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2017 | Book

Abductive Question-Answer System () for Classical Propositional Logic Read chapter Read first chapter

Flexible Query Answering Systems

12th International Conference, FQAS 2017, London, UK, June 21–22, 2017, Proceedings

Editors: Henning Christiansen, Hélène Jaudoin, Panagiotis Chountas, Troels Andreasen, Henrik Legind Larsen

Publisher: Springer International Publishing

Book Series : Lecture Notes in Computer Science

Part of: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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About this book

This book constitutes the refereed proceedings of the 12th International Conference on Flexible Query Answering Systems, FQAS 2017, held in London, UK, in June 2017.

The 21 full papers presented in this book together with 4 short papers were carefully reviewed and selected from 43 submissions. The papers cover the following topics: foundations of flexible querying; recommendation and ranking; technologies for flexible representations and querying; knowledge discovery and information/data retrieval; intuitionistic sets; and generalized net model.

Table of Contents

Frontmatter

Foundations of Flexible Querying

Frontmatter
Abductive Question-Answer System () for Classical Propositional Logic
Abstract
We propose a new approach to modelling abductive reasoning by means of an abductive question-answer system. We introduce the concept of an abductive question which is the starting point of abductive reasoning. The result of applying the question processing procedure is a question, which is simpler than the initial one. \(\mathsf {AQAS}\) generates abductive hypotheses that fulfil certain criteria in one step, i.e. processes of generation and evaluation of abductive hypotheses are integrated.
Szymon Chlebowski, Andrzej Gajda
Querying with Vague Quantifiers Using Probabilistic Semantics
Abstract
Many realistic scenarios call for answers to questions involving vague expressions like almost all, about half, or at least about a third. We present a modular extension of classical first-order queries over relational databases, with binary, proportional, semi-fuzzy quantifiers modeling such expressions via random sampling. The extended query language has an intuitive semantics and allows one to pose natural queries with probabilistic answers. This is also demonstrated by experiments with an implementation involving the (geographical) MONDIAL data set.
Christian G. Fermüller, Matthias Hofer, Magdalena Ortiz
Towards Analogy-Based Decision - A Proposal
Abstract
This short paper outlines an analogy-based decision method. It takes advantage of analogical proportions between situations, i.e., a is to b as c is to d, for proposing plausibly good decisions that may be appropriate for a new situation at hand. It goes beyond case-based decision where the idea of graded similarity may hide some small but crucial differences between situations. The method relies on triples of known cases rather than on individual cases for making a prediction on the appropriateness of a potential decision, or for proposing a way of adapting a decision according to situations. The approach may be of interest in a variety of problems ranging from flexible querying systems to cooperative artificial agents.
Richard Billingsley, Henri Prade, Gilles Richard, Mary-Anne Williams
Flexible Query Answering with the powerset-AI Operator and Star-Based Ranking
Abstract
Query generalization is one option to implement flexible query answering. In this paper, we introduce a generalization operator (called powerset-AI) that extends conventional Anti-Instantiation (AI). We analyze structural modifications imposed by the generalization to obtain syntactic similarity measures (based on the star feature) that rank generalized queries with regard to their closeness to the original query.
Lena Wiese

