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.
1422959
Reasoning Support for OWL-E
Syntax DL Syntax Semantics domain(u1, . . . , un) (u1, . . . , un) u D 1 × . . .× un
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.
11147015
A Fuzzy Extension of SWRL
Experience in using ontologies and rules in applications has shown that in many cases we would like to extend their representational and reasoning capabilities to deal with vague or imprecise knowledge. For example, multimedia applications have highlighted the need to extend representation languages with capabilities which allow for the treatment of the inherent imprecision in multimedia object representation, matching, detection and retrieval. Unfortunately, neither OWL nor SWRL provides such capabilities. In order to capture imprecision in rules, we propose fuzzy extensions of SWRL (SWRL is proposed by the Joint US/EU ad hoc Agent Markup Language Committee., the reader is referred to [HPSB04] for full details of the model-theoretic semantics and abstract syntax of SWRL.). In this extension OWL facts (axioms relating to individuals) can include a specification of the “degree” (a truth value between 0 and 1) of confidence with which we assert that an individual (resp. pair of individuals) is an instance of a given class (resp. property); and SWRL atoms can include a “weight” (a truth value between 0 and 1) that represents the “importance” of the atom in a rule.The syntax extends the existing abstract syntax for SWRL, while the semantics is based on the theory of fuzzy sets [Zad65]. For example, the following fuzzy rule asserts that if one has a parent that is happy, then one is likely (with the weight 0.8) to be happy:
33448636
OWL-Eu: Adding Customised Datatypes into OWL
Although OWL is rather expressive, it has a very serious limitation on datatypes; i.e., it does not support customised datatypes. It has been pointed out that many potential users will not adopt OWL unless this limitation is overcome. Accordingly, the Semantic Web Best Practices and Development Working Group sets up a task force to address this issue. This paper makes the following two contributions: (i) it provides a brief summary of OWL-related datatype formalisms, and (ii) it provides a decidable extension of OWL DL, called OWL-Eu, that supports customised datatypes.
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.
16470845
Dynamic server selection using fuzzy inference in content distribution networks
To accommodate the exponential growth of Web traffic, Content Distribution Networks (CDN) have been designed and deployed to distribute content to different cache servers, and to transparently and dynamically redirect user requests to the cache servers according to the latest network and server status. Server selection therefore is vital and crucial to both the functionality and performance of any CDN systems. An appropriate server should be selected by taking estimated user location, measured round-trip time, and advertised server load into account. However, it is unlikely to obtain accurate and timely inputs of these parameters in practice, so that the effectiveness and efficiency of CDN cannot be fully achieved by traditional means. In this paper, a novel CDN server selection scheme using fuzzy inference is proposed. The scheme selects appropriate servers based on partial round-trip time measurements and historical server load information, and it can be implemented generically wherever the decision is made. It is shown that the fuzzy inference-based scheme is inherently capable of handling multiple decision inputs efficiently, tolerable to measurement noise and errors, and able to deal with network dynamics. Simulation results demonstrate that, compared with other server selection schemes, the proposed scheme can achieve higher resource utilization, provide better user-perceived Quality of Service (QoS), and efficiently deal with network dynamics.
44484805
Parallel Ant Colony Systems
In this paper a Parallel Ant Colony System (PACS) is developed. Three communication methods for updating the pheromone level between groups in PACS are proposed and work on the traveling salesman problem using our system is presented. Experimental results based on three well-known traveling salesman data sets demonstrate the proposed PACS is superior to the existing Ant Colony System (ACS) and Ant System (AS) with similar or better running times.
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).
9847155
GCap: Graph-based Automatic Image Captioning
Given an image, how do we automatically assign keywords to it? In this paper, we propose a novel, graph-based approach (GCap) which outperforms previously reported methods for automatic image captioning. Moreover, it is fast and scales well, with its training and testing time linear to the data set size. We report auto-captioning experiments on the "standard" Corel image database of 680 MBytes, where GCap outperforms recent, successful auto-captioning methods by up to 10 percentage points in captioning accuracy (50% relative improvement).
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.