There are two objectives of this chapter. One objective is to examine the basic principles and issues of granular computing. We focus on the tasks of granulation and computing with granules. From semantic and algorithmic perspectives, we study the construction, interpretation, and representation of granules, as well as principles and operations of computing and reasoning with granules. The other objective is to study a partition model of granular computing in a set-theoretic setting. The model is based on the assumption that a finite set of universe is granulated through a family of pairwise disjoint subsets. A hierarchy of granulations is modeled by the notion of the partition lattice.

Summary. In the last years, the spread of computers and the Internet caused a significant amount of documents to be available in digital format. Collecting them in digital repositories raised problems that go beyond simple acquisition issues, and cause the need to organize and classify them in order to improve the effectiveness and efficiency of the retrieval procedure. The success of such a process is tightly related to the ability of understanding the semantics of the document components and content. Since the obvious solution of manually creating and maintaining an updated index is clearly infeasible, due to the huge amount of data under consideration, there is a strong interest in methods that can provide solutions for automatically acquiring such a knowledge. This work presents a framework that intensively exploits intelligent techniques to support different tasks of automatic document processing from acquisition to indexing, from categorization to storing and retrieval. The prototypical version of the system DOMINUS is presented, whose main characteristic is the use of a Machine Learning Server, a suite of different inductive learning methods and systems, among which the more suitable for each specific document processing phase is chosen and applied. The core system is the incremental first-order logic learner INTHELEX. Thanks to incrementality, it can continuously update and refine the learned theories, dynamically extending its knowledge to handle even completely new classes of documents. Since DOMINUS is general and flexible, it can be embedded as a document management engine into many different Digital Library systems. Experiments in a real-world domain scenario, scientific conference management, confirmed the good performance of the proposed prototype. 1

The availability of large, heterogeneous repositories of electronic documents is increasing rapidly, and the need for flexible, sophisticated document manipulation tools is growing correspondingly. This work presents DOMINUS a system for automated electronic documents processing characterized by the intensive exploitation of intelligent techniques in each step of the process from document acquisition in electronic format to document indexing for categorization and information retrieval purposes. It embeds incremental learning techniques useful in the context of web libraries where documents are acquired in an incremental fashion. Since the system is general and flexible, it can be embedded as a document management engine into many different domain-specific applications. Here, we present the exploitation of the system on the Conference Management domain, and show how DOMINUS can usefully support some of the more critical and knowledge-intensive tasks involved by the organization of a scientific conference. 1.

This paper aims at presenting the application of first-order logic machine learning techniques to two document domains in order to earn rules fo reco) izing the semanticro eo f theirol:P co: o ents. Specifica y, the mu tistrategy incrementa earning system INTHELEX has been app ied to mu ti-fo rmat scientific papers and do cuments co ncerning Eur o ean fi msfro the 20's and 30's. The cha enge co es fro the di#erent eve so f fo rmatting standards in thesedo mains: fro m(mo re o r ess) standardized ayo uts, in scientific papers, to do cuments with a - mo) no standard, in histoR3l cu tura heritage material. Experimenta resu ts in bo th do mains and a c o pariso with the Progol system assess the advantages that the expoitation of INTHELEX can yield.

Traditional Machine Learning approaches are based on single inference mechanisms. A step forward concerned the integration of multiple inference strategies within a first-order logic learning framework, taking advantage of the benefits that each approach can bring.

Traditional Machine Learning approaches based on single inference mechanisms have reached their limits. This causes the need for a framework that integrates approaches based on abduction and abstraction capabilities in the inductive learning paradigm, in the light of Michalski's Inferential Theory of Learning (ITL). This work is intended as a survey of the most significant contributions that are present in the literature, concerning single reasoning strategies and practical ways for bringing them together and making them cooperate in order to improve the effectiveness and efficiency of the learning process. The elicited role of an abductive proof procedure is tackling the problem of incomplete relevance in the incoming examples. Moreover, the employment of abstraction operators based on (direct and inverse) resolution to reduce the complexity of the learning problem is discussed. Lastly, a case study that implements the combined framework into a real multistrategy learning system is briefly presented.

This work presents the application of a new, enhanced version of theincremfi tal learningsystem INTHELEX (INcrem5 tal THEory Learnerfrom EXamr5xC: the learningcom onent in the architecture of the EU project COLLATE, dealing with the annotation of cultural heritage documC ts. Due to thecomx:C shape of the handledmndled5# the addition ofm ultistrategy capabilities was needed toim#: ve the e#ectiveness and e#ciency of the learning process.Som resultsdemlt strating the benefits that the addition of each strategy can bring are also reported.

Abstract. The most common methodology in symbolic learning consists in inducing, given a set of observations, a general concept definition. It is widely known that the choice of the right description language for a learning problem can affect the efficacy and effectiveness of the learning task. Furthermore, most of the real-world domain are contaminated by various kinds of imperfections in data such as inappropriateness of the description language which does not contain/facilitate an exact representation of the target concept. To deal with such kind of situations, Machine Learning approaches have moving from a framework exploiting a single inference mechanism, such as induction, towards one integrating multiple inference strategies such as abstraction. The literature so far assumed that the information needed to the learning systems to apply additional inference strategies is provided by the an expert domain. The objective in this work is the automatic inference of such information. The efficacy of the proposed method in generating effective theories to perform abstraction was tested by providing the generated abstraction theories to the learning system INTHELEX allowing it to exploit its multistrategy capabilities, in particular the abstraction one. Various experiments were carried out on a real-world application domain of scientific paper documents showing the validity of the proposed method. 1

This article shows that cartographic generalization is best viewed as representing (formulating, renaming knowledge) and abstracting (simplifying a given representation). The general process of creating map is described so as to show how it fits into an abstraction framework developed in artificial intelligence to emphasize the difference between abstraction and representation. The utility of the framework lies in its efficiency to automate knowledge acquisition for the cartographic generalization as a combined acquisition of knowledge for abstraction and knowledge for changing a representation. Keywords Cartographic Generalization, Abstraction, Representation. 1. INTRODUCTION In this paper we address the problem of automating cartographic generalization. This automation is needed for several reasons: first to decrease cost and time necessary to produce maps, then to allow geography experts who are not necessary cartography specialists to create their own maps with a good quality, ...

This article proposes a machine learning approach to overcome the knowledge acquisition bottleneck that limits the automation of cartographic generalisation. It first explains why this automation must be guided by a differentiation of two main types of knowledge involved in this process. More precisely, it shows that cartographic generalisation can be accomplished by a combination of two processes: representing (formulating, renaming knowledge) and abstracting (simplifying a given representation). The whole process of creating maps fits into an abstraction framework we developed to account for the difference between knowledge abstraction and knowledge representation. The utility of this framework lies in its efficiency to support the automation of knowledge acquisition for cartographic generalisation as a combined learning of both abstraction and representation knowledge. The results experiments show the interest of this approach.