Abstract The rise of social media has brought new dynamics to the dissemination of scientific research, with Twitter (X) playing a significant role. This study focuses on the role of social bots—automated accounts designed to mimic human behavior and amplify content—in scientific communication. By analyzing over 3.7 million papers published between 2017 and 2021 and their 51 million Twitter mentions. Using a novel hybrid method that includes BotometerLite and specific activity parameters, with verification via a robustness check, it was found that 0.23% of accounts were bots. Despite their small numbers, these bots contributed to 4.72% of all mentions, indicating a significant presence, but with varied impact. Bots were particularly active in Mathematics, Physics, and Space Sciences, where they generated over 70% of tweets in some cases. Automated accounts disproportionately influence the visibility and perceived impact of research in these disciplines, which underscores the need for discipline‐specific analysis when considering Twitter's role in scientific communication. This large‐scale study highlights the potential for bots to skew altmetric indicators, misleading stakeholders about true engagement.

Abstract Universities face increasing demands to improve their visibility, public outreach, and online presence. There is a broad consensus that scientific reputation significantly increases the attention universities receive. However, in most cases estimates of scientific reputation are based on composite or weighted indicators and absolute positions in university rankings. In this study, we adopt a more granular approach to assessment of universities' scientific performance using a multidimensional set of indicators from the Leiden Ranking and testing their individual effects on university Wikipedia page views. We distinguish between international and local attention and find a positive association between research performance and Wikipedia attention which holds for regions and linguistic areas. Additional analysis shows that productivity, scientific impact, and international collaboration have a curvilinear effect on universities' Wikipedia attention. This finding suggests that there may be other factors than scientific reputation driving the general public's interest in universities. Our study adds to a growing stream of work which views altmetrics as tools to deepen science–society interactions rather than direct measures of impact and recognition of scientific outputs.

As a novel architecture of a fuzzy decision tree constructed on fuzzy rules, the fuzzy rule-based decision tree (FRDT) achieved better performance in terms of both classification accuracy and the size of the resulted decision tree than other classical decision trees such as C4.5, LADtree, BFtree, SimpleCart and NBTree. The concept of Z-number extends the classical fuzzy number to model both uncertain and partial reliable information. Z-numbers have significant potential in rule-based systems due to their strong representation capability. This paper designs a Z-number-valued rule-based decision tree (ZRDT) and provides the learning algorithm. Firstly, the information gain is used to replace the fuzzy confidence in FRDT to select features in each rule. Additionally, we use the negative samples to generate the second fuzzy numbers that adjust the first fuzzy numbers and improve the model's fit to the training data. The proposed ZRDT is compared with the FRDT with three different parameter values and two classical decision trees, PUBLIC and C4.5, and a decision tree ensemble method, AdaBoost.NC, in terms of classification effect and size of decision trees. Based on statistical tests, the proposed ZRDT has the highest classification performance with the smallest size for the produced decision tree.

Information that people rely on is often uncertain and partially reliable. Zadeh introduced the concept of Z-numbers as a more adequate formal construct for describing uncertain and partially reliable information. Most existing applications of Z-numbers involve discrete ones due to the high complexity of calculating continuous ones. However, the continuous form is the most common form of information in the real world. Simplifying continuous Z-number calculations is significant for practical applications. There are two reasons for the complexity of continuous Z-number calculations: the use of normal distributions and the inconsistency between the meaning and definition of Z-numbers. In this paper, we extend the triangular distribution as the hidden probability density function of triangular Z-numbers. We add a new parameter to the triangular distribution to influence its convexity and concavity, and then expand the value's domain of the probability measure. Finally, we implement the basic operations of triangular Z-numbers based on the extended triangular distribution. The suggested method is illustrated with numerical examples, and we compare its computational complexity and the entropy (uncertainty) of the resulting Z-number to the traditional method. The comparison shows that our method has lower computational complexity, higher precision and lower uncertainty in the results.

