6604472
A Fine-Grained Approach to Resolving Unsatisfiable Ontologies
In the semantic Web, inconsistencies in OWL ontologies may easily occur. Existing approaches either identify the minimally unsatisfiable sub-ontologies or calculate the maximally satisfiable sub-ontologies. However practical problems remain; it is not clear which axioms or which parts of axioms should be selected for repair, and how to repair those axioms. In this paper, we address this limitation by proposing a fine-grained approach to resolving unsatisfiable ontologies. We revise the axiom tracing technique first proposed by Baader and Hollunder, so as to track which parts of the problematic axioms cause the unsatisfiability. Moreover, we support ontology users in rewriting problematic axioms. In order to minimise the impact of changes and prevent unintended entailment loss, harmful and helpful changes are identified and provided as guidelines. Based on the methods described we present a preliminary version of an interactive debugging tool and demonstrate its applicability in practice
9877562
RDFS(FA): A DL-ised Sub-language of RDFS
Description Logics (DLs), as a field of research, form a formal foundation of first-order semantic Web ontology languages, such as DAML+OIL and OWL. The Semantic Web will build on XML’s ability to define customized tagging schemes and RDF(S)’s flexible approach to representing data. RDF Schema (RDFS), however, has a non-standard metamodeling architecture, which makes some elements in the model have dual roles in the RDFS specification. As a result, the specification of its semantics requires a non-standard model theory, RDF MT. This leads to semantic problems when trying to layer DL-based and other conventional firstorder Web ontology languages on top of RDFS. In this paper we suggest layering Web ontology languages on top of RDFS(FA), a sub-language of RDFS, which is based on a (relatively) standard model-theoretic semantics. We will also compare this approach with the existing RDF Model Theory and show how their semantics relate to the semantics of OWL, a newly developed Web ontology language by W3C.
17950526
RDFS(FA) and RDF MT: Two Semantics for RDFS
RDF Schema (RDFS) has a non-standard metamodeling architecture, which makes some elements in the model have dual roles in the RDFS specification. As a result, this can be confusing and difficult to understand and, more importantly, the specification of its semantics requires a non-standard model theory. This leads to semantic problems when trying to layer conventional first order languages, like DAML+OIL, on top of RDFS. In this paper we will first demonstrate how this problem with RDFS can be solved in a sub-language of RDFS - RDFS(FA), which introduces a Fixed layer metamodeling Architecture to RDFS, based on a (relatively) standard model-theoretic semantics. Logical layer Semantic Web languages such as DAML+OIL and OWL can, therefore, be built on top of both the syntax and semantics of RDFS(FA). We will also compare this approach with the existing RDF Model Theory and discuss the advantages and disadvantages of the two approaches.
18411364
Uncertainty and RuleML Rulebases: A Preliminary Report
Uncertainty, like imprecision and vagueness, has gained considerable attention the last decade. To this extend we present a preliminary report on extending the Rule Markup Language (RuleML) with fuzzy set theory, in order to be able to represent and handle vague knowledge. We also provide semantics for the case of fuzzy FOL RuleML.
14220183
Web Ontology Reasoning with Datatype Groups
When providing reasoning services for ontology languages such as DAML+OIL and OWL, it is necessary for description logics to deal with "concrete" datatypes (strings, integers, etc.) as well as "abstract" concepts and relationships. In this paper, we present a new approach, the datatype group approach, to integrating DLs with multiple datatypes. We discuss the advantages of such approach over the existing ones and show how a tableaux algorithm for the description logic SHOQ(Dn) can be modified in order to reason with datatype groups.
171830
Inconsistencies, Negations and Changes in Ontologies
Ontology management and maintenance are considered cornerstone issues in current Semantic Web applications in which semantic integration and ontological reasoning play a fundamental role. The ability to deal with inconsistency and to accommodate change is of utmost importance in real-world applications of ontological reasoning and management, wherein the need for expressing negated assertions also arises naturally. For this purpose, precise, formal definitions of the the different types of inconsistency and negation in ontologies are required. Unfortunately, ontology languages based on Description Logics (DLs) do not provide enough expressive power to represent axiom negations. Furthermore, there is no single, well-accepted notion of inconsistency and negation in the Semantic Web community, due to the lack of a common and solid foundational framework. In this paper, we propose a general framework accounting for inconsistency, negation and change in ontologies. Different levels of negation and inconsistency in DL-based ontologies are distinguished. We demonstrate how this framework can provide a foundation for reasoning with and management of dynamic ontologies.
