Natural Language Understanding (NLU) is important to drive smart machines and autonomous systems. Hence, it is critical to take the right NLU approach to optimize outcomes and decision-making. However, for NLU programs to succeed, they need a deep understanding of language structure and word classification. This is why it is essential to understand how ontology works with NLU.
Language understanding in NLU programs is complex because of ambiguity. For example, taxonomy can classify a chicken as an animal. In contrast, ontology can apply relational understanding to distinguish the term chicken as either an animal or a type of food.
Furthermore, ontology disambiguates language by defining word relationships and provides a precise understanding of the natural language so businesses can take the next step in their NLU journey. Ontologies make language processing and analysis more efficient and accurate, so artificial intelligence technologies can apply advanced linguistic capabilities. The benefits of ontology on NLU include:
• Improved entity analysis
• Increased use, reuse, and maintainability of domain information
• Domain knowledge sharing
• Scaling ontology with artificial intelligence
From these examples, it is easy to see how ontology help disambiguate language so NLU programs perform more accurately.
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