Companies invest a substantial amount of time and resources in ensuring the compliance to the existing regulations or in the form of fines when compliance cannot be proven in auditing procedures. The topic is not only relevant, but also highly complex, given the frequency of changes and amendments, the complexity of the cases and the difficulty of the juristic language. This paper aims at applying advanced extractive summarization to democratize the understanding of regulations, so that non-jurists can decide which regulations deserve further follow-up. To achieve that, we first create a corpus named EUR-LexSum EUR-LexSum containing 4595 curated European regulatory documents and their corresponding summaries. We then fine-tune transformer-based models which, applied to this corpus, yield a superior performance (in terms of ROUGE metrics) compared to a traditional extractive summarization baseline. Our experiments reveal that even with limited amounts of data such transformer-based models are effective in the field of legal document summarization.

Clustering analysis is a key technique in reducing the dimensionality of high volume irregular data containing large-scale group decision-making (LSGDM) information. Uncertainty theory is suitable for subjective estimation or situation, such as lack of historical data, and it can be employed to effectively express the uncertainty of trust and preference information in LSGDM problems. This paper studies the dimensionality reduction and subgroup optimization in LSGDM by utilizing linear uncertain variables in social networks. A clustering method is proposed to decompose the large group into several subgroups of higher consilience degrees and higher preference similarities, and lower the dimension of information for LSGDM. In the clustering process, two measurement attributes, trust relationship and preference relationship of decision-makers, are combined, and information consistent degree is utilized as the clustering indicator. This approach does not need to preset the threshold and the number of subgroups, and can be employed to obtain subgroups with similar preferences and stable trust relationship. Through the clustering reliability evaluation of subgroups, the rationality of large-scale group clustering results is verified. Subgroup consensus contribution is used to identify superior subgroups and quantify the role of subgroups in improving the consensus level. An example of emergency decision-making and comparative analysis is provided to explain the feasibility and advantages of the proposed method.

Human personality plays a crucial role in decision-making and it has paramount importance when individuals negotiate with each other to reach a common group decision. Such situations are conceivable, for instance, when a group of individuals want to watch a movie together. It is well known that people influence each other’s decisions, the more assertive a person is, the more influence they will have on the final decision. In order to obtain a more realistic group recommendation system (GRS), we need to accommodate the assertiveness of the different group members’ personalities. Although pairwise preferences are long-established in group decision-making (GDM), they have received very little attention in the recommendation systems community. Driven by the advantages of pairwise preferences on ratings in the recommendation systems domain, we have further pursued this approach in this paper, however we have done so for GRS. We have devised a three-stage approach to GRS in which we 1) resort to three binary matrix factorization methods, 2) develop an influence graph that includes assertiveness and cooperativeness as personality traits, and 3) apply an opinion dynamics model in order to reach consensus. We have shown that the final opinion is related to the stationary distribution of a Markov chain associated with the influence graph. Our experimental results demonstrate that our approach results in high precision and fairness.

Urban resettlement projects involve a large number of stakeholders and impose tremendous cost. Developing resettlement plans and reaching an agreement amongst stakeholders about resettlement plans at a reasonable cost are some of the key issues in urban resettlement. From this perspective, urban resettlement is a typical large-scale group decision-making (GDM) problem, which is challenging because of the scale of participants and the requirement of high consensus levels. Observing that residents who are affected by a resettlement project often have tight social connections, this study proposes a framework to improve the consensus reaching and uses the minimum consensus cost to reduce the total cost for urban resettlement projects with more than 1000 participants. Firstly, we construct a network topology that consists of two layers to deal with incomplete social relationships amongst large-scale participants. An inner layer consists of participants whose preference similarities and trust relations are known. Meanwhile, an outside layer includes participants whose trust relations cannot be determined. Secondly, we develop a classification method to classify participants into small subgroups based on their preference similarities. We can then connect the participants whose trust relations are unknown (the outside layer) with the ones in the inner layer using the classification results. To facilitate effective consensus reaching in large-scale social network GDM, we develop a three-step approach to reconcile conflicting preferences and accelerate the consensus process at the minimum cost. A real-life urban resettlement example is used to validate the proposed approach. Results show that the proposed approach can reduce the total consensus cost compared with the other two practices used in the actual urban resettlement operations.

