- Data acquisition
- Ground truth acquisition
- Cross validation technique
- Query type
- Scoring metric
- Significance test
Relational evaluation techniques in artificial intelligence typically involve assessing the quality and performance of relational databases or knowledge bases. The components of relational evaluation techniques include:
- Accuracy: This measures how close the output of the system is to the true or expected values. In the context of relational databases, it might refer to the correctness of the data retrieved or the accuracy of query results.
- Completeness: This assesses whether the system provides all the necessary information required for a given task. In relational databases, it could refer to whether all relevant data is retrieved or if any important information is missing.
- Consistency: This evaluates the uniformity and coherence of the data in the database. It ensures that there are no conflicting or contradictory pieces of information within the database.
- Efficiency: This measures the system’s performance in terms of resource usage such as time and memory. In relational databases, it could refer to the speed of query execution or the scalability of the system.
- Robustness: This refers to the system’s ability to handle unexpected inputs or situations gracefully. It involves testing the system under various conditions to ensure it performs reliably.
- Scalability: This assesses how well the system can handle increasing amounts of data or users without significantly impacting performance. In relational databases, scalability refers to the ability to efficiently store and retrieve data as the database grows.
- Usability: This evaluates how easy it is for users to interact with the system. In the context of relational databases, usability could involve the clarity of query languages, the intuitiveness of user interfaces, and the availability of helpful documentation.
When answering an interview question about the components of relational evaluation techniques, it’s essential to provide a comprehensive overview of these key aspects and perhaps elaborate on how they apply specifically to relational databases or knowledge bases within the realm of artificial intelligence.