique is called the evolved Pattern-based Knowledge Induction (ePKI) technique and allows construction of neighbor hierarchies for nonunique attributes based upon confidences, popularities, and clustering correlations of inferential relationships among attribute values. The technique is applicable for both categorical and numerical (discrete and continuous) attribute values. Attribute value neighbor hierarchies generated by the ePKI technique allow a cooperative query answering system to search for approximate answers by relaxing each individual query condition separately. Consequently, users can search for approximate answers even when the exact match answers do not exist in the database (i.e., searching for existing similar parts as part of the implementation of the concepts of rapid prototyping). Several experiments were conducted to assess the performance of the ePKI in constructing attribute-level neighbor hierarchies. Results indicate that the ePKI technique produces accurate neighbor hierarchies when strong inferential relationships appear among data. © 2006 IEEE

A Review on Cooperative Question-Answering Systems

The Question-Answering (QA) systems fall in the study area of Information Retrieval (IR) and Natural Language Processing (NLP). Given a set of documents, a QA system tries to obtain the correct answer to the questions posed in Natural Language (NL). Normally, the QA systems comprise three main components: question classification, information retrieval and answer extraction. Question classification plays a major role in QA systems since it classifies questions according to the type in their entities. The techniques of information retrieval are used to obtain and to extract relevant answers in the knowledge domain. Finally, the answer extraction component is an emerging topic in the QA systems. This module basically classifies and validates the candidate answers. In this paper we present an overview of the QA systems, focusing on mature work that is related to cooperative systems and that has got as knowledge domain the Semantic Web (SW). Moreover, we also present our proposal of a cooperative QA for the SW

Planning Responses From High-Level Goals: Adopting the Respondent\u27s Perspective Cooperative Response Generation

Within the natural-language research community it has long been acknowledged that the conventions and pragmatics of natural-language communication often oblige dialogue systems to consider and address the underlying purposes of queries in their responses rather than answering them literally and without further comment or elaboration. Such systems cannot simply translate their users\u27 requests into transactions on database or expert systems, but must apply many more complex reasoning mechanisms to the task of selecting responses that are both appropriate and useful. This idea has given rise to a broadly-defined program of research in cooperative response generation (CRG). Research in CRG carried on over more than a decade has yielded a substantial body of literature. Analysis of that literature, however, shows that investigators have focused primarily on modeling manifestations of cooperative behavior without directly considering the nature and motivations of the behavior itself. But if we want to develop natural language dialogue systems that are truly to function as cooperative respondents instead of serving only as models of particular kinds of cooperative responses, a different approach is required. I identify two opposing perspectives on the process of cooperative response generation: the questioner-based and the respondent-based perspectives. I argue that past research efforts have largely been questioner-based, and that this view has led to the development of theories that are incompatible and cannot be integrated. I propose the respondent-based view as an alternative, and provide evidence that taking such a perspective might allow several interesting but otherwise poorly-understood aspects of cooperative response behavior to be modeled. The final portion of the dissertation explores the computational implications of a respondent-based perspective. I outline the architecture of a Cooperative Response Planning System, a dialogue system that raises, reasons about, and attempts to satisfy high-level cooperative goals in its responses. This architecture constitutes a first approximation to a theory of how a system might reason from the beliefs it derives from a questioner\u27s utterances to choose a cooperative response. The processing of two sample responses in this framework is described in detail to illustrate the architecture\u27s capabilities

ULASAN: KEBERHASILAN BELAJAR SISWA MELALUI PENERAPAN PEMBELAJARAN KOOPERATIF TIPE STAD

One way learning method could lead students unfocused and  difficult to gain learning outcomes . Currently, the Comparative learning model by Student Team Achievement Division (STAD) it is largely used in teaching. Therefore, this review was made to understand and find out the impact of the learning model system in the class. The study is based on literatures with the following criteria  (1) STAD cooperative type with a quasi-experiment and (2) research subjects were at the senior high school at MIPA class. We obtained 14 articles are similar with the criteria. Each of the article shortly describe the research finding. Indicator of student success with cooperative type STAD can be seen from the average value above 50%. It was found that the STAD type of cooperative learning model lead the creation of  actively students mood, creative, and independent learning atmosphere for students in receiving, processing, and answering learning material. This learning model is likely one of the most adaptive model learning,  modifications some elements are required depend on characteristic  and environment learning locally