64356
Querying the Semantic Web with Preferences
Ranking is an important concept to avoid empty or overfull and unordered result sets. However, such scoring can only express total orders, which restricts its usefulness when several factors influence result relevance. A more flexible way to express relevance is the notion of preferences. Users state which kind of answers they ‘prefer’ by adding soft constraints to their queries. 
 
Current approaches in the Semantic Web offer only limited facilities for specification of scoring and result ordering. There is no common language element to express and formalize ranking and preferences. We present a comprehensive extension of SPARQL which directly supports the expression of preferences. This includes formal syntax and semantics of preference expressions for SPARQL. Additionally, we report our implementation of preference query processing, which is based on the ARQ query engine.
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.
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.
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.
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
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.
18784921
Multidimensional Vector Regression for Accurate and Low-Cost Location Estimation in Pervasive Computing
In this paper, we present an algorithm for multidimensional vector regression on data that are highly uncertain and nonlinear, and then apply it to the problem of indoor location estimation in a wireless local area network (WLAN). Our aim is to obtain an accurate mapping between the signal space and the physical space without requiring too much human calibration effort. This location estimation problem has traditionally been tackled through probabilistic models trained on manually labeled data, which are expensive to obtain. In contrast, our algorithm adopts kernel canonical correlation analysis (KCCA) to build a nonlinear mapping between the signal-vector space and the physical location space by transforming data in both spaces into their canonical features. This allows the pairwise similarity of samples in both spaces to be maximally correlated using kernels. We use a Gaussian kernel to adapt to the noisy characteristics of signal strengths and a Matern kernel to sense the changes in physical locations. By using real data collected in an 802.11 wireless LAN environment, we achieve accurate location estimation for pervasive computing while requiring a much smaller set of labeled training data than previous methods
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.
3648465
Optimal base-station locations in two-tiered wireless sensor networks
We consider generic two-tiered wireless sensor networks (WSNs) consisting of sensor clusters deployed around strategic locations, and base-stations (BSs) whose locations are relatively flexible. Within a sensor cluster, there are many small sensor nodes (SNs) that capture, encode, and transmit relevant information from a designated area, and there is at least one application node (AN) that receives raw data from these SNs, creates a comprehensive local-view, and forwards the composite bit-stream toward a BS. This paper focuses on the topology control process for ANs and BSs, which constitute the upper tier of two-tiered WSNs. Since heterogeneous ANs are battery-powered and energy-constrained, their node lifetime directly affects the network lifetime of WSNs. By proposing algorithmic approaches to locate BSs optimally, we can maximize the topological network lifetime of WSNs deterministically, even when the initial energy provisioning for ANs is no longer always proportional to their average bit-stream rate. The obtained optimal BS locations are under different lifetime definitions according to the mission criticality of WSNs. By studying intrinsic properties of WSNs, we establish the upper and lower bounds of maximal topological lifetime, which enable a quick assessment of energy provisioning feasibility and topology control necessity. Numerical results are given to demonstrate the efficacy and optimality of the proposed topology control approaches designed for maximizing network lifetime of WSNs.
2042793
Peer Collaboration in Wireless Ad Hoc Networks
Voluntary peer collaboration is often assumed in media access, route discovery, packet forwarding, and upper-layer protocols for wireless ad hoc networks. This assumption is seriously challenged when some peers are autonomous, selfish, or malicious in large-scale, heterogeneous networks. In this paper, based on the latest advances in identity-based cryptography, we design a lightweight and cheat-resistant micropayment scheme to stimulate and compensate collaborative peers that sacrifice their resources to relay packets for other peers. We also demonstrate that when security and collaboration measures are properly enforced, profitable collaboration is a preferable strategy for all peers.