Extracting, deriving, and using legal matter semantics to generate e-discovery queries in an e-discovery system

1. A computer-implemented method for generating e-discovery queries for an e-discovery system comprising:

iteratively building a semantic knowledge graph during a build phase by,

receiving meet and confer document instances, legal matter types, historical e-discovery queries for different legal matters, and legal semantic types extracted from the historical e-discovery queries;

adding the legal semantic types to the semantic knowledge graph;

in response to adding the legal semantic types to the semantic knowledge graph, identifying a list of terms that serve as a basis of an initial query; and

generating an e-discovery query for an e-discovery system by:

1) mapping a predicate clause of the initial query to a legal semantic type by:

identifying a metadata field and operator combination;

determining the legal semantic type from a glossary;

adding a first semantic type node to the semantic knowledge graph;

connecting the first semantic type node to a legal matter node with an edge weight; and

adding an expression term node for the metadata field with another edge weight; and

2) mapping a keyword of a text clause of the initial query to a legal semantic type by:

running a partial query for the keyword against a saved result set to generate a new result set; and

for each document in the new result set,

retrieving surrounding text around a location within the document where the keyword was found;

applying Named Entity Recognition (NER) to the keyword using surrounding text as context;

adding a second semantic type node for an entity type node;

connecting the second semantic type node to the legal matter node with an edge weight; and

adding an expression term node for the keyword with another edge weight; and

modifying the e-discovery query using the semantic knowledge graph and additional input during a query generation phase by:

receiving a legal matter type and meet and confer information;

issuing a series of questions to obtain the legal semantic types that are relevant to the legal matter type and the meet and confer information, wherein a priority of an order of questions is determined by weight associated with occurrence of those legal semantic types in the semantic knowledge graph;

modifying the e-discovery query based on the obtained legal semantic types to add one or more predicates that capture historical information based on user feedback responding to the series of questions;

providing the modified e-discovery query as a suggested query; and

in response to receiving selection of the modified e-discovery query, executing the modified e-discovery query.

2. The computer-implemented method of claim 1 , wherein the meet and confer document instances are any of digital instances and physical instances.

3. The computer-implemented method of claim 1 , further comprising:

providing a map for metadata fields and operator combinations.

4. The computer-implemented method of claim 1 , further comprising:

providing a text index for documents.

5. The computer-implemented method of claim 1 , further comprising:

receiving feedback on the modified e-discovery query to improve the semantic knowledge graph.

6. The computer-implemented method of claim 1 , wherein a Software as a Service (SaaS) is configured to perform method operations.

7. A computer program product for generating e-discovery queries for an e-discovery system, the computer program product comprising a computer readable storage medium having program code embodied therewith, the program code executable by at least one processor to perform:

iteratively building a semantic knowledge graph during a build phase by,

receiving meet and confer document instances, legal matter types, historical e-discovery queries for different legal matters, and legal semantic types extracted from the historical e-discovery queries;

adding the legal semantic types to the semantic knowledge graph;

in response to adding the legal semantic types to the semantic knowledge graph, identifying a list of terms that serve as a basis of an initial query; and

generating an e-discovery query to for e-discovery system by:

1) mapping a predicate clause of the initial query to a legal semantic type by:

identifying a metadata field and operator combination;

determining the legal semantic type from a glossary;

adding a first semantic type node to the semantic knowledge graph;

connecting the first semantic type node to a legal matter node with an edge weight; and

adding an expression term node for the metadata field with another edge weight; and

