11631353
f-SWRL: A Fuzzy Extension of SWRL
Although the combination of OWL and Horn rules results in the creation of a highly expressive language, i.e. SWRL, there are still many occasions where this language fails to accurately represent knowledge of our world. In particular, SWRL fails at representing vague and imprecise knowledge and information. Such type of information is apparent in many applications like multimedia processing and retrieval, information fusion, etc. In this paper, we propose f-SWRL, a fuzzy extension to SWRL to include fuzzy assertions (such as ‘Mary is tall in the degree of 0.9’) and fuzzy rules (such as ‘being healthy is more important than being rich to determine if one is happy’).
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.
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.
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
15823770
Supporting Semi-Automatic Semantic Annotation of Multimedia Resources
Ontologies provide an attractive basis for the representation of semantic information to be attached to multimedia information. However, the flexibility available to develop one’s own ontology or partially reuse an existing ontology means that human input is unavoidable in the process of creating the annotations. We address the issue of how to use ontology reasoning services and natural language generation to provide presentations of relevant ontology structures for human use.
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.
40837902
VQ image coding using sub-vector techniques
A fast codeword search algorithm for vector quantization is presented. Three inequalities based on the characteristics of sums and variances of a vector and its two sub-vectors are introduced to reject a larger number of codewords. Simulation results demonstrate the effectiveness of the proposed algorithm.
16432489
Genetic watermarking based on transform-domain techniques
Abstract An innovative watermarking scheme based on genetic algorithms (GA) in the transform domain is proposed. It is robust against watermarking attacks, which are commonly employed in the literature. In addition, the watermarked image quality is also considered. In this paper, we employ GA for optimizing both the fundamentally conflicting requirements. Watermarking with GA is easy for implementation. We also examine the effectiveness of our scheme by checking the fitness function in GA, which includes both factors related to robustness and invisibility. Simulation results also show both the robustness under attacks, and the improvement in watermarked image quality with GA.
3926195
Fast Random Walk with Restart and Its Applications
How closely related are two nodes in a graph? How to compute this score quickly, on huge, disk-resident, real graphs? Random walk with restart (RWR) provides a good relevance score between two nodes in a weighted graph, and it has been successfully used in numerous settings, like automatic captioning of images, generalizations to the "connection subgraphs", personalized PageRank, and many more. However, the straightforward implementations of RWR do not scale for large graphs, requiring either quadratic space and cubic pre-computation time, or slow response time on queries. We propose fast solutions to this problem. The heart of our approach is to exploit two important properties shared by many real graphs: (a) linear correlations and (b) block- wise, community-like structure. We exploit the linearity by using low-rank matrix approximation, and the community structure by graph partitioning, followed by the Sherman- Morrison lemma for matrix inversion. Experimental results on the Corel image and the DBLP dabasets demonstrate that our proposed methods achieve significant savings over the straightforward implementations: they can save several orders of magnitude in pre-computation and storage cost, and they achieve up to 150x speed up with 90%+ quality preservation.
6676888
Constrained Ant Colony Optimization for Data Clustering
Processes that simulate natural phenomena have successfully been applied to a number of problems for which no simple mathematical solution is known or is practicable. Such meta-heuristic algorithms include genetic algorithms, particle swarm optimization and ant colony systems and have received increasing attention in recent years. 
 
This paper extends ant colony systems and discusses a novel data clustering process using Constrained Ant Colony Optimization (CACO). The CACO algorithm extends the Ant Colony Optimization algorithm by accommodating a quadratic distance metric, the Sum of K Nearest Neighbor Distances (SKNND) metric, constrained addition of pheromone and a shrinking range strategy to improve data clustering. We show that the CACO algorithm can resolve the problems of clusters with arbitrary shapes, clusters with outliers and bridges between clusters.
7197213
A Manifold Regularization Approach to Calibration Reduction for Sensor-Network Based Tracking
The ability to accurately detect the location of a mobile node in a sensor network is important for many artificial intelligence (AI) tasks that range from robotics to context-aware computing. Many previous approaches to the location-estimation problem assume the availability of calibrated data. However, to obtain such data requires great effort. In this paper, we present a manifold regularization approach known as LeMan to calibration-effort reduction for tracking a mobile node in a wireless sensor network. We compute a subspace mapping function between the signal space and the physical space by using a small amount of labeled data and a large amount of unlabeled data. This mapping function can be used online to determine the location of mobile nodes in a sensor network based on the signals received. We use Crossbow MICA2 to setup the network and USB camera array to obtain the ground truth. Experimental results show that we can achieve a higher accuracy with much less calibration effort as compared to several previous systems.