Method and system of ranking and clustering for document indexing and retrieval

A clustering method comprising the steps of:

• parsing an input query into at least one query predicate structure;
• vectorizing said input query;
• identifying said at least one query predicate structure by a first predicate key that is a first integer, and constructing multi-dimensional vectors, for said at least one query predicate structure, using said integer;
• parsing a plurality of documents into at least one document predicate structure for each of said plurality of documents;
• vectorizing said plurality of documents;
• identifying said at least one document predicate structure by a second predicate key that is a second integer, wherein conceptual nearness of two of said document predicate structures is estimated by subtracting corresponding ones of said second predicate keys;
• comparing said at least one query predicate structure with said at least one document predicate structure for said plurality of documents;
• clustering similar documents, within said plurality of documents, wherein at least one document vector representation matches said at least one query predicate structure.

A relevancy ranking system comprising:

• at least one ontological parser to parse an input query into at least one query predicate structure and to parse each document of a set of documents into at least one document predicate structure;
• an input query predicate storage unit that stores said at least one input query predicate structure;
• a document predicate storage unit that stores said at least one document predicate structure for each of said documents in said set;
• a query vectorization unit that converts said at least one query predicate structure into multi-dimensional numerical query vectors;
• a document vectorization unit that converts said at least one document predicate structure into second multi-dimensional numerical document vectors;
• and
• a relevancy ranking unit that compares each of said at least one input query predicate structure with said at least one document predicate structure, calculates a matching degree to assign different relevance values to different parts of said at least one query predicate structure and said at least one document predicate structure match, and calculates a similarity coefficient based on pairs of said at least one query predicate structure and said at least one document predicate structure to determine relevance of each one of said set of documents to said input query.

A relevancy ranking system comprising:

• at least one ontological parser to parse an input query into at least one query predicate structure and to parse each of a set of documents into at least one document predicate structure;
• an input query predicate storage unit that stores said at least one input query predicate structure;
• a document predicate storage unit that stores said at least one document predicate structure for each of said documents in said set;
• a document vectorization unit that converts said at least one document predicate structure into multi-dimensional numerical vectors;
• a query vectorization unit that converts said at least one query predicate structures into second multi-dimensional numerical vectors;
• a relevancy ranking unit that compares each of said at least one input query predicate structure with each of said at least one document predicate structure, calculates a matching degree to assign different relevance values to different parts of said at least one query predicate structure and said at least one document predicate structure match, and calculates a similarity coefficient based on pairs of said at least one query predicate structure and said at least one document predicate structure to determine relevance of each one of said set of documents to said input query;
• and
• a neural network for providing clusters of matching ones of said set of documents that match said input query.

A clustering system comprising:

• at least one ontological parser to parse an input query into at least one query predicate structure and to parse each of a set of documents into at least one document predicate structure;
• an input query predicate storage unit that stores said at least one input query predicate structure;
• a document predicate storage unit that stores said at least one document predicate structure for each of said documents in said set;
• a document vectorization unit that converts said at least one document predicate structure into multi-dimensional numerical vector representations;
• a query vectorization unit that converts said at least one query predicate structure into second multi-dimensional numerical vector representations;
• and
• a neural network for providing clusters of matching ones of said set of documents that match said input query.

A question and answering system comprising:

• at least one ontological parser to parse an input query into at least one query predicate structure and to parse each of a set of documents into at least one document predicate structure;
• a query vectorization unit that converts said at least one query predicate structure into multi-dimensional numerical vector representations, wherein said at least one query predicate structure is identified by a first predicate key that is a first integer, and multi-dimensional vectors for said at least one query predicate structure is constructed using said first integer;
• a document vectorization unit that converts said at least one document predicate structure for each of said set of documents into multi-dimensional numerical vector representations, wherein said at least one document predicate structure is identified by a second predicate key that is a second integer, wherein conceptual nearness of two of said document predicate structures is estimated by subtracting corresponding ones of said second predicate keys;
• a clustering unit that groups similar documents, within said set of documents, wherein said at least one multi-dimensional numerical vector representation matches said at least one query predicate structure;
• and
• a relevancy ranking unit that compares said at least one query predicate structure with said at least one document predicate structure for each of said set of documents.