Gabriella Pasi

A Fuzzy Linguistic Approach Generalizing Boolean Information Retrieval: A Model and Its Evaluation.

The generalization of Boolean Information Retrieval Systems (IRS) is still an open research field; in fact, though such systems are diffused on the market, they present some limitations; one of the main features lacking in these systems is the ability to deal with the “imprecision” and “subjectivity” characterizing retrieval activity. However, the replacement of such systems would be much more costly than their evolution through the incorporation of new features to enhance their efficiency and effectiveness. Previous efforts in this area have led to the introduction of numeric weights to improve both document representation and query language. By attaching a numeric weight to a term in a query, a user can provide a quantitative description of the “importance” of that term in the documents he or she is looking for. However, the use of weights requires a clear knowledge of their semantics for translating a fuzzy concept into a precise numeric value. Our acquaintance with these problems led us to define, starting from an existing weighted Boolean retrieval model, a linguistic extension, formalized within fuzzy set theory, in which numeric query weights are replaced by linguistic descriptors which specify the degree of importance of the terms. This fuzzy linguistic model is defined and an evaluation is made of its implementation on a Boolean IRS.

Linguistic aggregation operators of selection criteria in fuzzy information retrieval

A “softening” of the hard Boolean scheme for information retrieval is presented. In this approach, information retrieval is seen as a multicriteria decision‐making activity in which the criteria to be satisfied by the potential solutions, i.e., the archived documents, are the requirements expressed in the query. the retrieval function is then an overall decision function evaluating the degree to which each potential solution satisfies a query consisting of information requirements aggregated by operators. Linguistic quantifiers and a connector dealing with primary and optional criteria are defined and introduced in the query language in order to specify the aggregation criteria of the single query requirements. These criteria make it possible for users to express queries in a simple and self‐explanatory manner. In particular, linguistic quantifiers are defined which capture the intrinsic vagueness of information needs.

Fuzzy Logic and Applications

Cluster analysis of high-dimensional data has become of special interest in recent years. The term high-dimensional data can refer to a larger number of attributes – 20 or more – as they often occur in database tables. But high-dimensional data can also mean that we have to deal with thousands of attributes as in the context of genomics or proteomics data where thousands of genes or proteins are measured and are considered in some analysis tasks as attributes. A main reason, why cluster analysis of high-dimensional data is different from clustering low-dimensional data, is the concentration of norm phenomenon, which states more or less that the relative differences between distances between randomly distributed points tend to be more and more similar in higher dimensions. On the one hand, fuzzy cluster analysis has been shown to be less sensitive to initialisation than, for instance, the classical k-means algorithm. On the other, standard fuzzy clustering is stronger affected by the concentration of norm phenomenon and tends to fail easily in high dimensions. Here we present a review of why fuzzy clustering has special problems with high-dimensional data and how this can be amended by modifying the fuzzifier concept. We also describe a recently introduced approach based on correlation and an attribute selection fuzzy clustering technique that can be applied when clusters can only be found in lower dimensions.

A fuzzy object-oriented data model for managing vague and uncertain information†

In this paper, a fuzzy Object‐Oriented Data model (FOOD) is defined based on the extension of a Graph‐based Object model (D. Lucarella and A. Zanzi “A graph‐oriented data model,” in Database and Expert Systems Applications, R. Wagner and H. Toma, Eds., Springer‐Verlag, Berlin, 1996, pp. 197–206), in order to manage both crisp and imperfect information. These capabilities are requisites of many current applications dealing with data of different nature and with complex interrelationships. The model is based on a visual paradigm which supports both the representation of the data semantics and the direct browsing of the information. In the extended model both the database scheme and instances are represented as directed labeled graphs in which the fuzzy and uncertain information has its own representation. ©1999 John Wiley & Sons, Inc.

Modeling the concept of majority opinion in group decision making

In this paper the problem of group decision making is studied. One of the main issues in this context is to define a decision strategy which takes into account the individual opinions of the decision makers. The concept of majority plays in this context a key role: what is often needed is an overall opinion which synthesizes the opinions of the majority of the decision makers. The reduction of the individual values into a representative value (which we call the majority opinion) is usually performed through an aggregation process. Within fuzzy set theory the concept of majority can be expressed by a linguistic quantifier (such as most), which is formally defined as a fuzzy subset. In this paper we propose two distinct approaches to the definition of a majority opinion. We first consider the case where linguistic quantifiers are associated with aggregation operators which allow us to compute a majority opinion by aggregating the individual opinions. In this case the majority opinion corresponds to the aggregated value. To model this semantics of linguistic quantifiers the IOWA operators are used and a new proposal of definition of their weighting vector is presented. A second method is based on the consideration of the concept of majority as a vague concept. Based on this interpretation we propose a formalization of a fuzzy majority opinion as a fuzzy subset.

