Mathematical Service Matching Using Description Logic and OWL

Caprotti, Olga; Dewar, Mike; Turi, Daniele

doi:10.1007/978-3-540-27818-4_6

Cited by 21 publications

(13 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Interactions between ontologies (the Ontology Web Language (OWL) is used to describe each ontology) that form part of the MONET project [6] can be found in Fig. 1 and include:…”

Section: Monet Ontologiesmentioning

confidence: 99%

Performance evaluation of semantic registries: OWLJessKB and instanceStore

Ludwig

Rana

2008

SOCA

View full text Add to dashboard Cite

Service discovery is a critical task in distributed computing architectures for finding a particular service instance. Semantic annotations of services help to enrich the service discovery process. Semantic registries are an important component for the discovery of services and they allow for semantic interoperability through ontology-based query formulation and dynamic mapping of terminologies between system domains. This paper evaluates two semantic registries-OWLJessKB implementation and instanceStore-to determine the suitability of these with regards to the query response time and the overall scalability for use in mathematical services. Mathematical ontologies from the MONET project are used to undertake comparison. The results demonstrate that the performance of registries may be compared across two axes: (1) time to initialize (i.e. time to load an initial ontology into memory); (2) time to query (i.e. time to reason with an ontology loaded into memory).

show abstract

“…Interactions between ontologies (the Ontology Web Language (OWL) is used to describe each ontology) that form part of the MONET project [6] can be found in Fig. 1 and include:…”

Section: Monet Ontologiesmentioning

confidence: 99%

Performance evaluation of semantic registries: OWLJessKB and instanceStore

Ludwig

Rana

2008

SOCA

View full text Add to dashboard Cite

show abstract

“…(4) Load of the service copy The load of the service copy is the number of copy services having the same function being accessed [14,15] . According to Fig.7(a) and Fig.7(b), take samples from probability and statistics of the copy services by using keyword discovering model and ontologydriven discovering model in a test period (one week).…”

Section: Fig 4 Relation Of Semantic Factors and Conflict Ratesmentioning

confidence: 99%

Ontology-driven discovering model for geographical information services

Zhang

Ke-ke

Pan

et al. 2010

Geo-spatial Information Science

View full text Add to dashboard Cite

This paper proposes an ontology-driven discovering model for the geographical information services to improve their recall ratio and precision ratio. This model uses the geographical information service ontology. In this paper, first we study the multilevel matching arithmetic of geographical information services. This arithmetic is used for filtering and matching the services in the service register center according to the similarity between services selected and services requested from the definition of the function similarity and credit standing similarity. The matching arithmetic, geographical information service ontology and semantic description constitute the discovering model. Finally, we test and analyze the model from the recall ratio, precision ratio, responsivity and load balance. The result indicates that the ontology-driven discovering model is excellent in recall ratio and precision ratio, and can maintain the dynamic load balance of service copy.

show abstract

“…However, current publications [1] seem to indicate that matching is limited to processing taxonomies and the functional issues raised by pre-and post-conditions are not considered. The MONET broker [3], in conjunction with the RACER reasoner and the Instance Store demonstrated one of the earliest uses of a description logic reasoner to identify services based on taxonomic descriptions coming closest to the objective of the plug-ins developed for GENSS in attempting to provide functional matching of task and capability.…”

Section: Related Workmentioning

confidence: 99%

Matchmaking Support for Dynamic Workflow Composition

Chapman

Ludwig

Naylor³

et al. 2007

Third IEEE International Conference on E-Science and Grid Computing (E-Science 2007)

View full text Add to dashboard Cite

Service description and discovery offer complementary challenges, but in both cases, the problem is finding the right trade-off between accuracy and generality that will result in a positive service identification. Discovery systems have historically tended to focus on domain-specific techniques using single sources of knowledge to help classify queries against services, making both maintenance and extension difficult. The primary contribution of this paper is the presentation of a generic brokerage framework based on the use of plug-in components, that are themselves web services. The framework has been developed in the context of the KNOOGLE project, where the focus has been on demonstrating support for (i) the discovery of Grid services for the GridSAM job submission system and (ii) integration with the Taverna workflow enactment system. However, the broker itself is domain independent and it is the multiple user-specified matchmaker plug-ins that act as sources of domain-specific knowledge. The broker collects the results of the matchmakers' comparison of the query and service and then applies a user-specified selection policy to determine the final choice of service. Thus a range of comprehensive packaging of brokerage functionality becomes possible through the use of supplied and user-defined matchers and supplied or user-defined selection policies.Third IEEE International Conference on e-Science and Grid Computing 0-7695-3064-8/07 $25.00

show abstract

Mathematical Service Matching Using Description Logic and OWL

Cited by 21 publications

References 2 publications

Performance evaluation of semantic registries: OWLJessKB and instanceStore

Performance evaluation of semantic registries: OWLJessKB and instanceStore

Ontology-driven discovering model for geographical information services

Matchmaking Support for Dynamic Workflow Composition

Contact Info

Product

Resources

About