Google’s VP research, Alfred Spector, reveals plans on exploiting huge amount of data for building a “database of concepts and relationships between them” for better search results. He envisions that Google should be able to learn such information from interactions and a very large amount of information, a different approach to a traditional AI where such an ontology is usually imposed to a system and controlled by an expert.
The use of such ontology is then pretty obvious:
Let’s imagine our search software is responding to a query on pets, but we find articles on dogs and cats, but without the word pets. This database of relationships would let Google know that the article is probably about pets because there are multiple instances of a subcategory of “pet.” The database would enable much better search and better language translation because there’d be a better understanding of the meaning of the words.
From this interview it is clear that Google recognises importance of semantics for search, however, a major challenge is in a construction of ontologies describing such a huge and a dynamic environment on which intelligent search would reliably operate.