Recommendation and Ranking

Frontmatter
On the Need for Explicit Confidence Assessments of Flexible Query Answers
Abstract
Flexible query answering systems aim to exploit data collections in a richer way than traditional systems can do. In approaches where flexible criteria are used to reflect user preferences, expressing query satisfaction becomes a matter of degree. Nowadays, it becomes more and more common that data originating from different sources and different data providers are involved in the processing of a single query. Also, data sets can be very large such that not all data within a database or data store can be trusted to the same extent and consequently the results in a query answer can neither be trusted to the same extent. For this reason, data quality assessment becomes an important aspect of query processing. In this paper we discuss the need for explicit data quality assessments of query results. Indeed, To correctly inform users, it is in our opinion essential to communicate not only the satisfaction degrees in a query answer, but also the confidence about these satisfaction degrees as can be derived from data quality assessment. As illustration, we propose a hierarchical approach for query processing and data quality assessment, supporting the computation of as well a satisfaction degree, as its associated confidence degree for each element of the query result. Providing confidence information adds an extra dimension to query processing and leads to more soundly query answers.
Guy De Tré, Robin De Mol, Antoon Bronselaer
Meeting and Joining Theme Models in Vector Spaces for Information Retrieval
Abstract
The upper bounds of Information Retrieval (IR) effectiveness could be improved if new frameworks were investigated beyond traditional retrieval models. Vector spaces and their untraditional operators – meet and join – are a step in this direction. On the other hand, users might express complex information needs. Complex information needs may take the form of themes, which cannot be effortlessly expressed using plain natural language queries. Therefore, new theoretical structures and operators should be designed for allowing users to express themes.
This paper illustrates how meet and join of vector spaces can rank documents by a relevance measure. Meet and join act on themes modeled as vector subspaces; for example, meet intersects two planes while join builds a plane from two lines. Since an operator applies to a pair of themes and results in another theme operators and theme models replace the traditional retrieval models. The experimental results show that this approach can compete with – it can retrieve relevant documents missed by – traditional retrieval models.
Emanuele Di Buccio, Massimo Melucci
A Typicality-Based Recommendation Approach Leveraging Demographic Data
Abstract
In this paper, we introduce a new recommendation approach leveraging demographic data. Items are associated with the audience who liked them, and we consider similarity based on audiences. More precisely, recommendations are computed on the basis of the (fuzzy) typical demographic properties (age, sex, occupation, etc.) of the audience associated with every item. Experiments on the MovieLens dataset show that our approach can find predictions that other tested state-of-the-art systems cannot.
Aurélien Moreau, Olivier Pivert, Grégory Smits
MRRA: A New Approach for Movie Rating Recommendation
Abstract
Nowadays, Movie constitutes a predominant form of entertainment in human life. Most video websites such as YouTube and a number of social networks allow users to freely assign a rate to watched or bought videos or movies. In this paper, we introduce a new movie rating recommendation approach, called MRRA, based on the exploitation of the Hidden Markov Model (HMM). Specifically, we extend the HMM to include user’s rating profiles, formally represented as triadic concepts. Triadic concepts are exploited for providing important hidden correlations between rates, movies and users. Carried out experiments using a benchmark movie dataset revealed that the proposed movie rating recommendation approach outperforms conventional techniques.
Chiraz Trabelsi, Gabriella Pasi

Technologies for Flexible Representations and Querying

Frontmatter
New Variants of Hash-Division Algorithm for Tolerant and Stratified Division
Abstract
Works done in the context of the relational division for DBMS led to several approaches. Among which, the Hash-Division algorithm proved its superiority compared to the other approaches in the most of the cases. Nowadays, current trends of division are been oriented towards flexible queries and those involving preferences. However, the emphasis was always on proposing new operators which provide more flexibility and tolerance than the classical division operator. The performance aspect has not been adequately addressed. The proposed approaches in the literature suffer from a lack of performance, especially in a large volume of data. In this paper, we attempt to address this problem. Our idea consists in exploiting the advantages offered by the classical Hash-Division algorithm to propose new variants tailored for the flexible context. We paid a special attention to the improvement of some extended tolerant operators. Furthermore, we introduce a parallel implementation of our proposed techniques. Experimental results show the efficiency of our proposition. We obtained a very satisfactory improvement in processing time (the gain exceeds a ratio of 20 in the majority of cases) in both sequential and parallel implementation.
Noussaiba Benadjmi, Khaled Walid Hidouci
Coverage Degree-Based Fuzzy Topological Relationships for Fuzzy Regions
Abstract
Geographical Information Systems and spatial database systems are well able to handle crisp spatial objects, i.e., objects in space whose location, extent, shape, and boundary are precisely known. However, this does not hold for fuzzy spatial objects characterized by vague boundaries and/or interiors. In the same way as fuzzy spatial objects are vague, the topological relationships (e.g., overlap, inside) between them are vague too. In this conceptual paper, we propose a novel model to formally define fuzzy topological relationships for fuzzy regions. For their definition we consider the numeric measure of coverage degree and map it to linguistic terms that can be embedded into spatial queries.
Anderson Chaves Carniel, Markus Schneider
Plug-and-Play Queries for Temporal Data Sockets
Abstract
Plug-and-play queries are portable, reliable, and easier to code. When a plug-and-play query is plugged into a data socket, the socket transforms the data to the shape needed by the query. If data is annotated with metadata, the semantics of the metadata potentially impacts the transformation. In this paper we describe how to account for the metadata in a transformation. We focus on temporal metadata and show how a transformation can preserve temporal semantics. We also show how the transformation can be driven by the metadata, for instance, the temporal metadata could be used to create data versions.
Curtis E. Dyreson, Sourav S. Bhowmick
Index Structures for Preference Database Queries
Abstract
Preference queries enable satisfying search results by delivering best matches, even when no tuple in a dataset fulfills all preferences perfectly. Several methods were developed for preference query processing, such as window-based, distributed, divide-and-conquer, and index-based algorithms. In particular, all index-based algorithms were designed to evaluate Pareto preferences, where the participating preferences are all equally important. In this paper we present index structures for base preferences. Our comprehensive experiments show how indexing data for preference database queries enable faster access of the data tuples and therefore lead to performance advantages when evaluating preferences.
Markus Endres, Felix Weichmann

