Agriculture Knowledge Graph: NLP Question Answering System for Agribusinesses

From creating Artificial Intelligence solutions to Digital Transformation, businesses accumulate 2.5 Quintillion Bytes of data each day. This accumulated data is used for the benefit of businesses in making business strategies and planning. But due to the plethora of data, businesses require massive storage systems and data engineers to filter useful data and discard the rest. In addition, high-level executives are dependent on technical staff to understand data dimensionality and extract the data patterns.

Here’s the need for a knowledge graph that comes into play. In this blog, we will discuss what a knowledge graph is and its benefit to the agriculture sector in the future.

What is Knowledge Graph

A knowledge graph is a semantic network that illustrates the connection of interrelated information or real-world entities to draw relationships and convey meaning within data in a better way. This information is then stored in a graph database and can be visualized as a “Graph” called a “Knowledge Graph”.

In a nutshell, a knowledge graph is a relationship between similar nodes or entities that help in better decision-making without any dependence on any resource. Besides, natural language query is used as an interface for non-technicals that bridges the gap where non-technical can interact with a technical product without any haze. The question answering NLQ (Natural Language Query) system is used to ease executives to ask questions in plain English language.

The AI-based question-answering system can be designed on top of Agriculture Knowledge Graphs in which data is semantically connected. This will lead to users for quick data analysis, information mining, and decision-making on the basis of information.

Benefits of Knowledge Graph to Agriculture

Knowledge graphs help companies to discover all possibilities via interconnected data points. It consists of nodes, edges, and data labels to describe a graph in a more sophisticated way. A node could be a place, an entity, a company like Odyssey Analytics, or a Group of companies like Odyssey Group. On the other hand, an edge describes the relationship between two entities. Similarly, the relationship between a client and Odyssey Analytics.

The ideology of knowledge graph was brought into reality as an abstraction of knowledge for high-level executives so that they can easily get insights through natural language commands. The knowledge graph can leverage agriculture in the following ways.

Semantic Data Connecting

Enable companies to integrate data from multiple resources within minutes instead of making the agriculture data integration process complex for stakeholders.

Faster Data Querying

As end-users required a specific understanding of query languages to fetch information such as SPARQL. So, to make the massive amount of data available to end users such as Farmer and Agribusiness owners. Natural language helps in fetching data faster than it was.

Data Presentation as KG

Knowledge graphs help organize, structure, and integrate large amounts of data from various sources, making it easier to find and access relevant information with Q&A as natural language commands.

Predictive Analytics

By analyzing the relationships between various data points, knowledge graphs enable identifying patterns and making predictions about future trends & outcomes, enabling farmers to proactively respond to challenges and new prospects.

Better Resource Allocation

By providing a comprehensive view of resources such as land, water, and labor, knowledge graphs can help farmers optimize their resource allocation and make more efficient use of available resources.

Improved Decision-Making

By connecting related data, knowledge graphs can provide a more complete and accurate view of a situation, enabling farmers and agricultural organizations to make informed decisions based on a better understanding of the environment, crops, pests, and more.

Intelligent Search

Knowledge graphs make it easier to share information and knowledge across organizations, communities, and regions, leading to better collaboration and increased efficiency in agricultural practices. By using natural language commands farmers can sell their crops at a decent return.

A natural language query system takes queries (Questions) in plain English and converts them into technical language or SPARQL for faster data retrieval from a knowledge graph.

As the knowledge graph requires a sound familiarity with complex query languages such as SPARQL/SQL to retrieve data. This drawback leads end-users to include a natural language question and answering system that is used to ease non-technicals to play with it and use it for their agriculture business success.

NLP Question Answering System For Agribusinesses

The question answering system in natural language processing is a challenge to machine learning engineers because it takes end-user queries as natural language and responds to them accordingly by transforming simple text into machine-readable instructions.

An agriculture knowledge graph has multiple use cases in the Agri Sector. Above all, let’s discuss a weather forecasting use case where a massive amount of weather data is reshaped into a weather knowledge graph. For laymen, it’s hard to interact with databases and traditional excel files to extract the relevant information from the data in the form of visual graphs (Agri Trends, Weather Trends) text, or tables.

To leverage end-users, NLQ (Natural Language Question Answering Systems) are deployed over a knowledge graph where a layman can ask their problem in the form of a natural language question and get more accurate answers with quick retrieval. The automated question and answering-system design can be represented as below:

Odyssey Analytics: Let’s Portray Your Data As a Knowledge Graph

Odyssey Analytics is an Artificial Intelligence and Machine Learning solution provider and consultant company in the United States working on cutting-edge technology for the well-being of mankind. We aim to help agriculture businesses to design intelligent infrastructure for farmers to sell crops at the best prices. In addition, agribusinesses can gain insights on the availability of farmers who are selling their crops in a specific region, price or category.

We aim to bridge the gap between farmers and agribusinesses to buy or sell Agri products at the best prices. Enabling agribusinesses to forecast their demands in the future and make decisions accordingly. Agriculture knowledge graphs enable companies to combine their scattered data to get semantic relationships among data points. It leverage stakeholders in better decision making.


Today, a lot of companies are transitioning towards knowledge graphs to represent data more semantically and retrieve it more efficiently than before. The agriculture knowledge graph is a graphical illustration of Agridata that enables Agribusinesses to quickly and uniformly retrieve data.

Besides, it requires technical knowledge to retrieve data from knowledge graphs, therefore, natural language-based questions and answering systems are deployed on the top of the knowledge graph. That leverage laymen like farmers, or nontechnical business executives to easily interact with data using natural language questions without having knowledge of technical terms like SQL, NoSQL, or SPARQL.