Natural language interface to web api

A system comprising:

• one or more hardware processors;
• transmission device for providing canonical representation information to at least one annotator;
• and
• at least one machine-readable storage medium having instructions stored thereon, the instructions when executed by the one or more hardware processors, cause the one or more hardware processors to:
• identify the syntax and context of application program interface (API) commands using a schema for the API;
• generate canonical commands for the API commands using a generalized grammar;
• generate a semantic mesh from the canonical commands;
• use a hierarchical probabilistic model with the semantic mesh to identify a subset of the canonical commands for labeling;
• provide the subset of the canonical commands for labeling;
• receive labeling information for the subset of the canonical commands;
• and
• update the API based on the received labeling information.

A computer implemented method comprising:

• identifying a syntax and context of application program interface (API) commands using a schema for the API;
• generating canonical commands for the API commands using a generalized grammar;
• generating a semantic mesh from the canonical commands, wherein the semantic mesh comprises a hierarchical structure of nodes representing a compositional relationship of the canonical commands;
• using a hierarchical probabilistic model with the semantic mesh to identify a subset of the canonical commands for annotation;
• providing the subset of the canonical commands for labeling;
• receiving labeling information for the subset of the canonical commands;
• and
• updating the API based on the received labeling information.

A machine readable medium having instructions stored thereon, the instructions when executed on a machine cause the machine to:

• identify the syntax and context of application program interface (API) commands using a schema for the API;
• generate canonical commands for the API commands using a generalized grammar;
• generate a semantic mesh from the canonical commands;
• identify a subset of the canonical commands for annotation using a hierarchical probabilistic model with the semantic mesh;
• provide the subset of the canonical commands for labeling;
• receive labeling information for the subset of the canonical commands;
• and
• update the API based on the received labeling information.