Comments (4)
A PyData 2019 presentation by Suyog S Swami has described one such approach.
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@edanweis Have a look at the enhancement issue #70 - similar ideas I think
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Tread carefully
there are several major pitfalls hidden in machine learning when applied to this kind of propleam.
- Current ML is just statistical guessing and will make a guess even when the input does not have any logical meaning
- The returned guess will change over time if you use a ML system that evolves with time and changes to the base Graql language. So every change to Graql or update to the training set will be a breaking change.
- Minor differences, even the placement of a comma, are likely make for generated output queries that exhibit wildly different befavaries and thus results.
- Since the query generated is the result of a statistical guess and not a logical analyst of the english language input debugging the english input becomes impossible because changes to the logic of the input will not map to changes in the logic of the output query only to some unknowable statistical relationship between the actual input and the training input/output pairs.
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Depends on the use case I suppose, if one were to write a chatbot engine it might be useful.
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Related Issues (20)
- Ignoring repeated constraints
- Support modifiers (at least the limit) in all type of queries HOT 3
- Using standard form for numeric literals throws an error when parsed HOT 5
- [Feature Request] More operators and basic functions HOT 1
- Make annotated constraints to apply to subtypes HOT 4
- Allow negations and disjunctions without an exterior binding
- Unary negation
- Factor out typedb-common and typedb-proc-macro crates
- Sanitize rust macros to avoid forcing user's environment
- Give relation and value constraints explicit keywords
- Add deployment tests for Cargo and Pip snapshots
- Introduce a syntax element to explicitly end a query string
- Plays-as overrides break schema export
- Fetch: add a way to project an item into a list of attributes by label HOT 1
- Introduce syntax for interface covariance HOT 2
- Composition of interface implementations as high-level abstractions (traits)
- Allow casting of type variables to value variables HOT 3
- Ability to define metadata for types without replicating the information across all type instances
- Simplify attribute deletes by using anonymous bindings
- Allow matching on types using a type label directly
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