Sławomir Zadrożny

Computing with words in intelligent database querying: standalone and internet-based application

Abstract We present how computing with words, meant as a set of fuzzy-logic-based tools for an effective and efficient handling of imprecise elements of natural language, can be implemented for fuzzy querying via a user-friendly interface to Microsoft Access, FQUERY for Access. The system accommodates fuzzy (imprecise) terms and linguistic quantifiers allowing for queries exemplified by “find (all) records such that most of the (important) clauses are satisfied (to a degree from [0,1])”. L.A. Zadehs [Comput. Math. Appl. 9 (1983) 149] fuzzy logic based calculus of linguistically quantified propositions, and R.R. Yagers [IEEE Trans. Syst. Man Cybernet. 18 (1988) 183] ordered weighted averaging (OWA) operators are employed to deal with fuzzy linguistic quantifiers. It is then shown how FQUERY for Access, which is a standalone application, may be extended to support fuzzy querying via the Internet (or, analogously, Intranet). It is shown how WWW browsers, both the Netscape Navigator and Microsoft Explorer, can be employed for developing a fuzzy querying interface for handling imprecise natural language elements in database queries following Zadehs computing with words paradigm.

Computing With Words Is an Implementable Paradigm: Fuzzy Queries, Linguistic Data Summaries, and Natural-Language Generation

We point out some relevant issues that are related to the computing-with-words (CWW) paradigm and argue for an urgent need for a new, nontraditional look at the area, since the traditional approach has resulted in very valuable theoretical research results. However, there is no proper exposure and recognition in other areas to which CWW belongs and can really contribute, notably natural-language processing (NLP), in general, and natural-language understanding (NLU) and natural-language generation (NLG), in particular. First, we present crucial elements of CWW, in particular Zadehs protoforms, and indicate their power and stress a need to develop new tools to handle more modalities. We argue that CWW also has a high implementation potential and present our approach to linguistic data(base) summaries, which is a very intuitive and human-consistent natural-language-based knowledge-discovery tool. Special emphasis is on the use of Zadehs protoform (prototypical form) as a general form of a linguistic data summary. We present an extension of our interactive approach, which is based on fuzzy logic and fuzzy database queries, to implement such linguistic summaries. In the main part of the paper, we discuss a close relation between linguistic summarization in the sense considered and some basic ideas and solutions in NLG, thus analyzing possible common elements and an opportunity to use developed tools, as well as some inherent differences and difficulties. Notably, we indicate a close relation of linguistic summaries that are considered to be some type of an extended template-based, and even a simple phrase-based, NLG system and emphasize a possibility to use software that is available in these areas. An important conclusion is also an urgent need to develop new protoforms, thus going beyond the classical ones of Zadeh. For illustration, we present an implementation for a sales database in a computer retailer, thereby showing the power of linguistic summaries, as well as an urgent need for new types of protoforms. Although we use linguistic summaries throughout, our discussion is also valid for CWW in general. We hope that this paper-which presents our personal view and perspective that result from our long-time involvement in both theoretical work in broadly perceived CWW and real-world implementations-will trigger a discussion and research efforts to help find a way out of a strange situation in which, on one hand, one can clearly see that CWW is related to words (language) and computing and, hence, should be part of broadly perceived mainstream computational linguistics, which lack tools to handle imprecision. These tools can be provided by CWW. Yet, CWW is practically unknown to these communities and is not mentioned or cited, and---reciprocally---even the top people in CWW do not refer to the results that are obtained in these areas. We hope that our paper, for the benefit of both the areas, will help bridge this gap that results from a wrong and dangerous fragmentation of break science.

FQUERY III+: A “human-consistent” database querying system based on fuzzy logic with linguistic quantifiers

Using a fuzzy-logic-based calculus of linguistically quantified propositions we present FQUERY III+, a new, more “human-friendly” and easier-to-use implementation of a querying scheme proposed originally by Kacprzyk and Ziolkowski to handle imprecise queries including a linguistic quantifier as, e.g. find all records in which most (almost all, much more than 75%, … or any other linguistic quantifier) of the important attributes (out of a specified set) are as desired (e.g. equal to five, more than 10, large, more or less equal to 15, etc.). FQUERY III+ is an “add-on” to Ashton-Tates dBase III Plus.