Recent multi-criteria group decision making methods focus their analysis on the experts preferences. They do not take into account the reasons why each expert has provided a specific set of preferences. In this paper, a method that introduces novel measures capable of explaining the reasons behind experts decisions is presented. A novel concept, the arguments are presented. They represent the experts have for maintaining a certain position in the debate. Several measures related to the arguments are proposed. These new argumentation measures, along with consensus measures, help us to get a clear idea about how and why a specific resolution has been reached. They help us to determine which is the most influential expert, that is, the expert whose contributions to the debate have inspired the rest. Also, the proposed method allows us to determine which are the arguments that most of the experts have followed. A clear overview about how the debate is evolving in terms of arguments is also provided. The novel presented analysis indicate how the experts change their opinions in every round and what was the reason for it, which changes have occurred between rounds and they also provide global analysis results.

The variance is a statistical measure frequently used for analysis of dispersion in the data. This paper presents new types of variances that use Bonferroni means and ordered weighted averages in the aggregation process of the variance. The main advantage of this approach is that we can underestimate or overestimate the variance according to the attitudinal character of the decision-maker. The work considers several particular cases including the minimum and the maximum variance and presents some numerical examples. The article also develops some extensions and generalizations by using induced aggregation operators and generalized and quasi-arithmetic means. These approaches provide a more general framework that can consider a lot of other particular cases and a complex attitudinal character that could be affected by a wide range of variables. The study ends with an application of the new approach in a business decision-making problem regarding strategic analysis in enterprise risk management.

Nowadays, in the social network–based decision-making processes, like the ones involved in e-commerce and e-democracy, multiple users with different backgrounds may take part and diverse alternatives might be involved. This diversity enriches the process, but at the same time, increases the uncertainty of opinions. This uncertainty can be considered from two different perspectives: (i) the uncertainty in the meaning of the words given as preferences, that is, motivated by the heterogeneity of the decision makers; and (ii) the uncertainty inherent to any decision-making process that may lead to an expert not being able to provide all their judgments. The main objective of this study is to address these two types of uncertainty. To do so, the following approaches are proposed: First, to capture, process, and keep the uncertainty in the meaning of the linguistic assumption, the Interval Type-2 Fuzzy Sets are introduced as a way to model the experts' linguistic judgments. Second, a measure of the coherence of the information provided by each decision maker is proposed. Finally, a consistency-based completion approach is introduced to deal with the uncertainty presented in the expert judgments. The proposed approach is tested in an e-democracy decision-making scenario.

On-line platforms foster the communication capabilities of the Internet to develop large-scale influence networks in which the quality of the interactions can be evaluated based on trust and reputation. So far, this technology is well known for building trust and harnessing cooperation in on-line marketplaces, such as Amazon (www.amazon.com) and eBay (www.ebay.es). However, these mechanisms are poised to have a broader impact on a wide range of scenarios, from large scale decision making procedures, such as the ones implied in e-democracy, to trust based recommendations on e-health context or influence and performance assessment in e-marketing and e-learning systems. This contribution surveys the progress in understanding the new possibilities and challenges that trust and reputation systems pose. To do so, it discusses trust, reputation and influence which are important measures in networked based communication mechanisms to support the worthiness of information, products, services opinions and recommendations. The existent mechanisms to estimate and propagate trust and reputation, in distributed networked scenarios, and how these measures can be integrated in decision making to reach consensus among the agents are analysed. Furthermore, it also provides an overview of the relevant work in opinion dynamics and influence assessment, as part of social networks. Finally, it identifies challenges and research opportunities on how the so called trust based network can be leveraged as an influence measure to foster decision making processes and recommendation mechanisms in complex social networks scenarios with uncertain knowledge, like the mentioned in e-health and e-marketing frameworks.

