Machine learning has the potential to greatly improve the development and performance of knowledge-based systems. By enabling these systems to learn from data and reason about complex situations, machine learning can help to create intelligent systems that are capable of solving a wide range of problems.
Machine learning techniques can be used to develop knowledge-based systems in several ways. One way is to use machine learning algorithms to mine knowledge from large datasets. For example, a knowledge-based system designed to diagnose medical conditions could be trained on a dataset of medical records using machine learning algorithms to learn the relationships between symptoms and diagnoses.
Another way that machine learning can be used in knowledge-based systems is to develop models that can reason about complex situations. These models can be trained using machine learning algorithms on large amounts of data, allowing them to learn patterns and relationships that would be difficult for a human to identify.
Reinforcement learning is another technique that can be used to develop knowledge-based systems. In reinforcement learning, the system is trained by interacting with an environment and receiving feedback in the form of rewards or punishments. This allows the system to learn how to make decisions and take actions based on the information it receives
Papers may address a range of topics related to the application of machine learning in knowledge-based systems, including but not limited to:
- Novel algorithms for learning from large domain or specific knowledge.
- Systems frameworks for integrating learnable components into knowledge-based systems
- Techniques for evaluating the effectiveness of machine learning in knowledge-based systems.
- Applications of machine learning to new practical problems in knowledge-based systems.
- Applications of machine learning to analyze large amounts of data, including social media.
Special Session Chairs
- Asdrúbal López-Chau
alchau@uaemex.mx
Universidad Autónoma del Estado de México, Mexico - David Valle-Cruz
davacr@uaemex.mx
Universidad Autónoma del Estado de México, Mexico