Using Meaning as Universal Knowledge Representation
Meaning is core to language because the meaning of a sentence determines the forms of words and phrases that are selected and vice versa. Or as I say: Form follows meaning®. But what is meaning?
In language, the word forms that we use to communicate with others follow the meaning of what we want to say and, just as importantly, the meaning of what we say is far deeper than the words we can use to say it. Therefore, meaning needs to be at the core of our language understanding systems, not word forms.
What is missing from data science today is meaning. When one data project completes another can begin immediately because the slice of meaning selected in data is always too narrow, but meaning allows ongoing generalization because it is rich content, not just labeled content. There can be many thousands of relations to a referent in a meaning layer, but data annotations may only capture a single feature for a particular purpose and unsupervised data is limited to the content of the source files. In contrast, meaning can continually add to the richness of the representation. That difference between data and meaning is a key differentiator intended for exploration in this article.