An interactive multi-user decision support system for consensus reaching processes using fuzzy logic with linguistic quantifiers

Abstract We present an interactive user-friendly microcomputer-based decision support system for consensus reaching processes. The point of departure is a group of individuals (experts, decision makers,…) who present their testimonies (opinions) in the form of individual fuzzy preference relations. Initially, these opinions are usually quite different, i.e., the group is far from consensus. Then, in a multistage session a moderator, who is supervising the session, tries to make the individuals change their testimonies by, e.g., rational argument, bargaining, etc. to eventually get closer to consensus. For gauging and monitoring the process a new ‘soft’ degree (measure) of consensus is used whose essence is the determination to what degree, e.g., ‘most of the individuals agree as to almost all of the relevant options’. A fuzzy-logic-based calculus of linguistically quantified propositions is employed.

Fquery for Access: Fuzzy Querying for a Windows-Based DBMS

We present an “add-on” to Microsoft Access, one of new Microsoft Windows based popular DBMSs, which makes possible the use of queries that allow for a more intelligent and human consistent information retrieval. More specifically, fuzzy (imprecise) descriptions and linguistic quantifiers are accommodated to allow for queries as, e.g., “find (all) records such that most of the (important) clauses are satisfied (to a degree from [0,1])”. Zadeh’s (1983) fuzzy logic based calculus of linguistically quantified propositions is employed.

Soft computing and Web intelligence for supporting consensus reaching

A novel idea and architecture of a group decision support system for reaching consensus in a group of individuals (agents) is proposed. The core of the system is the preferences modeling and consensus assessment module, which is based on fuzzy logic. However, the focus is on providing the members of the group with an information- and knowledge-rich environment, and tools to make an effective and efficient use of such an environment. This should help the agent gain proper opinions about the issues and opinions and/or attitudes of other agents, articulate proper testimonies, actively contribute to the discussion, and finally make sound and informed decisions that can help constructively run the consensus reaching process. For this purpose modern Web-based technologies are employed and tightly integrated with the core of the system.

Fuzzy Linguistic Summaries of Databases for an Efficient Business Data Analysis and Decision Support

We present the use of fuzzy logic for the derivation of linguistic summaries of data (databases) for providing efficient and human consistent means for the analysis of large amounts of data to be used for a more realistic business decision support. We concentrate on the issue of how to measure the goodness of a linguistic summary, and on how to embed data summarization within the fuzzy querying environment, for an effective and efficient computer implementation. Finally, we present an implementation for deriving linguistic summaries of a sales database at a small-to-medium size computer retailer. By analyzing the linguistic summaries obtained we indicate how they can help make decisions by the business owner.

Bipolar Queries Using Various Interpretations of Logical Connectives

In [1,2] we studied various concepts of bipolar queries (cf. Dubois and Prade [3]). We advocated the use of a fuzzified version of the original crisp definition by Lacroix and Lavency [4]. However, the general fuzzification proposed leaves open the choice of a representation of logical connectives and quantifiers. In the present paper we study the influence of the choice some representations that are popular in fuzzy logic on matching degrees of the tuples and their resulting ordering.

Handling Bipolarity in Elementary Queries to Possibilistic Databases

Making data-querying and representation easier and more human consistent is an important research topic. In this context, fuzzy logic with its capability to model linguistic expressions provides an interesting framework, which has been adopted by many researchers. However, there are still some aspects that have not been adequately covered. In particular, it becomes widely advocated that while communicating, humans give both positive and negative information to state what they desire and what they reject. Because positive and negative statements do not necessarily mirror each other, this results in so-called heterogeneous bipolar information. Traditional fuzzy approaches do not adequately support the handling of heterogeneous bipolar information in information systems. Therefore, there is a need for more advanced techniques. In this paper, how bipolarity can be dealt with in the formulation and evaluation of selection conditions in fuzzy querying within a possibilistic, relational database framework is presented. Three novel query-evaluation techniques based on interval-valued fuzzy sets, Atanassov fuzzy sets, and twofold fuzzy sets are presented and compared with each other. Possibility theory is used to deal with uncertainty. Special attention is paid to the description of the semantics, use, benefits, and drawbacks of each formalism.

Bipolar queries revisited

The concept of bipolar query, recently proposed by Dubois and Prade, is studied. A broader context for this concept is provided based on earlier approaches of other authors. Some properties of the aggregation operators involved are analyzed.