Applications of Fuzzy Sets Theory

Advances in Fuzzy Set Theory.- From Fuzzy Beliefs to Goals.- Information Entropy and Co-entropy of Crisp and Fuzzy Granulations.- Possibilistic Linear Programming in Blending and Transportation Planning Problem.- Measuring the Interpretive Cost in Fuzzy Logic Computations.- A Fixed-Point Theorem for Multi-valued Functions with an Application to Multilattice-Based Logic Programming.- Contextualized Possibilistic Networks with Temporal Framework for Knowledge Base Reliability Improvement.- Reconstruction of the Matrix of Causal Dependencies for the Fuzzy Inductive Reasoning Method.- Derivative Information from Fuzzy Models.- The Genetic Development of Uninorm-Based Neurons.- Using Visualization Tools to Guide Consensus in Group Decision Making.- Reconstruction Methods for Incomplete Fuzzy Preference Relations: A Numerical Comparison.- Fuzzy Information Access and Retrieval.- Web User Profiling Using Fuzzy Clustering.- Exploring the Application of Fuzzy Logic and Data Fusion Mechanisms in QAS.- Fuzzy Indices of Document Reliability.- Fuzzy Ontology, Fuzzy Description Logics and Fuzzy-OWL.- Fuzzy Machine Learning.- An Improved Weight Decision Rule Using SNNR and Fuzzy Value for Multi-modal HCI.- DWT-Based Audio Watermarking Using Support Vector Regression and Subsampling.- Improving the Classification Ability of DC* Algorithm.- Combining One Class Fuzzy KNNs.- Missing Clusters Indicate Poor Estimates or Guesses of a Proper Fuzzy Exponent.- An Analysis of the Rule Weights and Fuzzy Reasoning Methods for Linguistic Rule Based Classification Systems Applied to Problems with Highly Imbalanced Data Sets.- Fuzzy Clustering for the Identification of Hinging Hyperplanes Based Regression Trees.- Evaluating Membership Functions for Fuzzy Discrete SVM.- Improvement of Jarvis-Patrick Clustering Based on Fuzzy Similarity.- Fuzzy Rules Generation Method for Pattern Recognition Problems.- Outliers Detection in Selected Fuzzy Regression Models.- Possibilistic Clustering in Feature Space.- Fuzzy Architectures and Systems.- OpenAdap.net: Evolvable Information Processing Environment.- Binary Neuro-Fuzzy Classifiers Trained by Nonlinear Quantum Circuits.- Digital Hardware Implementation of High Dimensional Fuzzy Systems.- Optimization of Hybrid Electric Cars by Neuro-Fuzzy Networks.- Fuzzy k-NN Lung Cancer Identification by an Electronic Nose.- Efficient Implementation of SVM Training on Embedded Electronic Systems.- A Possible Approach to Cope with Uncertainties in Space Applications.- Special Session on Intuitionistic Fuzzy Sets: Recent Advances.- Fuzzy Measures: Collectors of Entropies.- Some Problems with Entropy Measures for the Atanassov Intuitionistic Fuzzy Sets.- Twofold Extensions of Fuzzy Datalog.- Combs Method Used in an Intuitionistic Fuzzy Logic Application.- Intuitionistic Fuzzy Spatial Relationships in Mobile GIS Environment.- A Two-Dimensional Entropic Approach to Intuitionistic Fuzzy Contrast Enhancement.- Intuitionistic Fuzzy Histogram Hyperbolization for Color Images.- Special Session on Soft Computing in Image Processing.- Computer Vision and Pattern Recognition in Homeland Security Applications.- A Genetic Algorithm Based on Eigen Fuzzy Sets for Image Reconstruction.- Fuzzy Metrics Application in Video Spatial Deinterlacing.- Fuzzy Directional-Distance Vector Filter.- Color Texture Segmentation with Local Fuzzy Patterns and Spatially Constrained Fuzzy C-Means.- A Flexible System for the Retrieval of Shapes in Binary Images.- Fuzzy C-Means Segmentation on Brain MR Slices Corrupted by RF-Inhomogeneity.- Dilation and Erosion of Spatial Bipolar Fuzzy Sets.- About the Embedding of Color Uncertainty in CBIR Systems.- Evolutionary Cellular Automata Based-Approach for Edge Detection.- Special Session Third International Workshop on Cross-Language Information Processing (CLIP 2007).- The Multidisciplinary Facets of Research on Humour.- Multi-attribute Text Classification Using the Fuzzy Borda Method and Semantic Grades.- Approximate String Matching Techniques for Effective CLIR Among Indian Languages.- Using Translation Heuristics to Improve a Multimodal and Multilingual Information Retrieval System.- Ontology-Supported Text Classification Based on Cross-Lingual Word Sense Disambiguation.- Opinion Analysis Across Languages: An Overview of and Observations from the NTCIR6 Opinion Analysis Pilot Task.- Some Experiments in Humour Recognition Using the Italian Wikiquote Collection.- Recognizing Humor Without Recognizing Meaning.- Computational Humour: Utilizing Cross-Reference Ambiguity for Conversational Jokes.- Special Session Fourth International Meeting on Computational Intelligence Methods for Bioinformatics Biostatistics (CIBB 2007).- Dataset Complexity and Gene Expression Based Cancer Classification.- A Novel Hybrid GMM/SVM Architecture for Protein Secondary Structure Prediction.- A Graph Theoretic Approach to Protein Structure Selection.- Time-Series Alignment by Non-negative Multiple Generalized Canonical Correlation Analysis.- Generative Kernels for Gene Function Prediction Through Probabilistic Tree Models of Evolution.- Liver Segmentation from CT Scans: A Survey.- Clustering Microarray Data with Space Filling Curves.- Fuzzy Ensemble Clustering for DNA Microarray Data Analysis.- Signal Processing in Comparative Genomics.- PCA Based Feature Selection Applied to the Analysis of the International Variation in Diet.- Evaluating Switching Neural Networks for Gene Selection.- Analysis of Proteomic Spectral Data by Multi Resolution Analysis and Self-Organizing Maps.- A Cost-Sensitive Approach to Feature Selection in Micro-Array Data Classification.- Liknon Feature Selection for Microarrays.- An Alternative Splicing Predictor in C.Elegans Based on Time Series Analysis.- Cancer Classification Based on Mass Spectrometry.- Computational Proteomics of Biomolecular Interactions in Sequence and Structure Space of the Tyrosine Kinome: Evolutionary Constraints and Protein Conformational Selection Determine Binding Signatures of Cancer Drugs.- Learning Transcriptional Regulatory Networks with Evolutionary Algorithms Enhanced with Niching.- Towards a Personalized Schedule with Triplex Vaccine.- Solving Protein Structures Using Molecular Replacement Via Protein Fragments.- An Interactive Tool for the Management and Visualization of Mass-Spectrometry Proteomics Data.- Smart Sequence Similarity Search(S4) System.- Prediction of over Represented Transcription Factor Binding Sites in Co-regulated Genes Using Whole Genome Matching Statistics.- Unsupervised Haplotype Reconstruction and LD Blocks Discovery in a Hidden Markov Framework.- Multi-class Protein Fold Recognition Through a Symbolic-Statistical Framework.- Assessment of Common Regions and Specific Footprints of DNA Copy Number Aberrations Across Multiple Affymetrix SNP Mapping Arrays.- Locally Adaptive Statistical Procedures for the Integrative Analysis on Genomic and Transcriptional Data.