Although fuzzy preference relations (FPRs) are among the most commonly used preference models in group decision making (GDM), they are not free from drawbacks. First of all, especially when dealing with many alternatives, the definition of FPRs becomes complex and time consuming. Moreover, they allow to focus on only two options at a time. This facilitates the expression of preferences but let experts lose the global perception of the problem with the risk of introducing inconsistencies that impact negatively on the whole decision process. For these reasons, different preference models are often adopted in real GDM settings and, if necessary, transformation functions are applied to obtain equivalent FPRs. In this paper, we propose fuzzy rankings, a new approximate preference model that offers a higher level of user-friendliness with respect to FPRs while trying to maintain an adequate level of expressiveness. Fuzzy rankings allow experts to focus on two alternatives at a time without losing the global picture so reducing inconsistencies. Conversion algorithms from fuzzy rankings to FPRs and backward are defined as well as similarity measures, useful when evaluating the concordance between experts’ opinion. A comparison of the proposed model with related works is reported as well as several explicative examples.

In decision making, a widely used methodology to manage unbalanced fuzzy linguistic information is the linguistic hierarchy (LH), which relies on a linguistic symbolic computational model based on ordinal 2-tuple linguistic representation. However, the ordinal 2-tuple linguistic approach does not exploit all advantages of Zadeh's fuzzy linguistic approach to model uncertainty because the membership function shapes are ignored. Furthermore, the LH methodology is an indirect approach that relies on the uniform distribution of symmetric linguistic assessments. These drawbacks are overcome by applying a fuzzy methodology based on the implementation of the type-1 ordered weighted average (T1OWA) operator. The T1OWA operator is not a symbolic operator and it allows to directly aggregate membership functions, which in practice means that the T1OWA methodology is suitable for both balanced and unbalanced linguistic contexts and with heterogeneous membership functions. Furthermore, the final output of the T1OWA methodology is always fuzzy and defined in the same domain of the original unbalanced fuzzy linguistic labels, which facilitates its interpretation via a visual joint representation. A case study is presented where the T1OWA operator methodology is used to assess the creditworthiness of European bonds based on real credit risk ratings of individual Eurozone member states modeled as unbalanced fuzzy linguistic labels.

Decision situations in which several individual are involved are known as group decision-making (GDM) problems. In such problems, each member of the group, recognizing the existence of a common problem, tries to come to a collective decision. A high level of consensus among experts is needed before reaching a solution. It is customary to construct consensus measures by using similarity functions to quantify the closeness of experts preferences. The use of a metric that describes the distance between experts preferences allows the definition of similarity functions. Different distance functions have been proposed in order to implement consensus measures. This paper examines how the use of different aggregation operators affects the level of consensus achieved by experts through different distance functions, once the number of experts has been established in the GDM problem. In this situation, the experimental study performed establishes that the speed of the consensus process is significantly affected by the use of diverse aggregation operators and distance functions. Several decision support rules that can be useful in controlling the convergence speed of the consensus process are also derived.

Currently, there are a large number of hotel Web sites that develop their own seals of quality based on customer feedback. As a result, a hotel can be classified differently by various Web sites at the same time, creating confusion in the consumer perceptions about the quality of a given hotel. Moreover, there are attempts to standardize such service quality evaluation, such as the SERVQUAL instrument, which is a multiple-item scale for measuring service quality with several dimensions. In this context, we present a two-stage linguistic multicriteria decision-making model to integrate the hotel guests' opinions included in several Web sites, with two objectives: on the one hand, obtaining a SERVQUAL scale evaluation value of service quality with the integrated answers of the input opinions; on the other hand, getting a SERVQUAL overall evaluation value of service quality. This model is incorporated into an opinion aggregation architecture to integrate heterogeneous data (natural language included) from various tourism Web sites. As a particular case study, we show an application example using the high-end hotels located in Granada (Spain). © 2012 Wiley Periodicals, Inc.

This article presents a new open‐source software tool, SciMAT , which performs science mapping analysis within a longitudinal framework. It provides different modules that help the analyst to carry out all the steps of the science mapping workflow. In addition, SciMAT presents three key features that are remarkable in respect to other science mapping software tools: (a) a powerful preprocessing module to clean the raw bibliographical data, (b) the use of bibliometric measures to study the impact of each studied element, and (c) a wizard to configure the analysis.

