Methods and systems for searching and retrieving information

A method for constructing a knowledge base, the method comprising:

• obtaining a set of N files, wherein each file included in the set of files is assigned to one of M different categories, where N and M are greater than 1;
• based on (i) content of the N files, (ii) a loss function of a first model, and (iii) an objective function of a second model, identifying a set of T topics, where T is greater than 1 and each topic is a group of one or more keywords;
• generating the knowledge base using the identified topics;
• and
• for each one of the N files, based on a particular category to which the file is assigned and keywords included in the file, adding the file to the knowledge base, wherein
• the first model is a categorization model that functions to map an input sentence to one of the M categories.

An apparatus for retrieving information using a knowledge base, the apparatus being adapted to:

• receive a search query entered by a user;
• based on the received search query, use a first model to identify a category corresponding to the received search query, wherein one or more files are assigned to the identified category and further wherein the first model is a categorization model that functions to map an input to one of M different categories, where M is greater than 1;
• based on (i) the received search query, (ii) a loss function of the first model, and (iii) an objective function of a second model, identify T topics corresponding to the received search query, where T is greater than or equal to 1;
• using the identified category and the identified topics, perform a search for the received search query only on a part of the knowledge base that is associated with the identified category and/or the identified topics;
• and
• based on the performed search, retrieve one or more files associated with the identified category and/or the identified topics.

An apparatus for constructing a knowledge base, the apparatus being adapted to:

• obtain a set of N files, wherein each file included in the set of files is assigned to one of M different categories, where N and M are greater than 1;
• based on (i) content of the N files, (ii) a loss function of a first model, and (iii) an objective function of a second model, identify a set of T topics, where T is greater than 1 and each topic is a group of one or more keywords;
• generate the knowledge base using the identified topics;
• and
• for each one of the N files, based on a particular category to which the file is assigned and keywords included in the file, add the file to the knowledge base, wherein
• the first model is a categorization model that functions to map an input sentence to one of the M categories.