Text Mining projects in the Agri-Food sector
Can you text mine agricultural content?
“Absolutely!” is the answer that AgroKnow will give you. And they can prove it! AgroKnow is one of the partners in the OpenMinTeD projects who are already very active in projects which apply text mining technologies to the agricultural sector.
They do this as part of the TETIS team (Territoires, Environnement, Télédetection et Information Spatiale or Land, environment, remote sensing and spatial information in English). Other actors in this team are:
- AgroParisTech (Institut des sciences et industries du vivant et de l’environnement)
- Irstea (National Research Institute of Science and Technology for Environment and Agriculture)
- Cirad (the International Cooperation Centre for Agricultural Development Research)
Together, the TETIS team has been working on a number of AgroNLP projects (Natural Language Processing applied to agricultural domain), to address the different challenges faced by stakeholders in the agri-food sector. A number of interesting research projects have been set up, including:
- Animal Disease Surveillance: The project proposes a new methodology in the domain of epidemic intelligence in animal health in order to discover knowledge in web documents dealing with animal disease outbreaks. To address this issue, a global process based on Information Retrieval (IR) and Information Extraction (IE) approaches is proposed. This is working on the same direction as our Foodakai project.
- Terminology Extraction for Document Matching and Open Data in Agricultural Domain: The project investigates the use and combination of Text Mining methodologies to highlight and publish in Open Data systems the most appropriate terms extracted with BioTex (both in French and in English). In addition, these terms are used to match heterogeneous data of agricultural domain.
- Information Extraction from Experimental Data of Agricultural Domain: This work seems to be the most technical of all, as it deals with knowledge engineering issues of experimental data that are extracted from scientific articles, in order for them to be reused in decision support systems. The work is based on Natural Language Processing (NLP) together with data mining approaches guided by the domain Ontological and Terminological Resource (OTR).
- BIg Data for Agriculture and biodiversitY (BIRTHDAY): This project aims at providing new efficient decision making tools for helping agricultural development as well as biodiversity protection in Peru. More specifically, it aims at developing a new platform for helping to acquire new data, to share data, to extract knowledge, and to share useful information and knowledge among different actors that are involved in agriculture or biodiversity domains in Peru. We were happy to see Sophia Ananiadou from the University of Manchester/NACTEM Director (also an OpenMinTeD consortium member) among the researchers working on this project.
You can find more information about these projects on the TETIS Unit website.
This blog post was written by Vassilis Protonotarios, Senior Project Manager at AgroKnow.