Science mapping aims to build bibliometric maps that describe how specific disciplines, scientific domains, or research fields are conceptually, intellectually, and socially structured. Different techniques and software tools have been proposed to carry out science mapping analysis. The aim of this article is to review, analyze, and compare some of these software tools, taking into account aspects such as the bibliometric techniques available and the different kinds of analysis.

One of the key issues in dynamic research areas, such as that of biomedical sciences, is the development of tools capable to retrieve and provide users relevant resources from large repositories according to their information needs. In this paper, we present a filtering and recommender system that applies Semantic Web technologies and fuzzy linguistic modeling techniques to provide users valuable information about resources that fit their interests. To carry out the recommendation process, we have defined three software agents (interface, task, and information agents) that are distributed in a five level hierarchical architecture. The system is also capable of to deal with incomplete information to define enriched user profiles and, therefore, soften the problem of cold start. A simple evaluation has been carried out, and the experimental outcomes reveal a reasonable good performance of the system in terms of precision and recall. © 2010 Wiley Periodicals, Inc.

Abstract Thomson Reuters' Web of Science (WoS) is undoubtedly a great tool for scientiometrics purposes. It allows one to retrieve and compute different measures such as the total number of papers that satisfy a particular condition; however, it also is well known that this tool imposes several different restrictions that make obtaining certain results difficult. One of those constraints is that the tool does not offer the total count of documents in a dataset if it is larger than 100,000 items. In this article, we propose and analyze different approaches that involve partitioning the search space (using the Source field) to retrieve item counts for very large datasets from the WoS. The proposed techniques improve previous approaches: They do not need any extra information about the retrieved dataset (thus allowing completely automatic procedures to retrieve the results), they are designed to avoid many of the restrictions imposed by the WoS, and they can be easily applied to almost any query. Finally, a description of WoS Query Partitioner, a freely available and online interactive tool that implements those techniques, is presented.

The aim of this paper is to propose a procedure to estimate missing preference values when dealing with incomplete fuzzy linguistic preference relations assessed using a two-tuple fuzzy linguistic approach. This procedure attempts to estimate the missing information in an individual incomplete fuzzy linguistic preference relation using only the preference values provided by the respective expert. It is guided by the additive consistency property to maintain experts' consistency levels. Additionally, we present a selection process of alternatives in group decision making with incomplete fuzzy linguistic preference relations and analyze the use of our estimation procedure in the decision process. © 2008 Wiley Periodicals, Inc.

Abstract In this article, our interest is focused on the automatic learning of Boolean queries in information retrieval systems (IRSs) by means of multi‐objective evolutionary algorithms considering the classic performance criteria, precision and recall. We present a comparative study of four well‐known, general‐purpose, multi‐objective evolutionary algorithms to learn Boolean queries in IRSs. These evolutionary algorithms are the Nondominated Sorting Genetic Algorithm (NSGA‐II), the first version of the Strength Pareto Evolutionary Algorithm (SPEA), the second version of SPEA (SPEA2), and the Multi‐Objective Genetic Algorithm (MOGA).

In this paper, we present a procedure to estimate missing preference values when dealing with pairwise comparison and heterogeneous information. This procedure attempts to estimate the missing information in an expert's incomplete preference relation using only the preference values provided by that particular expert. Our procedure to estimate missing values can be applied to incomplete fuzzy, multiplicative, interval-valued, and linguistic preference relations. Clearly, it would be desirable to maintain experts' consistency levels. We make use of the additive consistency property to measure the level of consistency and to guide the procedure in the estimation of the missing values. Finally, conditions that guarantee the success of our procedure in the estimation of all the missing values of an incomplete preference relation are given. © 2008 Wiley Periodicals, Inc.

Nowadays, the information access on the Web is a main problem in the computer science community. Any major advance in the field of information access on the Web requires the collaboration of different methodologies and research areas. In this paper, the concept of aggregation operator playing a role for information access on the Web is analyzed. We present some Web methodologies, as search engines, recommender systems, and Web quality evaluation models and analyze the way aggregation operators help toward the success of their activities. We also show an application of the aggregation operators in digital libraries. In particular, we introduce a Web information system to analyze the quality of digital libraries that implements an important panel of aggregation operators to obtain the quality assessments. © 2008 Wiley Periodicals, Inc.

