A survey of semantic web technology for agriculture

An ontology-based approach to integration of hilly citrus production knowledge

Towards a Legal Rule-Based System Grounded on the Integration of Criminal Domain Ontology and Rules

In this paper, according to the characteristics of maize disease knowledge, OWL DL language was used to build maize diseases ontology, and the reasoning rule of maize diseases was defined by using the expressive ability of SWRL rule language.The author introduced several realizable reasoning functions,and achieved the diagnostic reasoning of maize disease knowledge by Jess inference engine.The results indicated that constructing the maize diseases ontology,and introducing SWRL rule into maize disease ontology provided an effective way for the construction of high-intelligent, shareable and reused maize disease knowledge database and diagnostic rule database.

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Research on Construction and SWRL Reasoning of Ontology of Maize Diseases

The ability to collaborate has always been vitally important to businesses and enterprises. With the availability of current networking and computing power, the creation of Collaborative Working Environments (CWEs) has allowed for this process to occur anytime over any geographical distance. Sharing information between individuals through collaborative environments creates new challenges in privacy protection for organizations and the members of organizations. This thesis confronts the problems when attempting to protect the personal private information of collaborating individuals. In this thesis, a privacy-by-policy approach is taken to addressing the issue of protecting private information within collaborative environments. A privacy-bypolicy approach to privacy protection provides collaborating individuals with notice and choice surrounding their private information, in order to provide an individual with a level of control over how their information is to be used. To this end, a collaborative privacy architecture for providing privacy within a collaborative environment is presented. This architecture uses ontologies to express the static concept and relation definitions required for privacy and collaboration. The collaborative privacy architecture also contains a Collaborative Privacy Manager (CPM) service which handles changes in dynamic collaborative environments. The goals of this thesis are to provide privacy mechanisms for the non-client centric situation of collaborative working environments. This thesis also strives to provide privacy through technically enforceable and customizable privacy policies. To this end, individual collaborators are provided with access, modification rights, and transparency through the use of ontologies built into the architecture. Finally, iii individual collaborators are provided these privacy protections in a way that is easy to use and understand and use. A collaborative scenario as a test case is described to present how this architecture would benefit individuals and organizations when they are engaged in collaborative work. In this case study a university and hospital are engaged in collaborative research which involves the use of private information belonging to collaborators and patients from the hospital. This case study also highlights how different organizations can be under different sets of legislative guidelines and how these guidelines can be incorporated into the privacy architecture. Through this collaboration scenario an implementation of the collaborative privacy architecture is provided, along with results from semantic and privacy rule executions, and measurements of how actions carried out by the architecture perform under various conditions.

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Protecting Personal Private Information in Collaborative Environments

Semantic not consistency, and knowledge base is difficult to reuse and sharing are the key problems affecting the system development and application. This paper studies how to express the soil fertility level information using of the ontology and generate OWL (Ontology Web Language) document, and how to make use of SWRL (Semantic Web Rule Language) to express inference rules. On this basis, this paper integrates SWRL rules editor and JESS (java expert shell system) rules engine, establishes the reasoning framework based on JESS reasoning engine, and realizes the productivity grade evaluation based on ontology and SWRL.

https://hal.inria.fr/hal-01351836/document

The Knowledge Representation and Semantic Reasoning Realization of Productivity Grade Based on Ontology and SWRL

According to the uncertainty, dynamics and dependency of maize diseases, this paper puts forward a kind of maize disease diagnosis model combined ontology with multi-Agent. The knowledge representation of ontology provides a clear formal specification for the onset periods and symptom characteristics during maize disease occurs. Using the Agent’s intelligence and the division collaboration of Agent modules, we have solved the bottleneck problem of the acquisition and representation of maize disease knowledge and improved the learning ability of the ontology. Experiments show that the model can diagnose maize diseases accurately and efficiently and has a good application prospect.

/pdf/maize-disease-diagnosis-model-based-on-ontology-and-multi-1grmmxbl5c.pdf

Maize Disease Diagnosis Model Based on Ontology and Multi-Agent

Nowadays, there is a variety of methods on soil nutrient environment prediction, in order to improve the accuracy of prediction data, more than prediction model can be used. The Jilin province is an important agricultural production area in China. The analysis of the soil nutrition environment is an important theoretical basis for agricultural planting structure. It is play important role to make the food security and agricultural sustainable development strategy. The research results show that the optimal partition number is 8 kinds of nutrient types. From the research results, it can be found the method of principal component and cluster analysis can be successfully applied to classify soils in Jilin province.Not only the partition results can guide sampling, but also provide an important method for accurately predicting the soil nutrition environment in agricultural products.The results can be used to implement variable input and precise fertilization recommendation.

Study on Nutrient Characteristics Analysis of Back Soil Area

Soil fertility is the comprehensive reflection of related factors and the related factors. Soil fertility evaluation knowledge is stored by relational database as usually, and it is difficult to show the correlation and constraints among attributes .In this paper, Nongan county farmland productivity data is as the research object, Using rough set approach to do attribute reduction, using ontology method to establish the soil fertility level knowledge base, using multi Agent technology to implement the prototype system, and complete the reuse and sharing of knowledge, lay the foundation for semantic level reasoning.

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Li Ma

Papers

Research on Construction and SWRL Reasoning of Ontology of Maize Diseases

The Knowledge Representation and Semantic Reasoning Realization of Productivity Grade Based on Ontology and SWRL

Maize Disease Diagnosis Model Based on Ontology and Multi-Agent

Study on Nutrient Characteristics Analysis of Back Soil Area

Research on the Construction and Implementation of Soil Fertility Knowledge Based on Ontology