Research of Plant Domain Knowledge Model Based on Ontology

2008 ◽  
Author(s):  
Jing Fan ◽  
Xin-Pei Zhang ◽  
Tian-Yang Dong
Keyword(s):  
Domain Knowledge ◽  
Knowledge Model ◽  
Model Based

scholarly journals Ontology-Based Knowledge Model for Multi-View KDD Process

2012 ◽  
Vol 4 (3) ◽  
pp. 21-33 ◽  
Author(s):  
EL Moukhtar Zemmouri ◽  
Hicham Behja ◽  
Abdelaziz Marzak ◽  
Brigitte Trousse
Keyword(s):  
Knowledge Discovery ◽  
Domain Knowledge ◽  
Knowledge Discovery In Databases ◽  
Knowledge Model ◽  
Interactive Process ◽  
Types Of Knowledge ◽  
Do So

Knowledge Discovery in Databases (KDD) is a highly complex, iterative and interactive process that involves several types of knowledge and expertise. In this paper the authors propose to support users of a multi-view analysis (a KDD process held by several experts who analyze the same data with different viewpoints). Their objective is to enhance both the reusability of the process and coordination between users. To do so, they propose a formalization of viewpoint in KDD and a Knowledge Model that structures domain knowledge involved in a multi-view analysis. The authors’ formalization, using OWL ontologies, of viewpoint notion is based on CRISP-DM standard through the identification of a set of generic criteria that characterize a viewpoint in KDD.

Enhanced Compressive Imaging Using Model-Based Acquisition: Smarter sampling by incorporating domain knowledge

2016 ◽  
Vol 33 (5) ◽  
pp. 81-94 ◽  
Author(s):  
Aswin C. Sankaranarayanan ◽  
Matthew A. Herman ◽  
Pavan Turaga ◽  
Kevin F. Kelly
Keyword(s):  
Domain Knowledge ◽  
Compressive Imaging ◽  
Model Based

scholarly journals An ontology-based knowledge management framework for a distributed water information system

2012 ◽  
Vol 15 (4) ◽  
pp. 1169-1188 ◽  
Author(s):  
Qing Liu ◽  
Quan Bai ◽  
Corne Kloppers ◽  
Peter Fitch ◽  
Qifeng Bai ◽  
...  
Keyword(s):  
Knowledge Management ◽  
Life Cycle ◽  
Domain Knowledge ◽  
Heterogeneous Systems ◽  
Semantic Knowledge ◽  
Knowledge Model ◽  
Provenance Information ◽  
Management Framework ◽  
Data Transparency ◽  
Multiple Domains

With the increasing complexity of hydrologic problems, data collection and data analysis are often carried out in distributed heterogeneous systems. Therefore it is critical for users to determine the origin of data and its trustworthiness. Provenance describes the information life cycle of data products. It has been recognised as one of the most promising methods to improve data transparency. However, due to the complexity of the information life cycle involved, it is a challenge to query the provenance information which may be generated by distributed systems, with different vocabularies and conventions, and may involve knowledge of multiple domains. In this paper, we present a semantic knowledge management framework that tracks and integrates provenance information across distributed heterogeneous systems. It is underpinned by the Integrated Knowledge model that describes the domain knowledge and the provenance information involved in the information life cycle of a particular data product. We evaluate the proposed framework in the context of two real-world water information systems.

A Knowledge Model of Digital Evidence Review Elements Based on Ontology

2020 ◽  
pp. 281-290
Author(s):  
Ning Wang
Keyword(s):  
Knowledge Base ◽  
Domain Ontology ◽  
Digital Evidence ◽  
Knowledge Model ◽  
Ontology Construction ◽  
Model Based ◽  
Application Ontology ◽  
Evidence Review ◽  
Ontology Application ◽  
Knowledge Classification

As existing methods cannot express, share, and reuse the digital evidence review information in a unified manner, a solution of digital evidence review elements knowledge base model based on ontology is presented. Firstly, combing with the multi-source heterogeneous characteristic of digital evidence review knowledge, classification and extraction are accomplished. Secondly, according to the principles of ontology construction, the digital evidence review elements knowledge base model which includes domain ontology, application ontology, and atomic ontology is established. Finally, model can effectively acquire digital evidence review knowledge by analyzing review scenario.

A Knowledge Model of Digital Evidence Review Elements Based on Ontology

2017 ◽  
Vol 9 (3) ◽  
pp. 49-57 ◽  
Author(s):  
Ning Wang
Keyword(s):  
Knowledge Base ◽  
Domain Ontology ◽  
Digital Evidence ◽  
Knowledge Model ◽  
Ontology Construction ◽  
Model Based ◽  
Application Ontology ◽  
Evidence Review ◽  
Ontology Application ◽  
Knowledge Classification

As existing methods cannot express, share, and reuse the digital evidence review information in a unified manner, a solution of digital evidence review elements knowledge base model based on ontology is presented. Firstly, combing with the multi-source heterogeneous characteristic of digital evidence review knowledge, classification and extraction are accomplished. Secondly, according to the principles of ontology construction, the digital evidence review elements knowledge base model which includes domain ontology, application ontology, and atomic ontology is established. Finally, model can effectively acquire digital evidence review knowledge by analyzing review scenario.

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