Fuzzy Set Techniques in Information Retrieval

In this chapter an overview of the application of fuzzy set theory to soften Information Retrieval Systems is presented. It starts with the description of the main functionalities of an Information Retrieval System, then the main information retrieval models defined in the literature are reviewed and a classification of fuzzy information retrieval models is proposed. Further the fuzzy indexing procedures based on the computation of the significance of the document’s descriptors are illustrated and the introduction of soft requirements into queries, based on both numeric and linguistic weights and soft aggregation operators is discussed. The chapter presents also the fuzzy associative retrieval models based on thesauri, pseudothesauri, and documents clustering and relevance feedback techniques. Finally, in the last section some evaluation issues of IRSs are introduced.

An extended fuzzy linguistic approach to generalize Boolean information retrieval

Abstract The generalization of Boolean information retrieval systems is still of interest to scholars. In spite of the fact that commercial systems use Boolean retrieval mechanisms, such systems still have some limitations. One of the main problems is that such systems lack the ability to deal well with imprecision and subjectivity. Previous efforts have led to the introduction of numeric weights to improve both document representations (term weights) and query languages (query weights). However, the use of weights requires a clear knowledge of the semantics of the query in order to translate a fuzzy concept into a precise numeric value. Moreover, it is difficult to model the matching of queries to documents in a way that will preserve the semantics of user queries. A linguistic extension has been generated, starting from an existing Boolean weighted retrieval model and formalized within fuzzy set theory, in which numeric query weights are replaced by linguistic descriptors that specify the degree of importance of the terms. In the past, query weights were seen as measures of the importance of a specific term in representing the query or as a threshold to aid in matching a specific document to the query. The linguistic extension was originally modeled to view the query weights as a description of the ideal document, so that deviations would be rejected whether a given document had term weights that were too high or too low. This paper looks at an extension to the linguistic model that is not symmetric in that documents with a term weight below the query weight are treated differently than documents with a term weight above the query weight.

Lectures on information retrieval

Information Retrieval (IR) is concerned with the effective and efficient retrieval of information based on its semantic content. The central problem in IR is the quest to find the set of relevant documents, among a large collection containing the information sought, satisfying a users information need usually expressed in a natural language query. Documents may be objects or items in any medium: text, image, audio, or indeed a mixture of all three.

A fuzzy object oriented data model

The increasing complexity of real applications in the field of multimedia information systems requires the enhancement of modelling capabilities of object oriented data models (OODMs) in order to deal with imprecise and uncertain data. Some fuzzy extensions of the OODMs have been proposed in the literature, in which imprecision and uncertainty are managed at the level of object attributes and relations. What is still lacking is a unifying and systematic formalization of these extensions. In this contribution, starting from an existing graph-based-object model, the authors propose a fuzzy object oriented data (FOOD) model for the management of imprecise data.<<ETX>>