Most information retrieval systems based on linguistic approaches use symmetrically and uniformly distributed linguistic term sets to express the weights of queries and the relevance degrees of documents. However, to improve the system–user interaction, it seems more adequate to express these linguistic weights and degrees by means of unbalanced linguistic scales, that is, linguistic term sets with different discrimination levels on both sides of the middle linguistic term. In this contribution we present an information retrieval system that accepts weighted queries whose weights are expressed using unbalanced linguistic term sets. Then, the system provides the retrieved documents classified in linguistic relevance classes assessed on unbalanced linguistic term sets. To do so, we propose a methodology to manage unbalanced linguistic information and we use the linguistic 2-tuple model as the representation base of the unbalanced linguistic information. Additionally, the linguistic 2-tuple model allows us to increase the number of relevance classes in the output and also to improve the performance of the information retrieval system. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 1197–1214, 2007.

Abstract The World Wide Web is a popular and interactive medium used to collect, disseminate, and access an increasingly huge amount of information, which constitutes the mainstay of the so‐called information and knowledge society. Because of its spectacular growth, related to both Web resources (pages, sites, and services) and number of users, the Web is nowadays the main information repository and provides some automatic systems for locating, accessing, and retrieving information. However, an open and crucial question remains: how to provide fast and effective retrieval of the information relevant to specific users' needs. This is a very hard and complex task, since it is pervaded with subjectivity, vagueness, and uncertainty. The expression soft computing refers to techniques and methodologies that work synergistically with the aim of providing flexible information processing tolerant of imprecision, vagueness, partial truth, and approximation. So, soft computing represents a good candidate to design effective systems for information access and retrieval on the Web. One of the most representative tools of soft computing is fuzzy set theory. This special topic section collects research articles witnessing some recent advances in improving the processes of information access and retrieval on the Web by using soft computing tools, and in particular, by using fuzzy sets and/or integrating them with other soft computing tools. In this introductory article, we first review the problem of Web retrieval and the concept of soft computing technology. We then briefly introduce the articles in this section and conclude by highlighting some future research directions that could benefit from the use of soft computing technologies.

Abstract An evaluation methodology based on fuzzy computing with words aimed at measuring the information quality of Web sites containing documents is presented. This methodology is qualitative and user oriented because it generates linguistic recommendations on the information quality of the content‐based Web sites based on users' perceptions. It is composed of two main components, an evaluation scheme to analyze the information quality of Web sites and a measurement method to generate the linguistic recommendations. The evaluation scheme is based on both technical criteria related to the Web site structure and criteria related to the content of information on the Web sites. It is user driven because the chosen criteria are easily understandable by the users, in such a way that Web visitors can assess them by means of linguistic evaluation judgments. The measurement method is user centered because it generates linguistic recommendations of the Web sites based on the visitors' linguistic evaluation judgments. To combine the linguistic evaluation judgments we introduce two new majority guided linguistic aggregation operators, the Majority guided Linguistic Induced Ordered Weighted Averaging (MLIOWA) and weighted MLIOWA operators, which generate the linguistic recommendations according to the majority of the evaluation judgments provided by different visitors. The use of this methodology could improve tasks such as information filtering and evaluation on the World Wide Web.

Information retrieval is an activity that attempts to produce documents that better fulfill user information needs. To achieve this activity an information retrieval system uses matching functions that specify the degree of relevance of a document with respect to a user query. Assuming linguistic-weighted queries we present a new linguistic matching function for a threshold weighting semantics that is defined using a 2-tuple fuzzy linguistic approach (Herrera F, Martínez L. IEEE Trans Fuzzy Syst 2000;8:746–752). This new 2-tuple linguistic matching function can be interpreted as a tuning of that defined in “Modelling the Retrieval Process for an Information Retrieval System Using an Ordinal Fuzzy Linguistic Approach” (Herrera-Viedma E. J Am Soc Inform Sci Technol 2001;52:460–475). We show that it simplifies the processes of computing in the retrieval activity, avoids the loss of precision in final results, and, consequently, can help to improve the users' satisfaction. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 921–937, 2005.

