Nowadays, the popularity of chatbots, such as ChatGPT, has brought research attention to question answering systems, capable to generate natural language answers to user’s natural language queries. However, also in other kinds of systems, flexibility of querying, including but also going beyond the use of natural language, is an important feature. With this consideration in mind the paper presents a critical and comprehensive analysis of recent developments, trends and challenges of Flexible Query Answering Systems (FQASs). Flexible query answering is a multidisciplinary research field that is not limited to question answering in natural language, but comprises other query forms and interaction modalities, which aim to provide powerful means and techniques for better reflecting human preferences and intentions to retrieve relevant information. It adopts methods at the crossroad of several disciplines among which Information Retrieval (IR), databases, knowledge based systems, knowledge and data engineering, Natural Language Processing (NLP) and the semantic web may be mentioned. The analysis principles are inspired by the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) framework, characterized by a top-down process, starting with relevant keywords for the topic of interest to retrieve relevant articles from meta-sources And complementing these articles with other relevant articles from seed sources Identified by a bottom-up process. to mine the retrieved publication data a network analysis is performed Which allows to present in a synthetic way intrinsic topics of the selected publications. issues dealt with are related to query answering methods Both model-based and data-driven (the latter based on either machine learning or deep learning) And to their needs for explainability and fairness to deal with big data Notably by taking into account data veracity. conclusions point out trends and challenges to help better shaping the future of the FQAS field.

In this paper, we represent (moving) point configurations along a curved directed line qualitatively by means of a system of relational symbols based on two distance descriptors: one representing distance along the curved directed line and the other representing signed orthogonal distance to the curved directed line. The curved directed line represents the direction of the movement of interest. For instance, it could be straight as in the case of driving along a highway or could be curved as in the case of an intersection or a roundabout. Inspired by the Point Calculus, the order between the points on the curved directed line is described by means of a small set of binary relations (<,=,>) acting upon the distance descriptors. We call this representation the Point-Descriptor-Precedence-Static (PDPS) representation at a time point and Point-Descriptor-Precedence-Dynamic (PDPD) representation during a time interval. To illustrate how the proposed approach can be used to represent and analyse curved movements, some basic micro-analysis traffic examples are studied. Finally, we discuss some extensions of our work to highlight the practical benefits of PDP in identifying motion patterns that could be useful in GIS, autonomous vehicles, sports analytics, and gait analysis.

With "human centric data management", we denote all kind of practical and theoretical developments that contribute to the improvement of data management for human users, such that it becomes better to understand, easier to handle, and more natural to communicate with.The popularity of digital applications, social media and multimedia created a shift towards "big data" that are characterized by huge data volumes, a large variety of data formats, fast data processing requirements, and veracity problems.The more data we have at our disposal, the more applications arise, but also the more sophisticated these applications become.Along with these technological developments comes the awareness that there is a growing need for human centric data management tools.Indeed, perfect data sets are rare and data imperfections propagate to imperfect data processing solutions.Humans communicate in natural language and cope with imperfect information in their everyday behavior, whereas conventional data management assumes that data are perfect and data manipulation is based on a bivalent Boolean logic.Computational intelligence techniques, more specifically soft computing and fuzzy set theory, offer the tools for bridging the gap between the way humans behave and communicate and the way conventional data management tools work.This is especially the case because they allow to generalize bivalent Boolean logic into multivalued fuzzy logic and offer sound foundations for uncertainty modeling that are less stringent, but broader applicable, than conventional probability theory.This special issue is an initiative of the working group on "Soft Computing in Database Management and Information Retrieval" of the European Society for

In a relational database, tuples are called “duplicate” if they describe the same real-world entity. If such duplicate tuples are observed, it is recommended to remove them and to replace them with one tuple that represents the joint information of the duplicate tuples to a maximal extent. This remove-and-replace operation is called a fusion operation. Within the setting of a relational database management system, the removal of the original duplicate tuples can breach referential integrity. In this paper, a strategy is proposed to maintain referential integrity in a semantically correct manner, thereby optimizing the quality of relationships in the database. An algorithm is proposed that is able to propagate a fusion operation through the entire database. The algorithm is based on a framework of first and second order fusion functions on the one hand, and conflict resolution strategies on the other hand. It is shown how classical strategies for maintaining referential integrity, such as DELETE cascading, are highly specialized cases of the proposed framework. Experimental results are reported that (i) show the efficiency of the proposed algorithm and (ii) show the differences in quality between several second order fusion functions. It is shown that some strategies easily outperform DELETE cascading.

