Catálogo de publicaciones - libros

HealthAgents proposes an agent-based distributed decision support system for brain tumour diagnosis and prognosis which employs Magnetic Resonance Imaging and Magnetic Resonance Spectroscopy techniques and genomic profiles. From a knowledge representation view point the distributed nature and the heterogeneity of the data to be integrated pose a number of challenging problems. This paper shows how Conceptual Graphs can be employed to describe the data sources in the HealthAgents system. Such knowledge representation based description of data allows for reasoning power when querying and for data modularisation capabilities.

This paper presents a combinatorial, structure based approach to the problem of finding a (di)similarity measure between two Conceptual Graphs. With a growing number of ontologies and an increasing need for quick, on the fly knowledge integration and querying, ontology similarity measures are essential for building the foundations of the Semantic Web. Conceptual Graphs benefit from a graph based representation that can be exploited in versatile optimisation techniques. We propose a disimilarity measure based on the content and the structure of two graphs. This disimilarity measure is based on the clique number of the matching graph, a combinatorial structure which encodes the two graphs projection information.

Knowledge representation (KR) is used to store and retrieve meaningful data. This data is saved using dynamic data structures that are suitable for the style of KR being implemented. The KR allows the system to manipulate the knowledge in the data by using reasoning operations. The data structure, together with the contents of the transformed knowledge, is known as the knowledge base (KB). An algorithm and the associated data structures make up the reasoning operation, and the performance of this operation is dependent on the KB.

In this paper, the basic reasoning operation for a query-answer system, projection, is explored using different theoretical algorithms. Within this discussion, the associated algorithms will be using different KBs for their Conceptual Graph (CG) knowledge representation. The basic projection algorithm defined using the CG representation is looking for a graph morphism of a query graph onto a graph from the KB.

The overall running time for the projection operation is known to be a NP class problem; however, by modifying the algorithm, taking into account the associated KB, the actual time needed for discovering and creating the projection/s can be improved. In fact, a new projection algorithm will be defined that, given a typical query onto a carefully defined KB, presents a running time for the actual projection that only grows with the number of projections present.

A software product-line is a set of products built from a core set of software components. Although software engineers develop software product-lines for various application types, they are most commonly used for embedded systems development, where the variability of hardware features requires variability in the supporting firmware. Feature models are used to represent the variability in these software product-lines. Various feature modeling approaches have been proposed, including feature diagrams, domain specific languages, constraint languages, and the semantic web language OWL. This paper explores a conceptual graph approach to feature modeling in an effort to produce feature models that have a more natural, and more easily expressed mapping to the problem domain. It demonstrates the approach using a standard Graph Product-line problem that has been discussed in various software product-line papers. A conceptual graph feature model is developed for the graph product-line and it is compared to other feature models for this product-line.

Smart applications behave intelligently because they understand at least partially the context where they operate. To do this, they need not only a formal domain model but also formal descriptions of the data they process and their own operational behaviour. Interoperability of smart applications is based on formalised definitions of all their data and processes. This paper studies the semantic interoperability of data in the case of eLearning and describes an experiment and its assessment. New content is imported into a knowledge-based learning environment without real updates of the original domain model, which is encoded as a knowledge base of conceptual graphs. A component called mediator enables the import by assigning dummy metadata annotations for the imported items. However, some functionality of the original system is lost, when processing the imported content, due to the lack of proper metadata annotation which cannot be associated fully automatically. So the paper presents an interoperability scenario when appropriate content items are viewed from the perspective of the original world and can be (partially) reused there.

We introduce a simple but efficient, multistage algorithm for constructing concept lattices (MCA). A concept lattice can be obtained as the closure system generated from attribute concepts (dually, object concepts). There are two strategies to use this as the basis of an algorithm: (a) forming intersections by joining one attribute concept at a time, and (b) repeatedly forming pairwise intersections starting from the attribute concepts. A straightforward translation of (b) to an algorithm suggests that pairwise intersection be performed among all cumulated concepts. MCA is parsimonious in forming the pairwise intersections: it only performs such operations among the newly formed concepts from the previous stage, instead of cumulatively. We show that this parsimonious multistage strategy is complete: it generates all concepts. To make this strategy really work, one must overcome the need to eliminate duplicates (and potentially save time even further), since concepts generated at a later stage may have already appeared in one of the earlier stages. As considered in several other algorithms in the literature [5], we achieve this by an auxiliary search tree which keeps all existing concepts as paths from the root to a flagged node or a leaf. The depth of the search tree is bounded by the total number of attributes, and hence the time complexity for concept lookup is bounded by the logarithm of the total number of concepts. For constructing lattice diagrams, we adapt a sub-quadratic algorithm of Pritchard [9] for computing subset partial orders to constructing the Hasse diagrams. Instead of the standard expected quadratic complexity, the Pritchard approach achieves a worst-case time ( / ). Our experimental results showed significant improvements in speed for a variety of input profiles against three leading algorithms considered in the comprehensive comparative study [5]: Bordat, Chein, and Norris.

A semiotic approach to the design space of information presentation is presented in which Formal Concept Analysis (FCA) is used to represent and explore attributes of abstract sign types and the media (graphical, haptic, acoustic, gestic) through which they are presented as specific representational forms. Early taxonomies in design have typically been incomplete (in only considering graphics) and inconsistent (in the absence of separation between media and sign types). With digital multimedia and the future “semantic web”, we need a consistent taxonomy to support component-based flexible (adaptive, tailorable) presentations with a clear separation between (a) the content forms of data, (b) the representational forms through which data is expressed, (c) the combination of media of presentation, and (d) the specific layout within the constraints of the presentation devices and the ergonomic and aesthetic choices of designers and users.

Representing concept lattices constructed from large contexts often results in heavy, complex diagrams that can be impractical to handle and, eventually, to make sense of. In this respect, many concepts could allegedly be dropped from the lattice without impairing its relevance towards a taxonomy description task at a certain level of detail. We propose a method where the notion of stability is introduced to select potentially more pertinent concepts. We present some theoretical properties of stability and discuss several use cases where taxonomy building is an issue.

Distributive lattices belong to the best studied ordered structures. A. Huhn introduced a generalisation of this lattice property, called . We present two new methods to recognise the parameter of this property for a given structure. For this purpose we use the in a formal context and . Additionally, we consider subsets of an order relation ≤ on a finite set with an additional property. These subsets will be called of ≤. We show that the family of left clearings forms a complete dually -distributive lattice, where denotes the length of (, ≤ ). Using these results, we determine that parameter for lattices for the -distributivity and dually -distributivity.

The aim of this paper is to approach a which is understood as the theory and methodology of musical semantic structures. The analysis of music structures is based on a threefold semantics which is performed on the musical level, the abstract philosophic-logical level, and the hypothetical mathematical level. Basic music structures are discussed by examples, in particular: tone systems, chords, harmonies, scales, modulations, musical time flow, and music forms. A specific concern of this paper is to clarify how a Semantology of Music may support the .