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Is there a downside to relying too heavily on ontology in natural language processing?

Silvia Kinastan

Yes, there is definitely a downside to relying too heavily on ontology in natural language processing.

First of all, it's important to understand what ontology is in this context. Essentially, ontology is a system of categorizing and defining concepts in a given domain. For example, an ontology of a restaurant might include categories like "appetizers," "entrees," and "desserts." These categories are defined by certain properties (e.g. "appetizers" are usually smaller portions of food meant to be eaten before the main course) and relationships (e.g. "appetizers" are a type of food served at a restaurant).

So, how does this relate to natural language processing? Well, one of the challenges of natural language processing is that human language is often ambiguous and context-dependent. For example, the word "duck" could refer to a bird, a verb meaning to lower oneself, or a type of fabric. In order to process language accurately, NLP systems need to be able to disambiguate these different meanings and understand the context in which a word is being used.

Ontology can be useful as a tool for NLP systems to disambiguate language. If an NLP system knows that a certain word is associated with a certain category in an ontology, it can use that information to help determine the most likely meaning of the word in a given context. For example, if the system knows that "duck" is a type of bird, it might be able to correctly identify that in the sentence "I saw a duck flying overhead."

However, there are several downsides to relying too heavily on ontology in NLP:

1. Limited coverage: No matter how comprehensive an ontology is, it will always be limited in its coverage of the vast range of concepts and contexts that humans can express in language. There will inevitably be situations where a word or phrase doesn't fit neatly into any category in the ontology, or where two different words might belong to the same category. In these situations, relying too heavily on ontology can actually hinder accuracy rather than help it.

2. Over-reliance on context: While context is certainly important in disambiguating language, relying too heavily on it can create problems. For example, if an NLP system relies too heavily on the context of a single sentence to determine the meaning of a word, it might miss important nuances or connotations that are only apparent from a wider perspective. This can lead to errors in interpretation.

3. Difficulty in updating: Ontologies can be difficult to update and maintain over time. As new concepts and domains emerge, the ontology will need to be updated accordingly. This can be a time-consuming process, and there is always the risk that the ontology will become outdated or inaccurate.

4. Bias: Finally, ontologies can be biased in ways that reflect the biases of their creators. For example, an ontology of occupations might have categories like "doctor" and "lawyer," but might not include categories for less prestigious jobs like "janitor" or "cashier." If an NLP system relies too heavily on this ontology, it could perpetuate these biases in its language processing.

Overall, while ontology can be a useful tool in natural language processing, it's important not to rely too heavily on it. Instead, NLP systems should use ontology in conjunction with other techniques, such as machine learning algorithms and statistical models, to achieve the most accurate and nuanced results possible.