Catálogo de publicaciones - libros

BibSonomy is a web-based social resource sharing system which allows users to organise and share bookmarks and publications in a collaborative manner. In this paper we present the system, followed by a description of the insights in the structure of its bibliographic data that we gained by applying techniques we developed in the area of Formal Concept Analysis.

The paper presents the MILL – a system which supports planning of training measures. Its background technique, task-competency modeling, is based on formal concept analysis as an indirect and qualitative way of determining the abilities of learners. In that context, the fulfillment or failure of a work task is the indicator of a set of necessary competencies. The core of the presented approach is a matrix structure – a formal context – which has tasks as labels for its rows and competencies labeling its columns. This matrix is the basis for defining formal concepts which can be ordered in a lattice for navigation and systematic decision support on training measures in an organisation.

Bilingual word association networks can be beneficial as a tool in foreign language education because they show relationships among cognate words of different languages and correspond to structures in the mental lexicon. This paper discusses possible technologies that can be used to generate and represent word association networks.

After decades of concurrent development of symbolic and connectionist methods, recent years have shown intensifying efforts of integrating those two paradigms. This paper contributes to the development of methods for transferring present symbolic knowledge into connectionist representations. Motivated by basic ideas from formal concept analysis, we propose two ways of directly encoding closure operators on finite sets in a 3-layered feed forward neural network.

The projection problem (conceptual graph projection, homomorphism, injective morphism, -subsumption, OI-subsumption) is crucial to the efficiency of relational learning systems. How to manage this complexity has motivated numerous studies on learning biases, restricting the size and/or the number of hypotheses explored. The approach suggested in this paper advocates a projection operator based on the classical arc consistency algorithm used in constraint satisfaction problems. This projection method has the required properties : polynomiality, local validation, parallelization, structural interpretation. Using the arc consistency projection, we found a generalization operator between labeled graphs. Such an operator gives the structure of the classification space which is a concept lattice.

Many knowledge representation mechanisms consist of link-based structures; they may be studied formally by means of unordered trees. Here we consider the case where labels on the nodes are nonexistent or unreliable, and propose data mining processes focusing on just the link structure. We propose a representation of ordered trees, describe a combinatorial characterization and some properties, and use them to propose an efficient algorithm for mining frequent closed subtrees from a set of input trees. Then we focus on unordered trees, and show that intrinsic characterizations of our representation provide for a way of avoiding the repeated exploration of unordered trees, and then we give an efficient algorithm for mining frequent closed unordered trees.

Many smart applications allow enterprises to communicate effectively with and through interconnected computing resources, however, successful communication presupposes a shared understanding; a so-called Devolved ontology was developed to promote semantic alignment in agile partnerships. We consider the approach to be promising for environment where multiple contexts interface and co-locate, including, for example, the Pragmatic Web, virtual organisations and indeed smart applications. We motivate and introduce devolved ontology and show how to use this to foster semantic alignment.

During the Renaissance there was a growing interest for the use of diagrams within conceptual studies. This paper investigates the historical and philosophical foundation of this renewed use of diagrams in ontology as well as the modern relevance of this foundation. We discuss the historical and philosophical background for Jacob Lorhard’s invention of the word ‘ontology’ as well as the scientific status of ontology in the 16 and 17 century. We also consider the use of Ramean style diagrams and diagrammatic ontology in general. A modern implementation of Lorhard’s ontology is discussed and this classical ontology is compared to some modern ontologies.

We build a generic methodology based on learning and reasoning to detect specific attitudes of human agents and patterns of their interactions. Human attitudes are determined in terms of communicative actions of agents; models of machine learning are used when it is rather hard to identify attitudes in a rule-based form directly. We employ scenario knowledge representation and learning techniques in such problems as predicting an outcome of international conflicts, assessment of an attitude of a security clearance candidate, mining emails for suspicious emotional profiles, mining wireless location data for suspicious behavior, and classification of textual customer complaints. A preliminary performance estimate evaluation is conducted in the above domains. Successful use of the proposed methodology in rather distinct domains shows its adequacy for mining human attitude-related data in a wide range of applications.

We define an abstract model of belief propagation on a graph based on the methodology of the revision theory of truth together with the , a graphical interface designed to test our semantics.