Catálogo de publicaciones - libros

This paper details the design, implementation and evaluation of an ontology-based knowledge sharing tool. The system, “Squidz”, automatically classifies browsed web pages against an ontology, and allows users to share comments made about those pages to members of a community. As the user browses web pages, recommendations of relevant documents which have already been shared are produced, based upon both the user’s social network as well as the semantic content of the page currently in view. Key to the design of the system has been the requirement, evidenced by earlier studies, that sharing should be easily effected as a side-effect of browsing rather than comprising a separate and distracting task. Another feature of the system is the linkage of a formal ontology with user-provided tags of shared information, thus combining the proven popularity of folksonomy-based systems with the shared and formal domain model provided by an ontology.

This work is motivated by experiences in the course of developing an ontology-based application within a real-world setting. We found out that current benchmarks are not well suited to provide helpful hints for users who seek for an appropriate reasoning system able to deal with expressive terminological descriptions, large volumes of assertional data, and frequent updates in a sound and complete way. This paper tries to provide some insights into currently available reasoning approaches and aims at identifying requirements to make future benchmarks more useful for application developers.

You can only control what you can measure. Measuring ontologies is necessary to evaluate ontologies both during engineering and application. Metrics allow the fast and simple assessment of an ontology and also to track their subsequent evolution. In the last few years, a growing number of ontology metrics and measures have been suggested and defined. But many of them suffer from a recurring set of problems, most importantly they do not take the semantics of the ontology language properly into account. The work presented here is a principal approach to facilitate the creation of ontology metrics with the clear goal to go beyond structural metrics to proper semantic-aware ontology metrics. We have developed guidelines and a set of methodological tools based on the notions of “normalization” and “stable metrics” for creating ontology metrics. These guidelines allow the metric author to decide which properties metrics need to fulfil and to appropriately design the desired metric. A discussion of an exemplary metric (taken from literature) illustrates and motivates the issues and suggested solutions.

In this paper, we propose a novel approach to measure inconsistencies in ontologies based on Shapley values, which are originally proposed for game theory. This measure can be used to identify which axioms in an input ontology or which parts of these axioms need to be removed or modified in order to make the input consistent. We also propose optimization techniques to improve the efficiency of computing Shapley values. The proposed approach is independent of a particular ontology language or a particular reasoning system used. Application of this approach can improve the quality of ontology diagnosis and repair in general.

Search is seen as a key application that can benefit from semantic technology with improvements to recall and precision over conventional Information Retrieval techniques. This paper describes Squirrel, a search and browse tool that provides access to semantically annotated data. Squirrel provides combined keyword based and semantic searching. The intention is to provide a balance between the speed and ease of use of simple free text search and the power of semantic search. In addition, the ontological approach provides the user with a much richer browsing experience. Squirrel builds on and integrates a number of semantic technology components. These include machine learning and information extraction components which generate, extract and manage semantic metadata contained within and about textual documents at index time. A number of run-time components have also been integrated to deliver an enhanced user experience which goes beyond merely presenting a list of documents as a query response. The tool has been trialled and evaluated in two case studies and we report early results from this exercise, revealing promising results.

Many semantic portals use faceted browsing, where the facets are based on the underlying indexing ontologies of the content. However, in many cases, like in medical applications, the ontologies may be very large and complex, and do not provide the end-user with intuitive facet hierarchies for conceptualizing the content, for formulating queries, and for classifying the search results. We argue that in such cases end-user facets should be separated from the annotation ontologies, and show how to generalize the semantic view-based search paradigm to take into account this fact. A user-centric card sorting method is proposed for designing intuitive views for the end-users and a method for mapping its facets onto the indexing ontologies and search items is presented. The system has been implemented in a prototype of the semantic portal TerveSuomi.fi, a national health promotion portal in Finland.

The growing need for content customization in websites has fostered the development of systems which try to identify the user’s navigation patterns. These may be, normally, identified by means of log file analysis. However, this solution does not identify the semantic intention behind user’s navigation. This paper provides an approach to incorporating semantic knowledge to the process of identifying the user’s intentions in the navigation of a website with semantic support. The capture of the user’s intentions is achieved by the semantic enrichment of the log files and the use of and approach that takes into account the linguistic and cognitive aspects in the development of the user model.

We present a novel combination of disjunctive logic programs under the answer set semantics with description logics for the Semantic Web. The combination is based on a well-balanced interface between disjunctive logic programs and description logics, which guarantees the decidability of the resulting formalism without assuming syntactic restrictions. We show that the new formalism has very nice semantic properties. In particular, it faithfully extends both disjunctive programs and description logics. Furthermore, we describe algorithms for reasoning in the new formalism, and we give a precise picture of its computational complexity. We also provide a special case with polynomial data complexity.

In an open, constantly changing and collaborative environment like the forthcoming Semantic Web, it is reasonable to expect that knowledge sources will contain noise and inaccuracies. Practical reasoning techniques for ontologies therefore will have to be tolerant to this kind of data, including the ability to handle inconsistencies in a meaningful way. For this purpose, we employ paraconsistent reasoning based on four-valued logic, which is a classical method for dealing with inconsistencies in knowledge bases. Its transfer to OWL DL, however, necessitates the making of fundamental design choices in dealing with class inclusion, which has resulted in differing proposals for paraconsistent description logics in the literature. In this paper, we build on one of the more general approaches which due to its flexibility appears to be most promising for further investigations. We present two algorithms suitable for implementation, one based on a preprocessing before invoking a classical OWL reasoner, the other based on a modification of the KAON2 transformation algorithms. We also report on our implementation, called ParOWL.

In this paper we propose an approach to semantic matchmaking that exploits various knowledge representation technologies to find most promising partners in peer-to-peer e-marketplaces. In particular we mix in a formal and principled way the semantic expressiveness of DLR-lite Logic Programs, fuzzy logic and utility theory. We adopt DLR-Lite Logic Programs to obtain a reasonable compromise between expressiveness and complexity to ensure the scalability of our approach to large e-marketplaces, and Fuzzy Logic to model logical specifications as soft constraints. Furthermore, fully exploiting the peer-to-peer paradigm, we consider in the matchmaking process preferences and corresponding utilities of both parties.