scholarly journals Development of the ALIS IP Ontology: Merging Legal and Technical Perspectives

2008 ◽  
pp. 169-180 ◽  
Author(s):  
Claudia Cevenini ◽  
Giuseppe Contissa ◽  
Migle Laukyte ◽  
Régis Riveret ◽  
Rossella Rubino
Keyword(s):  
Ontology Merging

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.

scholarly journals Using ontology merging for the integration of information systems and the production capacity planning system

2019 ◽  
pp. 401-408
Author(s):  
N Yarushkina ◽  
A Romanov ◽  
A Filippov ◽  
A Dolganovskaya ◽  
M Grigoricheva
Keyword(s):  
Information System ◽  
Information Systems ◽  
Relational Database ◽  
Data Model ◽  
Capacity Planning ◽  
Production Capacity ◽  
Semantic Integration ◽  
Data Sources ◽  
Planning System ◽  
Ontology Merging

This article describes the method of integrating information systems of an aircraft factory with the production capacity planning system based on the ontology merging. The ontological representation is formed for each relational database (RDB) of integrated information systems. The ontological representation is formed in the process of analyzing the structure of the relational database of the information system (IS). Based on the ontological representations merging the integrating data model is formed. The integrating data model is a mechanism for semantic integration of data sources.

An ontology merging tool to facilitate interoperability between coastalsensor networks

2009 ◽  
Author(s):  
Shruthi Bheemireddy ◽  
Surya S. Durbha ◽  
Roger L. King ◽  
Santhosh K. Amanchi ◽  
Nicolas H. Younan
Keyword(s):  
Ontology Merging

Imprecise Ontology Merging Framework Design

2011 ◽  
Vol 135-136 ◽  
pp. 578-584
Author(s):  
Guan Yu Li ◽  
Yan Zhao ◽  
Hai Yan Li
Keyword(s):  
Semantic Web ◽  
Set Theory ◽  
Rough Set ◽  
Fuzzy Set Theory ◽  
Rough Set Theory ◽  
Distributed Environment ◽  
Ontology Merging ◽  
Web Environment ◽  
Framework Design ◽  
Efficient Measure

Precision is selected unwillingly by human being when dealing with imprecise objects because of the limitation of human cognitive ability, which deviates from the substance of the processed object when it gets the feasible way of solution. Nowadays, in terms of the research in the Ontology and the Semantic Web, the time for the transformation from the “precise phase” to the “imprecise phase” is ripe. The interoperability among ontologies is seriously blocked by the heterogeneity of ontologies constructed under distributed environment. In this case, Ontology merging in the same domain is the most effective method to solve ontology heterogeneity. Firstly, the improved fuzziness and the R-improved roughness are respectively defined and verified as the more efficient measure way for the fuzziness and roughness. Secondly, a composite appraisal method of fuzzy-rough relevancy in combination of the fuzzy set theory and the rough set theory is proposed, which can serve as the basis of the inquiry and reasoning of the imprecise ontology, the transformation reference of the fuzzy roughness set or the rough fuzziness set. Lastly, by employing semantic bridge generator and conflict processor, a novel multiple-mapping-based imprecise ontology merging framework is proposed. The example verification reveals that both the imprecise ontology merging efficiency can be improved and the merging source imprecise ontologies into object imprecise ontology can be done automatically under the semantic web environment.

Sign in / Sign up

Export Citation Format

Share Document