A Semantic Reasoner is a key tool in AI that interprets and makes sense of data based on rules and knowledge about the world. It helps the system understand content beyond the literal meaning and link it to a wider context, similar to how humans make sense of content using their general knowledge.
Imagine you’re reading a story and come across words and sentences you don’t know. But because you know other parts of the story, you can guess what those unknown parts mean. That’s what a Semantic Reasoner does in an AI system. It’s a part that uses the other knowledge it has to understand and make guesses about things it doesn’t directly know.
A Semantic Reasoner, often tied to the field of knowledge representation and reasoning in AI, involves understanding data based its semantic information and context. The core objective of the Semantic Reasoner is to infer logic or derive new knowledge based on the existing information and rules.
At its core, it relies on a knowledge base—usually represented with ontologies or description logics—which stores facts about the world, and a set of inference rules. These rules guide the process of deriving new knowledge. This new knowledge isn’t necessarily present explicitly in the knowledge base, but is logically implied based on the pre-existing knowledge.
Semantic Reasoners are diverse in their architecture and approach to reasoning, ranging from rule-based and tableaux algorithms to probabilistic and hybrid reasoners. What unites these different approaches is the end goal, which is essentially to enhance the system’s understanding and interpretation of the data at a level equivalent or close to human intelligence.
A use case for Semantic Reasoners is in Natural Language Processing (NLP), where they help in understanding and interpreting human language at a semantic level. In Semantic Web technologies, Semantic Reasoner is employed to infer logical conclusions from a set of asserted facts or axioms.
However, Semantic Reasoner systems are not always perfect and suffer from few limitations. One such limitation is computational inefficiency—semantic reasoning might be slow when dealing with a large amount of data or complex logic. Additionally, semantic reasoners need high-quality datasets and well-defined inference rules to perform well.