2) mapping a keyword of a text clause of the initial query to a legal semantic type by:

running a partial query for the keyword against a saved result set to generate a new result set; and

for each document in the new result set,

retrieving surrounding text around a location within the document where the keyword was found;

applying Named Entity Recognition (NER) to the keyword using the surrounding text as context;

adding a second semantic type node for an entity type node;

connecting the second semantic type node to the legal matter node with an edge weight; and

adding an expression term node for the keyword with another edge weight; and

modifying the e-discovery query using the semantic knowledge graph and additional input during a query generation phase by:

receiving a legal matter type and meet and confer information;

issuing a series of questions to obtain the legal semantic types that are relevant to the legal matter type and the meet and confer information, wherein a priority of an order of questions is determined by weight associated with occurrence of those legal semantic types in the semantic knowledge graph;

modifying the e-discovery query based on the obtained legal semantic types to add one or more predicates that capture historical information based on user feedback responding to the series of questions;

providing the modified e-discovery query as a suggested query; and

in response to receiving selection of the modified e-discovery query, executing the modified e-discovery query.

8. The computer program product of claim 7 , wherein the meet and confer document instances are any of digital instances and physical instances.

9. The computer program product of claim 7 , wherein the program code is executable by the at least one processor to perform:

providing a map for metadata fields and operator combinations.

10. The computer program product of claim 7 , wherein the program code is executable by the at least one processor to perform:

providing a text index for documents.

11. The computer program product of claim 7 , wherein the program code is executable by the at least one processor to perform:

receiving feedback on the modified e-discovery query to improve the semantic knowledge graph.

12. The computer program product of claim 7 , wherein a Software as a Service (SaaS) is configured to perform computer program product operations.

13. A computer system for generating e-discovery queries for an e-discovery system, comprising:

one or more processors, one or more computer-readable memories and one or more computer-readable, tangible storage devices; and

program instructions, stored on at least one of the one or more computer-readable, tangible storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, to perform operations comprising:

iteratively building a semantic knowledge graph during a build phase by,

receiving meet and confer document instances, legal matter types, historical e-discovery queries for different legal matters, and legal semantic types extracted from the historical e-discovery queries;

adding the legal semantic types to the semantic knowledge graph;

in response to adding the legal semantic types to the semantic knowledge graph, identifying a list of terms that serve as a basis of an initial query; and

generating an e-discovery query to for e-discovery system by:

1) mapping a predicate clause of the initial query to a legal semantic type by:

identifying a metadata field and operator combination;

determining the legal semantic type from a glossary;

adding a first semantic type node to the semantic knowledge graph;

connecting the first semantic type node to a legal matter node with an edge weight; and

adding an expression term node for the metadata field with another edge weight; and

2) mapping a keyword of a text clause of the initial query to a legal semantic type by:

running a partial query for the keyword against a saved result set to generate a new result set; and

for each document in the new result set,

retrieving surrounding text around a location within the document where the keyword was found;

applying Named Entity Recognition (NER) to the keyword using surrounding text as context;

adding a second semantic type node for an entity type node;

connecting the second semantic type node to the legal matter node with an edge weight; and

adding an expression term node for the keyword with another edge weight; and

modifying the e-discovery query using the semantic knowledge graph and additional input during a query generation phase by:

receiving a legal matter type and meet and confer information;

issuing a series of questions to obtain the legal semantic types that are relevant to the legal matter type and the meet and confer information, wherein a priority of an order of questions is determined by weight associated with occurrence of those legal semantic types in the semantic knowledge graph;

modifying the e-discovery query based on the obtained legal semantic types to add one or more predicates that capture historical information based on user feedback responding to the series of questions;

providing the modified e-discovery query as a suggested query; and

in response to receiving selection of the modified e-discovery query, executing the modified e-discovery query.

14. The computer system of claim 13 , wherein the meet and confer document instances are any of digital instances and physical instances.

15. The computer system of claim 13 , wherein the operations further comprise:

providing a map for metadata fields and operator combinations.

16. The computer system of claim 13 , wherein the operations further comprise:

providing a text index for documents.

17. The computer system of claim 13 , wherein the operations further comprise:

receiving feedback on the modified e-discovery query to improve the semantic knowledge graph.

18. The computer system of claim 13 , wherein a Software as a Service (SaaS) is configured to perform computer system operations.