Dynamic faceted search on a document corpus

1. A computer-implemented query-focused faceted structure generation method for generating a query-focused faceted structure from a taxonomy for searching a document collection, the method comprising:

ingesting a document corpus including a pre-processing that filters parts of speech;

generating a vector space representation of a query and instances from a taxonomy of the document corpus via at least two models, the taxonomy being loaded and including a graph of a type and instance nodes where the instance nodes have a consistent relationship to the type; and

producing a dynamic structure of a relevant category and facet using a two-vector space representation from the generated vector space representation based on a separate two-vector space representation of the at least two models,

wherein the ingesting ingests the document corpus by:

extracting the terminology that includes noun words and phrases from the document corpus to:

train a type model that generates a phrase embedding of the terminology in the document corpus; and

train a topic model that generates a second phrase embedding of the terminology in the document corpus,

wherein the generating generates a vector for a user query as a weighted combination of the vector for each query token in the topic model as a query vector,

wherein the generating generates a list of the vectors for instances from the taxonomy in the topic model, and

wherein the producing produces the dynamic structure of the relevant category and the facet by:

selecting a first parameter of nearest neighbor instances to the query vector from the taxonomy instances using the topic model as query-similar instances;

selecting a second parameter of types in the taxonomy with a most number of query-similar instances to use as categories;

selecting a third parameter of facets from instances of the types corresponding to each of the categories for the second parameter; and

expanding from the third parameter of the facets within each of the second parameter of the categories to obtain more category-similar instances from the document corpus using the type model.

2. The method of claim 1 , further comprising returning the dynamic structure as a data file to a user.

3. The method of claim 1 , wherein the facets are ranked within each of the first parameter of the categories by distance to both:

the query vector in the topic model vector space, and

a centroid of the third parameter of instances that correspond to the category.

4. The method of claim 1 , embodied in a cloud-computing environment.

5. A computer program product for query-focused faceted structure generation, the computer program product comprising a computer-readable storage medium having program instructions embodied therewith for generating a query-focused faceted structure from a taxonomy for searching a document collection, the program instructions executable by a computer to cause the computer to perform:

ingesting a document corpus including a pre-processing that filters parts of speech;

generating a vector space representation of a query and instances from a taxonomy of the document corpus via at least two models, the taxonomy being loaded and including a graph of a type and instance nodes where the instance nodes have a consistent relationship to the type; and

producing a dynamic structure of a relevant category and facet using a two-vector space representation from the generated vector space representation based on a separate two-vector space representation of the at least two models,

wherein the ingesting ingests the document corpus by:

extracting the terminology that includes noun words and phrases from the document corpus to:

train a type model that generates a phrase embedding of the terminology in the document corpus; and

train a topic model that generates a second phrase embedding of the terminology in the document corpus,

wherein the generating generates a vector for a user query as a weighted combination of the vector for each query token in the topic model as a query vector,

wherein the generating generates a list of the vectors for instances from the taxonomy in the topic model, and

wherein the producing produces the dynamic structure of the relevant category and the facet by:

selecting a first parameter of nearest neighbor instances to the query vector from the taxonomy instances using the topic model as query-similar instances;

selecting a second parameter of types in the taxonomy with a most number of query-similar instances to use as categories;

selecting a third parameter of facets from instances of the types corresponding to each of the categories for the second parameter; and

expanding from the third parameter of the facets within each of the second parameter of the categories to obtain more category-similar instances from the document corpus using the type model.

6. The computer program product of claim 5 , further comprising returning the dynamic structure as a data file to a user.

7. The computer program product of claim 5 , wherein the facets are ranked within each of the first parameter of the categories by distance to both:

the query vector in the topic model vector space, and

a centroid of the third parameter of instances that correspond to the category.

8. A query-focused faceted structure generation system for generating a query-focused faceted structure from a taxonomy for searching a document collection, the system comprising:

a processor; and

a memory, the memory storing instructions to cause the processor to perform:

ingesting a document corpus including a pre-processing that filters parts of speech;

generating a vector space representation of a query and instances from a taxonomy of the document corpus via at least two models, the taxonomy being loaded and including a graph of a type and instance nodes where the instance nodes have a consistent relationship to the type; and

producing a dynamic structure of a relevant category and facet using a two-vector space representation from the generated vector space representation based on a separate two-vector space representation of the at least two models,

wherein the ingesting ingests the document corpus by:

extracting the terminology that includes noun words and phrases from the document corpus to:

train a type model that generates a phrase embedding of the terminology in the document corpus; and

train a topic model that generates a second phrase embedding of the terminology in the document corpus,

wherein the generating generates a vector for a user query as a weighted combination of the vector for each query token in the topic model as a query vector,

wherein the generating generates a list of the vectors for instances from the taxonomy in the topic model, and

wherein the producing produces the dynamic structure of the relevant category and the facet by:

selecting a first parameter of nearest neighbor instances to the query vector from the taxonomy instances using the topic model as query-similar instances;

selecting a second parameter of types in the taxonomy with a most number of query-similar instances to use as categories;

selecting a third parameter of facets from instances of the types corresponding to each of the categories for the second parameter; and

expanding from the third parameter of the facets within each of the second parameter of the categories to obtain more category-similar instances from the document corpus using the type model.

9. The system of claim 8 , further comprising returning the dynamic structure as a data file to a user.

10. The system of claim 8 , embodied in a cloud-computing environment.