The ongoing exponential growth of online information sources has led to a need for reliable and efficient algorithms for text clustering. In this paper, we propose a novel text model called the relational text model that represents each sentence as a binary multirelation over a concept space \documentclass{article}\usepackage{amssymb}\pagestyle{empty}\begin{document}${\mathcal{C}}$\end{document}. Through usage of the smart indexing engine (SIE), a patented technology of the Belgian company i.Know, the concept space adopted by the text model can be constructed dynamically. This means that there is no need for an a priori knowledge base such as an ontology, which makes our approach context independent. The concepts resulting from SIE possess the property that frequency of concepts is a measure for relevance. We exploit this property with the development of the CR-algorithm. Our approach relies on the representation of a data set \documentclass{article}\usepackage{amssymb}\pagestyle{empty}\begin{document}${\mathcal{D}}$\end{document} as a multirelation, of which k-cuts can be taken. These cuts can be seen as sets of relevant patterns with respect to the topics that are described by documents. Analysis of dependencies between patterns allows to produce clusters, such that precision is sufficiently high. The best k-cut is the one that best approximates the estimated number of clusters to ensure recall. Experimental results on Dutch news fragments show that our approach outperforms both basic and advanced methods. © 2012 Wiley Periodicals, Inc.

In this paper, we identify and describe fundamental logic properties of multicriteria methods for land-use suitability analysis and the design of suitability maps. The existing multicriteria methods can be evaluated from the standpoint of their ability to support the necessary logic properties that affect the expressive power of evaluation methods. The paper investigates and compares simple additive scoring, multiattribute value technique, multiattribute utility technique, analytic hierarchy process, ordered weighted average, outranking methods, and logic scoring of preference (LSP). We introduce canonical forms of logic aggregation in suitability maps and show how to use canonical aggregation structures to design LSP suitability maps that evaluate distributions of points of interests (POIs) in urban areas. © 2011 Wiley Periodicals, Inc.

When expressing their information needs in a (database) query, users sometimes prefer to state what has to be rejected rather than what has to be accepted. In general, what has to be rejected is not necessarily the complement of what has to be accepted. This phenomenon is commonly known as the heterogeneous bipolar nature of expressing information needs. Satisfaction degrees in regular fuzzy querying approaches are based on the “symmetric'' assumption that the extent to which a database record, respectively, satisfies and does not satisfy a given query are complements of each other and are therefore less suited to adequately handle heterogeneous bipolarity in query specifications and query processing. In this paper, we present a bipolar query satisfaction modeling framework which is based on pairs that consist of an independent degree of satisfaction and degree of dissatisfaction. The use and advantages of the framework are illustrated in the context of fuzzy query evaluation in regular relational databases. More specifically, the evaluation of heterogeneous bipolar queries that contain both positive, negative, and bipolar criteria is studied. © 2011 Wiley Periodicals, Inc.

The Sugeno integral has been identified and used as an aggregation operator many times in the past. In this paper, an extension of the Sugeno integral for the framework of possibilistic truth values is presented, resulting in a powerful domain-specific aggregation operator. Next, it is shown how the presented integral can be plugged into a reasoning framework for identification of coreferent objects, which are entity descriptions that refer to the same entity in a different way. The concept of hierarchical fuzzy measures, linked to an object structure is introduced, offering a new conditional possibilistic reasoning framework for object matching. © 2008 Wiley Periodicals, Inc.

Comparing objects can be considered as a hierarchical process. Separate aspects of objects are compared to each other, and the results of these comparisons are combined into a single result in one or more steps by aggregation operators. The set of operators used to compare the objects and the way these operators are related with each other is called the comparison scheme. If a threshold is applied to the final result of the object comparison, the mathematical properties of the operators in the comparison scheme can be used to derive thresholds on the intermediate results. These derived threshold can be used to break of a comparison early, thus offering a reduction of the comparison cost. Using this information, we show that the order in which the operators are evaluated has an influence on the average cost of comparing two objects. Next, we proceed with a study of the properties that allow us to find an optimal order, such that this average cost is minimized. Finally, we provide an algorithm that calculates an optimal order efficiently. Although specifically developed for object comparison, the algorithm can be applied to all kinds of selection processes that involve the combination of several test results. © 2009 Wiley Periodicals, Inc.

A problem with the modeling of uncertainty within the context of an information or knowledge-based system is the handling of missing information. In this article, extended possibilistic truth values are introduced as a formal means to cope with this problem. The notion of an extended possibilistic truth value has been obtained from the assumption that the truth value of a proposition can be undefined. This is the case if the proposition cannot be evaluated due to the non-applicability of (some of) its elements. By definition, an extended truth value can either be true, false, or undefined. Using these three values, a ternary strong Kleene propositional logic has been built. An uncertainty model for this logic is proposed, in order to model (linguistic) uncertainty concerning the extended truth value of a proposition. This uncertainty model is based on possibility measures, and leads to the concept of an extended possibilistic truth value. Finally, the algebraic properties of extended possibilistic truth values are presented. © 2002 Wiley Periodicals, Inc.