Knowledge Discovery and Information/Data Retrieval

Frontmatter
Content-Based Meta-Discovery Service of Remote Sensing Images
Abstract
The paper proposes a novel perspective on Web discovery services of remote sensing images and derived products currently available through the Web portals of providers managing big geo-spatial data repositories. Actual discovery services do not provide facilities for ranking images based on queries specifying spatial-content conditions, i.e. asking for images having desired pixels values in a Region Of Interest (ROI). Our objective is to enable such a facility by designing both a query language with linguistic terms to ask for the desired qualitative characteristics of the image content in a ROI, and a retrieval mechanism to evaluate the degrees of satisfaction of the images with respect to the query spatial-content conditions. The retrieval mechanism is implemented as a meta-discovery service, i.e., as a front-end on the discovery service of the image provider, that does not need to access the images, but just their previews, empowering the retrieval with ranking capabilities. It requires a spatial-content inverted index, previously built off-line by processing all image previews so as to achieve scalability and retrieval efficiency.
Bordogna Gloria, Ceresi Andrea, Sterlacchini Simone
Machine Learning Method for Paraphrase Identification
Abstract
A new effective algorithm and a system for paraphrase identification have been developed using a machine learning approach. The system architecture has the form of a multilayer classifier. According to their strategies, sub-classifiers of the lower level make decisions about the presence of paraphrase in sentences, while a super-classifier of the upper level makes the final decision. Conducted experiments demonstrated that the system has the accuracy of the paraphrase detection comparable with the best known analogous systems while being superior to all of them in implementation.
Oleksandr Marchenko, Anatoly Anisimov, Andrii Nykonenko, Tetiana Rossada, Egor Melnikov
DRIMS: A Software Tool to Incrementally Maintain Previous Discovered Rules
Abstract
A wide spectrum of methods for knowledge extraction have been proposed up to date. These expensive algorithms become inexact when new transactions are made into business data, an usual problem in real-world applications. The incremental maintenance methods arise to avoid reruns of those algorithms from scratch by reusing information that is systematically maintained. This paper introduces a software tool: Data Rules Incremental Maintenance System (DRIMS) which is a free tool written in Java for incrementally maintain three types of rules: association rules, approximate dependencies and fuzzy association rules. Several algorithms have been implemented in this tool for relational databases using their active resources. These algorithms are inspired in efficient computation of changes and do not include any mining technique. We operate on discovered rules in their final form and sustain measures of rules up-to-date, ready for real-time decision support. Algorithms are applied over a generic form of measures allowing the maintenance of a wide rules’ metrics in an efficient way. DRIMS software tool do not discover new knowledge, it has been designed to efficiently maintain interesting information previously extracted.
Alain Pérez-Alonso, Ignacio J. Blanco, Jose M. Serrano, Luisa M. González-González
Querying Streams of Alerts for Knowledge-Based Detection of Long-Lived Network Intrusions
Abstract
Intrusion detection relies on the analysis of flows of network and system events that are checked against signatures or models of normality to raise alerts. However, these alerts are often the result of having detected a single step in the unfolding sequence of activities of an attacker, and techniques relying on simple alerting fall short in recognizing or preventing subsequent actions. Here we present the design and prototype implementation of a novel intrusion detection approach based on agents that are triggered in reaction to alerts that use attack patterns as working hypotheses. Those agents query the real-time stream of alerts, matching them with a particular attack pattern and a graph model of the network being monitored. The architecture for that system scales using the distributed streaming framework of Apache Kafka and a lightweight agent container, allowing for long-lived monitoring of attack hypotheses, each of them embodied in a single agent. The approach is tested against synthetic flows of data representing single-node and multi-step “island hopping” scenarios.
Miguel-Angel Sicilia, Javier Bermejo-Higuera, Elena García-Barriocanal, Salvador Sánchez-Alonso, Daniel Domínguez-Álvarez, Miguel Monzón-Fernández