In this article, we introduce the induced ordered weighted geometric (IOWG) operator and its properties. This is a more general type of OWG operator, which is based on the induced ordered weighted averaging (IOWA) operator. We provide some IOWG operators to aggregate multiplicative preference relations in group decision-making (GDM) problems. In particular, we present the importance IOWG (I-IOWG) operator, which induces the ordering of the argument values based on the importance of the information sources; the consistency IOWG (C-IOWG) operator, which induces the ordering of the argument values based on the consistency of the information sources; and the preference IOWG (P-IOWG) operator, which induces the ordering of the argument values based on the relative preference values associated with each one of them. We also provide a procedure to deal with “ties” regarding the ordering induced by the application of one of these IOWG operators. This procedure consists of a sequential application of the aforementioned IOWG operators. Finally, we analyze the reciprocity and consistency properties of the collective multiplicative preference relations obtained using IOWG operators. © 2004 Wiley Periodicals, Inc.

This special issue encompasses eight papers devoted to recent developments in the field of preference modeling.The issue originates from presentations at the EUROFUSE Workshop on Preference Modeling and Applications (EUROFUSE 2001) that was held in Granada, Spain, April 25-27, 2001.These eight original contributions have been revised thoroughly and expanded to become the articles currently presented in this issue.Preference modeling is a fundamental step in solving problems in various fields such as economics, medical diagnosis, information retrieval, decision theory, etc.Most of these problems take place in a complex environment where uncertain and imprecise knowledge and possibly vague preferences have to be considered.To face such complexity, preference modeling requires the use of specific techniques and concepts that allow the available information to be represented appropriately.As is well known, the fuzzy approach has led to considerable advances in preference modeling, mainly because the use of fuzzy techniques increases the reliability and flexibility of decision models.The present issue includes different proposals of fuzzy preference modeling in several application fields.The first group of articles is focused on the study of different aspects of preference modeling in Multicriteria Decision Making

The ordered weighted geometric (OWG) operator is an aggregation operator that is based on the ordered weighted averaging (OWA) operator and the geometric mean. Its application in multicriteria decision making (MCDM) under multiplicative preference relations has been presented. Some families of OWG operators have been defined. In this article, we present the origin of the OWG operator and we review its relationship to the OWA operator in MCDM models. We show a study of its use in multiplicative decision-making models by providing the conditions under which reciprocity and consistency properties are maintained in the aggregation of multiplicative preference relations performed in the selection process. © 2003 Wiley Periodicals, Inc.

A linguistic model for an Information Retrieval System (IRS) defined using an ordinal fuzzy linguistic approach is proposed. The ordinal fuzzy linguistic approach is presented, and its use for modeling the imprecision and subjectivity that appear in the user-IRS interaction is studied. The user queries and IRS responses are modeled linguistically using the concept of fuzzy linguistic variables. The system accepts Boolean queries whose terms can be weighted simultaneously by means of ordinal linguistic values according to three possible semantics: a symmetrical threshold semantic, a quantitative semantic, and an importance semantic. The first one identifies a new threshold semantic used to express qualitative restrictions on the documents retrieved for a given term. It is monotone increasing in index term weight for the threshold values that are on the right of the mid-value, and decreasing for the threshold values that are on the left of the mid-value. The second one is a new semantic proposal introduced to express quantitative restrictions on the documents retrieved for a term, i.e., restrictions on the number of documents that must be retrieved containing that term. The last one is the usual semantic of relative importance that has an effect when the term is in a Boolean expression. A bottom-up evaluation mechanism of queries is presented that coherently integrates the use of the three semantics and satisfies the separability property. The advantage of this IRS with respect to others is that users can express linguistically different semantic restrictions on the desired documents simultaneously, incorporating more flexibility in the user–IRS interaction.