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EXTRACTING, DERIVING, AND USING LEGAL MATTER SEMANTICS TO GENERATE E-DISCOVERY QUERIES IN AN E-DISCOVERY SYSTEM

  • US 20200134757A1
  • Filed: 10/30/2018
  • Published: 04/30/2020
  • Est. Priority Date: 10/30/2018
  • Status: Active Grant
First Claim
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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.

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