Journal of Intelligent Information Systems 

About: Journal of Intelligent Information Systems is an academic journal published by Springer Science+Business Media. The journal publishes majorly in the area(s): Computer science & Recommender system. It has an ISSN identifier of 0925-9902. Over the lifetime, 461 publications have been published receiving 5038 citations.

An overview of cooperative answering

Abstract: Databases and information systems are often hard to use because they do not explicitly attempt to cooperate with their users. Direct answers to database and knowledge base queries may not always be the best answers. Instead, an answer with extra or alternative information may be more useful and less misleading to a user. This paper surveys foundational work that has been done toward endowing intelligent information systems with the ability to exhibit cooperative behavior. Grice's maxims of cooperative conversation, which provided a starting point for the field of cooperative answering, are presented along with relevant work in natural language dialogue systems, database query answering systems, and logic programming and deductive databases. The paper gives a detailed account of cooperative techniques that have been developed for considering users' beliefs and expectations, presuppositions, and misconceptions. Also, work in intensional answering and generalizing queries and answers is covered. Finally, the Cooperative Answering System at Maryland, which is intended to be a general, portable platform for supporting a wide spectrum of cooperative answering techniques, is described.

Some approaches for relational databases flexible querying

Abstract: One of the main objectives of third generation databases is to design database management systems which provide users with more and more functionalities. In such a wide context, various proposals have been made in order to introduce some kind of explicit or implicit flexibility into user queries. In this paper, we propose a classification of the various approaches dealing with imprecise queries. Moreover, we show that the approach based on fuzzy sets is powerful enough to answer a wide range of imprecise queries in an appropriate way and to support the expression of the capabilities available in the other classes of solutions. An outline of an SQL-like language allowing for a variety of imprecise queries is also presented.

Relaxation as a platform for cooperative answering

Abstract: Responses to queries posed by a user of a database do not always contain the information desired. Database answers to a query, although they may be logically correct, can sometimes be misleading. Research in the area of cooperative answering for databases and deductive databases seeks to rectify these problems. We introduce a cooperative method calledrelaxation for expanding deductive database and logic programming queries. The relaxation method expands the scope of a query by relaxing the constraints implicit in the query. This allows the database to return answers related to the original query as well as the literal answers themselves. These additional answers may be of interest to the user. In section 1 we introduce the problem and method. In Section 2 we give some background on the research done in cooperative answering. Section 3 discusses the relaxation method, a potential control strategy, and uses. Section 4 looks at a semantic counterpart to this notion. In Section 5 we explore some of the control and efficiency issues. We enumerate open issues in Section 6, and conclude in Section 7.

Recommender systems in the healthcare domain: state-of-the-art and research issues

Abstract: Nowadays, a vast amount of clinical data scattered across different sites on the Internet hinders users from finding helpful information for their well-being improvement. Besides, the overload of medical information (e.g., on drugs, medical tests, and treatment suggestions) have brought many difficulties to medical professionals in making patient-oriented decisions. These issues raise the need to apply recommender systems in the healthcare domain to help both, end-users and medical professionals, make more efficient and accurate health-related decisions. In this article, we provide a systematic overview of existing research on healthcare recommender systems. Different from existing related overview papers, our article provides insights into recommendation scenarios and recommendation approaches. Examples thereof are food recommendation, drug recommendation, health status prediction, healthcare service recommendation, and healthcare professional recommendation. Additionally, we develop working examples to give a deep understanding of recommendation algorithms. Finally, we discuss challenges concerning the development of healthcare recommender systems in the future.

Selecting among rules induced from a hurricane database

Abstract: Rule induction can achieve orders of magnitude reduction in the volume of data descriptions. For example, we applied a commercial tool (IXLtm) to a 1,819 record tropical storm database, yielding 161 rules. However, the human comprehension goals of Knowledge Discovery in Databases may require still more orders, of magnitude. We present a rule refinement strategy, partly implemented in a Prolog program, that operationalizes "interestingness" into performance, simplicity, novelty, and significance. Applying the strategy to the induced rulebase yielded 10 "genuinely interesting" rules.