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Artificial intelligence is a cutting-edge technology widely used in a variety of spheres nowadays. To properly understand and use the data encoded in semantic web documents, AI requires a special component – an inference engine.
What Is an Inference Engine in Artificial Intelligence?
It’s a system that acknowledges graphs or bases to figure out new facts rules and relationships applying the new rules. The inference engine can use the pattern of deductive learning in artificial intelligence, and then knowing that Rome is in Italy, conclude that any entity in Rome is in Italy. It can also follow the way of inductive reasoning in artificial intelligence. A vivid example of it may be the fact that every technology company with more than a hundred employees tends to have a CTO, thus any enterprise meeting this criterion without a CTO must be missing a record.
Initially, the inference engine in the expert system had to become a component that emulates problem-solving by a human expert in a certain domain.
Some AI inference engines are called reasoners, they are software apps deriving new facts, insights, logical conclusions, and associations from the information that already exists based on a certain model. Inference and inference rules allow to add new pieces of knowledge based on previously known data.
Key Methods Employed within Inference Engines
There are two major methods that are utilized by inference engines to gather new knowledge:
- backward chaining
- forward chaining reasoning
Backward chaining in artificial intelligence begins with a hypotheses list and analyzes data backward to see if the data plugged into the rules match these hypotheses. Thus, the facts that support a hypothesis are highlighted.
Forward chaining begins with the data available, analyzes it, and infer new details and facts based on a specific rule.
Both approaches are based on deductive reasoning in artificial intelligence that if A implies that B is true and A is true, then B must also be true.
Forward chaining in artificial intelligence can be demonstrated through an example of the rule like this:
Rule1: Cat(x) => Mammal(x)
We state that all cats are mammals, and then we can take every breed separately and a fact will be created. For instance, A Maine Coon is a mammal.
A backward chaining example in AI can be demonstrated through a reverse process, however, the inference engine should be aided by an interface for a human.
Let’s look at the rule like above from the other side, going from the hypothesis that should be checked “A Maine Coon is a mammal”. To prove it, the backward chaining reasoning needs to highlight the assertion through inquiring a human if Maine Coon is a cat. In case the answer is positive, the hypothesis “a Maine Coon is a mammal” would be validated.
In a rule based inference engine, the simplest actions taken go through 3 stages: matching, selecting, and executing rules.
- Match rules is a process when all the rules are found and triggered by the contents of a knowledge base.
- Select rules is an action discerning the order of the rules that should be applied.
- Execute rules is applying rules to the knowledge ере exists with the help of either forward or backward chaining.
Once the execute rules process is completed, the match rules procedure is restarted until all the opportunities for either forward or backward chaining deductions are exhausted.
Inference engines are mighty components of a knowledge-based system in artificial intelligence, however, any knowledge base needs data.
To get the necessary data for processing, you can scrape it from the relevant online sources yourself or order data delivery services. That’s where DataOx offers you a professional helping hand, schedule a free consultation with our expert , and discuss the opportunities.