Case Study - Risk Management System for Agricultural Production

Case Study: Semantic Web Technologies for Risk Management Systems

Risk Management System for Agricultural Production

Maria Curie Skłodowska University is the largest university in Eastern Poland, with a tradition of over 70 years of teaching excellence. From the beginning, it has been considered a leader in the education sector, providing students with a world-class education. Over the years, more than 210,000 students have graduated from UMCS.

Maria Curie Skłodowska University is constantly developing through a variety of initiatives and activities and research projects. The University, in association with Cognitum, is about to offer a new curriculum focusing on semantic technologies for students studying information technology. Cognitum’s Ontorion Server™ with Fluent Editor™ will form the key part of the technology infrastructure that will be used in the program.

The Goal

Agriculture is a sector of the economy which is highly susceptible to the influence of natural factors (temperature, rainfall, sunshine, etc.), as well as economic factors. The main objective of the project was to reduce risk within the process of agricultural production by increasing farmers’ access to knowledge about natural processes and market forces. In order to accomplish this, researchers used Fluent Editor 2014 to create an ontology of risks in agricultural production using the example of hops cultivation, facilitated by Fluent Editor’s powerful Controlled Natural Language (CNL) feature.

The Challenge

Farms are often deprived of the opportunity to adapt to rapidly changing economic and growing conditions. For this reason, it is extremely important to correctly interpret the relationship between natural conditions, markets, and farm production in order to best allocate resources and achieve the best economic results. In order to provide the most comprehensive risk management system, researchers from Maria Curie Skłodowska University were faced with the challenge of transforming into structured data the vast amount of data concerning agricultural production and the factors that influence it. With data scattered across the web and various local databases, researchers needed to semantically analyze the data by identifying entities, parsing the data, extracting the core information, and classifying the results.

The Solution

Researchers from Maria Curie Skłodowska University used Fluent Editor 2014, a comprehensive tool developed by Cognitum for creating, editing and manipulating complex ontologies in Controlled Natural Language, to create an ontology of risks that may occur during the process of commercial hops cultivation. This approach dramatically simplifies access to the data, making it significantly more accessible and valuable for the end user. Here is a small example of ontology risk and the relationship between different factors in the process of hops cultivation.

The researchers at the University were impressed with the results. “The use of a Controlled Natural Language (CNL) in the ontology creation seems to be a solution that can significantly simplify and speed up the creation of both the ontology and knowledge systems,” says Dariusz Dobrowolski, researcher at Maria Curie Skłodowska University. “The results show that the use of Fluent Editor 2014 with CNL is a solution that, in the near future, may help to create and manipulate large ontologies and thereby will allow for easier access and exchange of knowledge.”

Thanks to this, in the near future, we will be able to create a comprehensive and very powerful database that will warn the end user, in this case, the farmer, about the possibility of risks under specific conditions, for example, temperatures higher than [the specified value] + humidity higher than [the specified value] + diseases.



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