How close to you have to get to natural language to do the search?
I’ve wondered whether a similar system could check legal systems for contradictions—probably a harder problem, but not as hard as full natural language.
Most of the knowledge used, is in its ontology. It doesn’t try to parse sentences with categories like {noun, verb, adverb}; it uses categories like {drug, disease, chemical, gene, surgery, physical therapy}. It doesn’t categorize verbs as {transitive, intransitive, etc.}; it categorizes verbs as eg {increases, decreases, is-a-symptom-of}. When you build a grammar (by hand) out of word categories that are this specific, it makes most NLP problems disappear.
ADDED: It isn’t really a grammar, either—it grabs onto the most-distinctive simple pattern first, which might be the phrase “is present in”, and then says, “Somewhere to the left I’ll probably find a symptom, and somewhere to the right I’ll probably find a disease”, and then goes looking for those things, mostly ignoring the words in-between.
I don’t know what you mean by ‘ontology’. I thought it meant the study of reality.
I can believe that the language in scientific research (especially if you limit the fields) is simplified enough for the sort of thing you describe to work.
In computer science and information science, an ontology is a formal representation of knowledge as a set of concepts within a domain, and the relationships between those concepts. It is used to reason about the entities within that domain, and may be used to describe the domain.
I don’t know what you mean by ‘ontology’. I thought it meant the study of reality.
“In computer science and information science, an ontology) is a formal representation of knowledge as a set of concepts within a domain, and the relationships between those concepts. It is used to reason about the entities within that domain, and may be used to describe the domain.”
How close to you have to get to natural language to do the search?
I’ve wondered whether a similar system could check legal systems for contradictions—probably a harder problem, but not as hard as full natural language.
Most of the knowledge used, is in its ontology. It doesn’t try to parse sentences with categories like {noun, verb, adverb}; it uses categories like {drug, disease, chemical, gene, surgery, physical therapy}. It doesn’t categorize verbs as {transitive, intransitive, etc.}; it categorizes verbs as eg {increases, decreases, is-a-symptom-of}. When you build a grammar (by hand) out of word categories that are this specific, it makes most NLP problems disappear.
ADDED: It isn’t really a grammar, either—it grabs onto the most-distinctive simple pattern first, which might be the phrase “is present in”, and then says, “Somewhere to the left I’ll probably find a symptom, and somewhere to the right I’ll probably find a disease”, and then goes looking for those things, mostly ignoring the words in-between.
I don’t know what you mean by ‘ontology’. I thought it meant the study of reality.
I can believe that the language in scientific research (especially if you limit the fields) is simplified enough for the sort of thing you describe to work.
See: http://en.wikipedia.org/wiki/Ontology_(information_science)
“In computer science and information science, an ontology) is a formal representation of knowledge as a set of concepts within a domain, and the relationships between those concepts. It is used to reason about the entities within that domain, and may be used to describe the domain.”