Integration of disparate information resources has long been a significant research topic. Semantic approaches can help by allowing expression of concepts divorced from syntax and allowing rich, structured meta-data t...
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ISBN:
(纸本)9781424492190
Integration of disparate information resources has long been a significant research topic. Semantic approaches can help by allowing expression of concepts divorced from syntax and allowing rich, structured meta-data to be published in a form that is amenable to machine processing, reasoning and inter-domain concept mapping. However the creation of mappings, especially in a dynamic federations of autonomous entities, is time-consuming and vulnerable to brittleness and high maintenance costs due to change at many levels in the system. In this paper we propose an approach to managing change and maximizing mapping reuse by building explicit models of the federal relationship context of mapping deployment. These descriptions enable automated support for mapping reuse suggestions and ease discovery of relevant mappings due to changes at the federation, peer domain, shared capability or local model levels.
When consuming data from federated domains, it is often necessary to identify the relationships that exist between the data schemas used in each domain. Discovering the exact nature of these relationships is difficult...
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When consuming data from federated domains, it is often necessary to identify the relationships that exist between the data schemas used in each domain. Discovering the exact nature of these relationships is difficult due to data set schema heterogeneity. Prior work has focused on inter-domain class equivalence. However it is not always possible to find an equivalent class in both schemas. For example, when instances are modeled as classes in one domain (e.g. router type) but as the attribute values of a single class in the other domain (e.g. router interface). This paper investigates whether when classifying instances in one data set against a second schema, it may be more useful to use some attribute (or attribute group) other than the original class type, to perform this classification. A machine-learning based classification approach to appropriate attribute selection is presented and its operation is evaluated using two large data-sets available on the web as Linked data. The classification problem is compounded by the less formal semantics of Linked data when compared to full ontologies but this also highlights the strength of our approach to dealing with noisy or under-specified data-sets and schemas. The experimental results show that our attribute selection approach is capable of discovering appropriate mappings for cases where the correspondence is conditioned on one attribute and that information gain provides a suitable scoring function for selection of correspondence patterns to describe these complex attribute-based mappings.
Federated policy systems are required to support the emergent complexity and organizational heterogeneity of modern Internet service delivery. This paper presents a distributed policy management approach which utilize...
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Federated policy systems are required to support the emergent complexity and organizational heterogeneity of modern Internet service delivery. This paper presents a distributed policy management approach which utilizes a flexible, tree-based capability authority model to partition and delegate federated capabilities or services. A trust management model and a delegation logic is defined which supports secure decentralized policy reasoning and addresses performance overheads due to distributed rule evaluation, threats from malformed or malicious federated principals and allows flexibility with respect to delegation chain reduction or capability authority re-partitioning. The system is evaluated through a security analysis and a prototype implementation of a federated policy engineering framework based on this logic is described. This framework is based on public key certificates and an extension to the Keynote Trust Management language. It provides practical management services such as key discovery and certificate revocation in addition to the core capability delegation function.
With the advent of Web 2.0, as web service composition gets easier, there is a trend towards nonexpert users not just consuming information and services, but also providing, aggregating, composing and eventually manag...
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ISBN:
(纸本)9781424460373;9781424460380
With the advent of Web 2.0, as web service composition gets easier, there is a trend towards nonexpert users not just consuming information and services, but also providing, aggregating, composing and eventually managing their own information, services and resources. However non-expert users need to manage their composed services in a way they can understand. Policy-based management promises the ability to control services in a consistent manner via high-level declarative directives, constraints and goals. However, a significant drawback of policy-based management for complex systems remains the lack of an automated mechanism to resolve the meaning of high-level goals so they can be enforced [1,2]. We propose an approach to compose the available heterogeneous management interfaces of underlying constituent services to produce a coherent higher-level management interface for the composite service. This composed management interface can then be presented to the user at a level of abstraction that corresponds to non-expert user's needs thereby allowing the user to express their specific requirements. Hence the user can manage their composite service, in a manner tailored to them, without the burden of needing to understand how to manage each constituent service. This supports lower-cost user-initiated management, without access to IT experts, thereby increasing productivity and lowering operating costs.
The heterogeneity of ontologies is a major obstacle to the promised interoperability of knowledge in the Semantic Web. Ontology mappings can help to mitigate the effects by specifying the correspondences between relat...
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The heterogeneity of ontologies is a major obstacle to the promised interoperability of knowledge in the Semantic Web. Ontology mappings can help to mitigate the effects by specifying the correspondences between related ontologies. However, the creation of mappings is still complex and time-consuming and therefore it is appealing to discover existing mappings and to reuse them. For reuse, it is essential to understand how a mapping was created and applied. Thus meta-data documenting the lifecycle of an ontology mapping is essential to facilitate management and reuse. Currently the mapping lifecycle is only fragmentary documented and in general the reuse of ontology mappings is insufficiently supported by applications and formats. This paper addresses the question to what extent a semantically expressive ontology-based meta-data model can aid in the discovery, management and reuse of ontology mappings. In particular, experimental results collected from a use-case study on mapping discovery are presented. Based on findings derived, it is shown that semantic rich and effective meta-data describing the ontology mapping lifecycle are crucial for management of ontology mappings. Finally, to address the identified challenges, a framework for management of ontology mappings is outlined.
Integrating and relating heterogeneous data using inference is one of the cornerstones of semantic technologies and there are a variety of ways in which this may be achieved. Cross source relationships can be automati...
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Integrating and relating heterogeneous data using inference is one of the cornerstones of semantic technologies and there are a variety of ways in which this may be achieved. Cross source relationships can be automatically translated or inferred using the axioms of RDFS/OWL, via user generated rules, or as the result of SPARQL query result transformations. For a given problem it is not always obvious which approach (or combination of approaches) will be the most effective and few guidelines exist for making this choice. This paper discusses these three approaches and demonstrates them using an "acquaintance" relationship drawn from data residing in common RDF information sources such as FOAF and DBLP data stores. The implementation of each approach is described along with practical considerations for their use. Quantitative and qualitative evaluation results of each approach are presented and the paper concludes with initial suggestions for guiding principles to help in selecting an appropriate approach for integrating heterogeneous semantic data sources.
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