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Dili Institute of Technology
Rua Hudi Laran, Dili, Timor-Leste
Computer Science Department, 2nd Floor
Have questions about KR concepts? Need help understanding semantic networks, logic-based representation, or ontologies? Our team is ready to assist you.
Questions about building and structuring knowledge bases for AI systems? We can guide you through the process.
Need help visualizing relationships between concepts? Our team can explain semantic networks with practical examples.
Curious about how computers reason with knowledge? We cover forward chaining, backward chaining, and more.
Questions about rule-based systems and expert system development? We're here to help.
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Knowledge Representation (KR) is a field of AI focused on encoding human knowledge in a form that computers can process and reason with. It involves techniques like logic, semantic networks, frames, and ontologies to represent facts, concepts, and relationships.
KR is crucial because it provides the structure and meaning that transforms raw data into actionable intelligence. Without KR, computers can store data but cannot understand or reason with it. KR enables applications like expert systems, knowledge graphs, and semantic search.
Data is raw, unprocessed facts (e.g., "35", "Jakarta"). Information is processed data with context (e.g., "Temperature in Jakarta is 35°C"). Knowledge is information combined with understanding and relationships (e.g., "35°C in Jakarta means it's hot, so people should stay hydrated"). KR captures this deeper understanding.
Ontologies are formal, explicit specifications of shared conceptualizations. They define concepts, relationships, and constraints within a domain. For example, a medical ontology defines concepts like "Disease," "Symptom," and "Treatment," and relationships like "treats" and "has-symptom." Ontologies enable knowledge sharing and reuse.
Knowledge graphs organize information as nodes (entities) and edges (relationships). Google's Knowledge Graph, for example, connects entities like "Leonardo DiCaprio" to "Titanic" through the "acted-in" relationship. This enables semantic search—when you search for "Leonardo DiCaprio movies," the graph understands the relationship and returns relevant results.
An inference engine is a component of an AI system that applies logical rules to a knowledge base to derive new knowledge. It uses methods like forward chaining (starting from facts) and backward chaining (starting from goals) to reason and draw conclusions, enabling expert systems to make decisions.
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