33668284
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').
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
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.
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.
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.
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.
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.
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.
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.
1997586
Automatic multimedia cross-modal correlation discovery
Given an image (or video clip, or audio song), how do we automatically assign keywords to it? The general problem is to find correlations across the media in a collection of multimedia objects like video clips, with colors, and/or motion, and/or audio, and/or text scripts. We propose a novel, graph-based approach, "MMG", to discover such cross-modal correlations.Our "MMG" method requires no tuning, no clustering, no user-determined constants; it can be applied to any multimedia collection, as long as we have a similarity function for each medium; and it scales linearly with the database size. We report auto-captioning experiments on the "standard" Corel image database of 680 MB, where it outperforms domain specific, fine-tuned methods by up to 10 percentage points in captioning accuracy (50% relative improvement).