Remarks on Automated Ontology Merging Algorithm

2008 ◽  
Author(s):  
Monika Koprowska
Keyword(s):  
Ontology Merging ◽  
Merging Algorithm

Ontology-Based Partner Selection in Business Interaction

2011 ◽  
pp. 364-380
Author(s):  
Jingshan Huang ◽  
Jiangbo Dang ◽  
Michael N. Huhns
Keyword(s):  
Global Economy ◽  
Service Providers ◽  
Partner Selection ◽  
Vector System ◽  
Ontology Merging ◽  
Business Partners ◽  
Merging Algorithm

Traditional businesses are finding great advantages from the incorporation of e-business capabilities, especially for participation in the global economy, which is inherently open and dynamic. This imposes a requirement that businesses must coordinate with each other if they are to be most efficient and successful. To aid in this coordination and achieve seamless and autonomic interoperation, e-business partners are chosen to be represented by service agents. However, before agents are able to coordinate well with each other, they need to understand each others’ service descriptions. Ontologies developed by service providers to describe their service can render help. Unfortunately, due to the heterogeneity implicit in independently designed ontologies, distributed e-businesses will encounter semantic mismatches and misunderstandings. We introduce a compatibility vector system, created upon a schema-based ontology-merging algorithm, to determine and maintain ontology compatibility, which can be used as a basis for businesses to select candidate partners with which to interoperate.

scholarly journals A Clique Merging Algorithm to Solve Semidenite Relaxations of Optimal Power Flow Problems

2020 ◽  
pp. 1-1 ◽  
Author(s):  
Julie Sliwak ◽  
Erling Andersen ◽  
Miguel F Anjos ◽  
Lucas Letocart ◽  
Emiliano Traversi
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Flow Problems ◽  
Merging Algorithm ◽  
Optimal Power

A Meta-Mining Ontology Framework for Data Processing

2021 ◽  
Vol 12 (2) ◽  
pp. 37-56
Author(s):  
Man Tianxing ◽  
Nataly Zhukova ◽  
Alexander Vodyaho ◽  
Tin Tun Aung
Keyword(s):  
Data Mining ◽  
Data Processing ◽  
Data Streams ◽  
Specific Data ◽  
Ontology Merging ◽  
Knowledge Models ◽  
Task Requirements ◽  
Ontology Extraction ◽  
Merging Method ◽  
And Task

Extracting knowledge from data streams received from observed objects through data mining is required in various domains. However, there is a lack of any kind of guidance on which techniques can or should be used in which contexts. Meta mining technology can help build processes of data processing based on knowledge models taking into account the specific features of the objects. This paper proposes a meta mining ontology framework that allows selecting algorithms for solving specific data mining tasks and build suitable processes. The proposed ontology is constructed using existing ontologies and is extended with an ontology of data characteristics and task requirements. Different from the existing ontologies, the proposed ontology describes the overall data mining process, used to build data processing processes in various domains, and has low computational complexity compared to others. The authors developed an ontology merging method and a sub-ontology extraction method, which are implemented based on OWL API via extracting and integrating the relevant axioms.

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