Catálogo de publicaciones - libros

Hierarchical classifications, thesauri, and informal taxonomies are likely the most valuable input for creating, at reasonable cost, non-toy ontologies in many domains. They contain, readily available, a wealth of category definitions plus a hierarchy, and they reflect some degree of community consensus. However, their transformation into useful ontologies is not as straightforward as it appears. In this paper, we show that (1) it often depends on the context of usage whether an informal hierarchical categorization schema is a classification, a thesaurus, or a taxonomy, and (2) present a novel methodology for automatically deriving consistent RDF-S and OWL ontologies from such schemas. Finally, we (3) demonstrate the usefulness of this approach by transforming the two e-business categorization standards eCl@ss and UNSPSC into ontologies that overcome the limitations of earlier prototypes. Our approach allows for the script-based creation of meaningful ontology classes for a particular context while preserving the original hierarchy, even if the latter is not a real subsumption hierarchy in this particular context. Human intervention in the transformation is limited to checking some conceptual properties and identifying frequent anomalies, and the only input required is an informal categorization plus a notion of the target context. In particular, the approach does not require instance data, as ontology learning approaches would usually do.

Complex relationships, frequently referred to as semantic associa-tions, are the essence of the Semantic Web. Query and retrieval of semantic associations has been an important task in many analytical and scientific activities, such as detecting money laundering and querying for metabolic pathways in biochemistry. We believe that support for semantic path queries should be an integral component of RDF query languages. In this paper, we present SPARQLeR, a novel extension of the SPARQL query language which adds the support for semantic path queries. The proposed extension fits seamlessly within the overall syntax and semantics of SPARQL and allows easy and natural formulation of queries involving a wide variety of regular path patterns in RDF graphs. SPARQLeR’s path patterns can capture many low-level details of the queried associations. We also present an implementation of SPARQLeR and its initial performance results. Our implementation is built over BRAHMS, our own RDF storage system.

When creating Semantic Web data, users have to make a critical choice for a vocabulary: only through shared vocabularies can meaning be established. A centralised policy prevents terminology divergence but would restrict users needlessly. As seen in collaborative tagging environments, suggestion mechanisms help terminology convergence without forcing users. We introduce two domain-independent algorithms for recommending predicates (RDF statements) about resources, based on statistical dataset analysis. The first algorithm is based on similarity between resources, the second one is based on co-occurrence of predicates. Experimental evaluation shows very promising results: a high precision with relatively high recall in linear runtime performance.

An increasing number of applications benefits from light-weight ontologies, or to put it differently . However, our experience indicates that more expressiveness can offer significant advantages. Introducing disjointness axioms, for instance, greatly facilitates consistency checking and the automatic evaluation of ontologies. In an extensive user study we discovered that proper modeling of disjointness is a difficult and very time-consuming task. We therefore developed an approach to automatically enrich learned or manually engineered ontologies with disjointness axioms. This approach relies on several methods for obtaining syntactic and semantic evidence from different sources which we believe to provide a solid base for learning disjointness. After thoroughly evaluating the implementation of our approach we think that in future ontology engineering environments the automatic discovery of disjointness axioms may help to increase the richness, quality and usefulness of any given ontology.

Creating a coherent set of ontologies to support a collaborative design process amongst different firms which develop mechatronic products is a challenge due to the semantic heterogeneity of the underlying domain models and the amount of domain knowledge that needs to be covered. We tackle the problem of semantic heterogeneity by employing the DOLCE foundational ontology and by aligning our models to it. We approach the problem of scale, i.e. the amount of knowledge modeled by keeping the models at a descriptive level which is still granular enough to connect them with domain and task specific engineering tools. In order to manage the complexity of the modeling task we separate the models into the foundational layer, the mechatronic layer consisting of three domain ontologies, one process model and one cross-domain model, and the collaborative application layer. For the development process, we employ a methodology for dynamic ontology creation, which moves from taxonomical structures to formal models.

The media play an important role in the functioning of our society. This role is extensively studied by Communication Scientists, requiring a systematic analysis of media content. The methods developed in this field utilize complex data models and background knowledge. This data is generally represented ad hoc, making it difficult to analyze, combine and share data sets.

In this paper we present our work on formalizing this representation using RDF(S). We discuss the requirements for a good representation, highlighting a number of non-trivial modeling decisions. We conclude with a description of the resulting system and the benefits for a recent investigation of the 2006 Dutch parliamentary campaign. This case study shows concrete improvements for annotating, querying, and analyzing data, but also indicates a number of aspects that were more difficult to model in RDF(S), contributing to the discussion on modeling with and improving RDF(S) and associated tools.

This paper presents SEEMP, a marketplace to coordinate and integrate public and private employment services (ESs) around the EU Member States. The need for flexible collaboration in the marketplace gives rise to the issue of interoperability in both data exchange and share of services. SEEMP proposes a mixed approach that relies on the concepts of services and semantics. SEEMP approach combines Software Engineering and Semantic Web methodologies/tools in an infrastructure that allows for a .

This paper presents a framework for the reuse and extension of existing, established vocabularies in the Semantic Web. Driven by the primary application of expert finding, we will explore the reuse of vocabularies that have attracted a considerable user community already (FOAF, SIOC, etc.) or are derived from de facto standards used in tools or industrial practice (such as vCard, iCal and Dublin Core). This focus guarantees direct applicability and low entry barriers, unlike when devising a new ontology from scratch. The Web is already populated with several vocabularies which complement each other (but also have considerable overlap) in that they cover a wide range of necessary features to adequately describe the expert finding domain. Little effort has been made so far to identify and compare existing approaches, and to devise best practices on how to use and extend various vocabularies conjointly. It is the goal of the recently started ExpertFinder initiative to fill this gap. In this paper we present the ExpertFinder framework for reuse and extension of existing vocabularies in the Semantic Web. We provide a practical analysis of overlaps and options for combined use and extensions of several existing vocabularies, as well as a proposal for applying rules and other enabling technologies to the expert finding task.

Social networks have recently attracted much attention for their importance to the Semantic Web. Several methods exist to extract social networks for people (particularly researchers) from the web using a search engine. Our goal is to expand existing techniques to obtain social networks among various entities. This paper proposes two improvements, i.e. and , which enable us to deal with complex and inhomogeneous communities. Social networks among firms and artists (of contemporary) are extracted as examples: Several evaluations emphasize the effectiveness of these methods. Our system was used at the International Triennale of Contemporary Art (Yokohama Triennale 2005) to facilitate navigation of artists’ information. This study contributes to the Semantic Web in that we increase the applicability of social network extraction for several studies.

Computer manipulated social networks are usually built from the explicit assertion by users that they have some relation with other users or by the implicit evidence of such relations (e.g., co-authoring). However, since the goal of social network analysis is to help users to take advantage of these networks, it would be convenient to take more information into account. We introduce a three-layered model which involves the network between people (social network), the network between the ontologies they use (ontology network) and a network between concepts occurring in these ontologies. We explain how relationships in one network can be extracted from relationships in another one based on analysis techniques relying on this network specificity. For instance, similarity in the ontology network can be extracted from a similarity measure on the concept network. We illustrate the use of these tools for the emergence of consensus ontologies in the context of semantic peer-to-peer systems.