7020261
Improved partial distance search for k nearest-neighbor classification
An important method in pattern recognition is k nearest-neighbor classification. However, its computational complexity limits its real-time applications. The partial distance search is a solution to this problem. Although it is not very effective, it can be combined with other algorithms to reduce the complexity. The paper proposes an indexing method that uses the variance vector of feature vectors of a design set to improve the efficiency of the partial distance search. Experimental results indicate the effectiveness of this indexing preprocessing
10479007
Segmenting Motion Capture Data into Distinct Behaviors
Much of the motion capture data used in animations, commercials, and video games is carefully segmented into distinct motions either at the time of capture or by hand after the capture session. As we move toward collecting more and longer motion sequences, however, automatic segmentation techniques will become important for processing the results in a reasonable time frame.We have found that straightforward, easy to implement segmentation techniques can be very effective for segmenting motion sequences into distinct behaviors. In this paper, we present three approaches for automatic segmentation. The first two approaches are online, meaning that the algorithm traverses the motion from beginning to end, creating the segmentation as it proceeds. The first assigns a cut when the intrinsic dimensionality of a local model of the motion suddenly increases. The second places a cut when the distribution of poses is observed to change. The third approach is a batch process and segments the sequence where consecutive frames belong to different elements of a Gaussian mixture model. We assess these three methods on fourteen motion sequences and compare the performance of the automatic methods to that of transitions selected manually.
6313972
Analysis and evaluation of expiration-based hierarchical caching systems
This paper investigates some fundamental properties and performance issues of the expiration-based caching systems. We focus on the hierarchical caching systems based on the time-to-live (TTL) expiration mechanism, and present a basic model for such systems. By analyzing the intrinsic timing behavior in this model, we derive some important performance metrics from the perspectives of caching systems and end users, respectively. We use network simulation results to further substantiate the efficacy of our analysis. Our results show some basic properties and trade-offs for a hierarchical caching system based on the weak consistency mechanism.
7681917
ViVo: visual vocabulary construction for mining biomedical images
Given a large collection of medical images of several conditions and treatments, how can we succinctly describe the characteristics of each setting? For example, given a large collection of retinal images from several different experimental conditions (normal, detached, reattached, etc.), how can data mining help biologists focus on important regions in the images or on the differences between different experimental conditions? If the images were text documents, we could find the main terms and concepts for each condition by existing IR methods (e.g., tf/idf and LSI). We propose something analogous, but for the much more challenging case of an image collection: We propose to automatically develop a visual vocabulary by breaking images into n /spl times/ n tiles and deriving key tiles ("ViVos") for each image and condition. We experiment with numerous domain-independent ways of extracting features from tiles (color histograms, textures, etc.), and several ways of choosing characteristic tiles (PCA, ICA). We perform experiments on two disparate biomedical datasets. The quantitative measure of success is classification accuracy: Our "ViVos" achieve high classification accuracy (up to 83 %for a nine-class problem on feline retinal images). More importantly, qualitatively, our "ViVos" do an excellent job as "visual vocabulary terms": they have biological meaning, as corroborated by domain experts; they help spot characteristic regions of images, exactly like text vocabulary terms do for documents; and they highlight the differences between pairs of images.
13091217
A Parallel Particle Swarm Optimization Algorithm with Communication Strategies
Particle swarm optimization (PSO) is an alternative population-based evolutionary computation technique. It has been shown to be capable of optimizing hard mathematical problems in continuous or binary space. We present here a parallel version of the particle swarm optimization (PPSO) algorithm together with three communication strategies which can be used according to the independence of the data. The first strategy is designed for solution parameters that are independent or are only loosely correlated, such as the Rosenbrock and Rastrigrin functions. The second communication strategy can be applied to parameters that are more strongly correlated such as the Griewank function. In cases where the properties of the parameters are unknown, a third hybrid communication strategy can be used. Experimental results demonstrate the usefulness of the proposed PPSO algorithm.