Intuitionistic Sets

Frontmatter
Multiplicative Type of Operations over Intuitionistic Fuzzy Pairs
Abstract
Intuitionistic Fuzzy Sets (IFSs) are an extension of fuzzy sets. Each element x of the IFS A has degrees of a membership (\(\mu _A(x)\)) and of a non-membership (\(\nu _A(x)\)) so that \(0 \le \mu _A(x) + \nu _A(x) \le 1\). The pair \(\langle \mu _A(x), \nu _A(x) \rangle \) is called an Intuitionistic Fuzzy Pair (IFP). A lot of operations, relations and operators are defined over IFPs. In the paper, novel operations over IFPs are introduced and some of their basic properties are studied. Geometrical interpretations of these operations are given. Open problems are formulated.
Krassimir Atanassov, Eulalia Szmidt, Janusz Kacprzyk
New Modified Level Operator N γ Over Intuitionistic Fuzzy Sets
Abstract
The present paper takes the idea of the level operator N α,β and proposes a modification called N γ . The aim of the original level operator is to generate a subset of an intuitionistic fuzzy set A, called (α, β)-set, whose degrees of membership are above a given level (threshold) α and degrees of non-membership are below a given level β, where both α, β are fixed numbers in the [0, 1] interval and α + β ≤ 1. In the modification proposed here, we introduce the operator N γ that also generates a subset of an intuitionistic fuzzy set A, where the elements of the subset are those elements of A, for which the ratio of their degrees of membership to their degrees of non-membership, respectively, is greater or equal to a given number γ > 0.
Vassia Atanassova
Application of Topological Operators over Data from InterCriteria Analysis
Abstract
In this paper, two topological operators T and U over intuitionistic fuzzy sets are considered and applied. As a case study a parameter identification problem of E. coli fed-batch cultivation process model using genetic algorithms is investigated. A new result regarding T and U is established. The results obtained by the application of the topological operators over data processed by InterCriteria Analysis are discussed.
Olympia Roeva, Peter Vassilev, Panagiotis Chountas
Application of the InterCriteria Analysis Over Air Quality Data
Abstract
In the paper application of the InterCriteria analysis approach to real dataset with instances of hourly averaged responses from an array of 5 metal oxide chemical sensors embedded in an air quality chemical multisensor device [29, 30] is represented. The InterCriteria analysis is a new method that can be used for multicriteria decision making. The aim is to analyze the correlations between 12 indicators representing the recordings of on field deployed air quality chemical sensor devices responses.
Evdokia Sotirova, Veselina Bureva, Irena Markovska, Sotir Sotirov, Desislava Vankova

Generalized Net Model

Frontmatter
Generalized Net of Cluster Analysis Process Using STING: A Statistical Information Grid Approach to Spatial Data Mining
Abstract
Cluster analysis is one of the main topics in data mining. It helps to group elements with similar behavior in one group. Therefore, a good clustering method will produce high quality clusters containing objects similar to one another within the same group and dissimilar to the objects in other clusters. In the current research work one of the basic grid-based methods for clustering is modelled using Generalized nets.
Veselina Bureva, Evdokia Sotirova, Stanislav Popov, Deyan Mavrov, Velichka Traneva
A Generalized Net Model of the Neocognitron Neural Network
Abstract
In this paper a generalized net model of the Neocognitron neural network is presented. A Network Neocognitron is a self-organizing network with the ability to recognize patterns based on the difference of their form. A neocognitron is able to correctly identify an image, even if there is a violation or movement into position. Self-organization in the neocognitron is also realized uncontrollably - training for self-organizing neocognitron takes only a collection of recurring patterns in the recognizable image and does not need the information for categories that include templates. The output producing process is presented by a Generalized net model.
Todor Petkov, Plamena Jovcheva, Zhivko Tomov, Stanislav Simeonov, Sotir Sotirov
Comparison of Conceptual Models of Overall Telecommunication Systems with QoS Guarantees
Abstract
Different approaches to conceptual modeling of overall telecommunication systems with QoS guarantees are discussed. Generalized Nets (GNs) are used as an alternative to the already existing conceptual models based on the Service Networks Theory. Two GN representations of a part of the Switching stage are proposed and their advantages and disadvantages are discussed.
Stoyan Poryazov, Velin Andonov, Emiliya Saranova
Generalized Net Model of Muscle Pain Diagnosing
Abstract
Pain is the most common symptom of the many musculoskeletal pathologies. Musculoskeletal pain affects the muscles, ligaments, tendons, nerves and bones and might be caused by diverse factors. Musculoskeletal pain ranges from mild to severe. It can be local or diffuse, and acute or chronic. Due to the wide range of conditions that may cause such a symptom, diagnosing process is challenging and a systematic approach is necessary. In this investigation we present a successful example of generalized nets application in medical diagnosing and propose a novel approach leading to the appropriate diagnostic considerations. The method proposed in this investigation accurately identifies the various steps during the muscle pain diagnosing process and significantly improves the health care level. Obtained so far results could be used to assist in the decision making in the diagnostic processes.
Simeon Ribagin, Panagiotis Chountas, Tania Pencheva
Generalized Nets as a Tool for Modelling of the Urban Bus Transport
Abstract
It is shown that generalized nets can be used as a tool for modelling of the urban bus transport. An example of a generalized net of a part of the urban bus transport in town Burgas (Bulgaria), is given.
Ivan Valkov, Krassimir Atanassov, Lyubka Doukovska
Backmatter
Metadata
Title
Flexible Query Answering Systems
Editors
Henning Christiansen
Hélène Jaudoin
Panagiotis Chountas
Troels Andreasen
Henrik Legind Larsen
Copyright Year
2017
Electronic ISBN
978-3-319-59692-1
Print ISBN
978-3-319-59691-4
DOI
https://doi.org/10.1007/978-3-319-59692-1

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