2012
DOI: 10.1007/s11432-012-4697-1
|View full text |Cite
|
Sign up to set email alerts
|

A broker-based semantic agent for discovering Semantic Web services through process similarity matching and equivalence considering quality of service

Abstract: The lack of semantic descriptions for "web service properties" makes it difficult to find suitable web services. Current solutions are mostly based on broker/mediator agent systems. However, these techniques are syntactical, rather than semantics oriented. This article presents a semantic matching approach for discovering Semantic Web services through a broker-based semantic agent (BSA). The BSA includes knowledge-bases and several processing steps. The BSA's knowledge-bases are concept, task, and process onto… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
10
0

Year Published

2014
2014
2021
2021

Publication Types

Select...
7
1

Relationship

1
7

Authors

Journals

citations
Cited by 19 publications
(12 citation statements)
references
References 14 publications
0
10
0
Order By: Relevance
“…Let A contain m number of output parameters, denoted by 1 2 3 , , ..., DoM o o is defined as fail. In order to obtain useful information about similarity the levels of DoM are assigned with numerical equivalents by heuristics similar to Duygu Celik et al [32], Mehdi Bayat et al [33] and Syeda-Mahmood T et al [34]. The levels of DoM proposed in this work along with numeric equivalents are compared with [32][33][34] as given in Table 1.…”
Section: Output Similarity Modelmentioning
confidence: 99%
See 1 more Smart Citation
“…Let A contain m number of output parameters, denoted by 1 2 3 , , ..., DoM o o is defined as fail. In order to obtain useful information about similarity the levels of DoM are assigned with numerical equivalents by heuristics similar to Duygu Celik et al [32], Mehdi Bayat et al [33] and Syeda-Mahmood T et al [34]. The levels of DoM proposed in this work along with numeric equivalents are compared with [32][33][34] as given in Table 1.…”
Section: Output Similarity Modelmentioning
confidence: 99%
“…To compute the value of [32][33][34] DoM values as per [32] DoM values as per [33] DoM values as per [34] DoM levels as per …”
Section: Output Similarity Modelmentioning
confidence: 99%
“…The CME finds the semantic distance weight d weight ( A , Z ) between any two concepts A and Z in a particular domain ontology as given by ( 2 ): The applied CME scoring method is a simple multiplicative weighting function. A given ingredient side effects concept list C with input concepts C = { C 1 , C 2 ,…, C m } and a product P with ingredient concepts P = { P 1 , P 2 ,…, P n } are matched, and the total score IntoleranceScore ( C , P ) is calculated according to ( 3 ) (Çelik and Elçi [ 15 ]): Before going into details of the usage of the formulas given above in the proposed system, some formal information should be provided about these subconcepts since they are commonly used as food additives, as presented in the literature discussed below. According to the Micronutrient Research for Optimum Health of the Linus Pauling Institute ( http://lpi.oregonstate.edu/infocenter/vitamins/vitaminC/vitCform.html ), Ester C contains mainly Calcium Ascorbate but also contains small amounts of the Vitamin C metabolites, Dehydroascorbic Acid (oxidized Ascorbic Acid), Calcium Threonate, and trace levels of Xylonate and Lyxonate.…”
Section: Working Mechanism Of the Ontology-driven Mobile Safe Foodmentioning
confidence: 99%
“…It identifies the current situation based on the previous fever values and symptoms to parent and doctor as a report before taking the next medical step decision by his/her doctor. The semantic matching approach is similar to that of some recent studies of semantic service search in accordance with the users' needs [8][9][10].…”
Section: Digital Thermometer -Pediatric Digital Thermometermentioning
confidence: 99%