Providing answers to questions including assembling answers from multiple document segments

• US 9,495,481 B2
• Filed: 09/14/2012
• Issued: 11/15/2016
• Est. Priority Date: 09/24/2010
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Brown, Eric W., Chu-Carroll, Jennifer, Ferrucci, David A., Murdock, James W. IV
• Primary Examiner(s): Nguyen, Thu Nga
• Application Number:
  US13/618,131
• Publication Number:
  US 20130013615A1
• Time in Patent Office: 1,523 Days
• Field of Search: None
• US Class Current: 1/1
• CPC Class Codes: A61B 34/10 Computer-aided planning, simulation or modelling of surgical operations
• CPC Class Codes: A61B 5/00 Measuring for diagnostic purposes radiation diagnosis A61B6/00; diagnosis by ultrasonic, sonic or infrasonic waves A61B8/00; Identification of persons
• CPC Class Codes: G06F 16/2428 Query predicate definition using graphical user interfaces, including menus and forms G06F16/2423 takes precedence
• CPC Class Codes: G06F 16/24522 Translation of natural language queries to structured queries
• CPC Class Codes: G06F 16/2455 Query execution
• CPC Class Codes: G06F 16/31 Indexing; Data structures therefor; Storage structures
• CPC Class Codes: G06F 16/334 Query execution G06F16/335 takes precedence
• CPC Class Codes: G06F 16/532 Query formulation, e.g. graphical querying
• CPC Class Codes: G06F 16/90335 Query processing
• CPC Class Codes: G06F 16/93 Document management systems
• CPC Class Codes: G06F 3/048 Interaction techniques based on graphical user interfaces [GUI]
• CPC Class Codes: G06F 40/134 Hyperlinking
• CPC Class Codes: G06F 40/169 Annotation, e.g. comment data or footnotes
• CPC Class Codes: G06F 40/211 Syntactic parsing, e.g. based on context-free grammar [CFG] or unification grammars
• CPC Class Codes: G06N 5/027 Frames
• CPC Class Codes: G16H 10/20 for electronic clinical trials or questionnaires
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 15/00 ICT specially adapted for medical reports, e.g. generation or transmission thereof
• CPC Class Codes: G16H 40/20 for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/70 : for mining of medical data, e.g. analysing previous cases of other patients
• CPC Class Codes: G16H 70/00 : ICT specially adapted for the handling or processing of medical references
• CPC Class Codes: Y02A 90/10 : Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

A method, system and computer program product for generating answers to questions. In one embodiment, the method comprises receiving an input query, identifying a plurality of candidate answers to the query; and for at least one of these candidate answers, identifying at least one proof of the answer. This proof includes a series of premises, and a multitude of documents are identified that include references to the premises. A set of these documents is selected that include references to all of the premises. This set of documents is used to generate one or more scores for the one of the candidate answers. A defined procedure is applied to the candidate answers to determine a ranking for the answers, and this includes using the one or more scores for the at least one of the candidate answers in the defined procedure to determine the ranking for this one candidate answer.

Claims

1. A method of generating a score for a candidate answer to an input query, the method comprising:

receiving an input query;

conducting a search in a data source to identify a candidate answer to the input query;

determining a set of documents using theorem proving for scoring the candidate answer, including;

identifying a plurality of logical proofs of the candidate answer, each of the logical proofs including a conclusion and a sequence of premises that logically prove the conclusion, including

using the candidate answer as the conclusion of each of the logical proofs, and

wherein the sequence of the premises of each of the logical proofs forms a logical proof of the candidate answer;

for each of the logical proofs, identifying one or more documents that establish all the premises of the each of the logical proofs, including

for each of the premises of said each of the logical proofs, searching through a group of documents to identify one or more of the documents of the group of documents that include said each of the premises, and

selecting a plurality of the identified documents of the group of documents to form a set of documents for said each of the logical proofs, wherein the set of documents for said each of the logical proofs includes all the premises of said each of the logical proofs; and

selecting, based on specified criteria, one of the sets of documents for the logical proofs as the set of documents for scoring the candidate answer; and

using the set of documents selected for scoring the candidate answer to generate a score for the candidate answer.

2. The method according to claim 1, wherein the selecting a plurality of the identified documents to form a set of documents for said each of the logical proofs includes using a defined algorithm to identify a quasi-minimal number of the identified documents to form the set of documents for the each logical proof.

3. The method according to claim 1, wherein the selecting a plurality of the identified documents to form a set of documents for said each of the logical proofs includes using a defined algorithm to identify a minimum number of the identified documents needed to include all of the premises of said each of the logical proofs.

4. The method according to claim 1, wherein:

for each of the premises of each of the logical proofs, each of the documents identified that include said each of the premises includes to a given number of the premises of said each of the logical proofs; and

the selecting a plurality of the identified documents to form a set of documents for said each of the logical proofs includes selecting the plurality of the identified documents to form the set of documents for said each of the logical proofs in order of the number of the premises of said each of the logical proofs included in each of the documents identified that include said each of the premises.

5. The method according to claim 1, wherein for each of the premises of each of the logical proofs, the selecting a plurality of the identified documents to form a set of documents for said each of the logical proofs includes selecting a plurality of the identified documents to form said set of documents for said each of the logical proofs based on the number of the premises of said each of the logical proofs in each of the documents identified that include said each of the premises.

6. The method according to claim 1, wherein each of the plurality of logical proofs is a deductive proof, and the sequence of premises of said each of the logical proofs deductively establish the candidate answer.

7. A system for generating a score for a candidate answer to an input query, the system comprising:

a computer device comprising at least one distinct software module, each distinct software module being embodied on a tangible computer-readable medium;

a memory; and

at least one processor coupled to the memory and operative for;

receiving an input query;

conducting a search in a data source to identify a candidate answer to the input query;

determining a set of documents using theorem proving for scoring the candidate answer, including

identifying a plurality of logical proofs of the candidate answer, each of the logical proofs including a conclusion and a sequence of premises that logically prove the conclusion, including

using the candidate answer as the conclusion of each of the logical proofs, and

wherein the sequence of the premises of each of the logical proofs forms a logical proof of the candidate answer;

for each of the logical proofs, identifying a one or more of documents that establish all the premises of the each of the logical proofs, including

for each of the premises of said each of the logical proofs, searching through a group of documents to identify one or more of the documents of the group of documents that include said each of the premises, and

selecting a plurality of the identified documents of the group of documents to form a set of documents for said each of the logical proofs, wherein the set of documents for said each of the logical proofs includes all the premises of said each of the logical proofs; and

selecting, based on specified criteria, one of the sets of documents for the logical proofs as the set of documents for scoring the candidate answer; and

using the set of documents selected for scoring the candidate answer to generate a score for the candidate answer.

8. The system according to claim 7, wherein the selecting a plurality of the identified documents to form a set of documents for said each of the logical proofs includes using a defined algorithm to identify a quasi-minimal number of the identified documents to form the set of documents for said each of the logical proofs.

9. The system according to claim 7, wherein the selecting a plurality of the identified documents to form a set of documents for said each of the logical proofs includes using a defined algorithm to identify a minimum number of the identified documents needed to include all of the premises of said each of the logical proofs.

10. The system according to claim 7, wherein:

for each of the premises of each of the logical proofs, each of the documents identified that include said each of the premises includes a given number of the premises of said each of the logical proofs; and

the selecting a plurality of the identified documents to form a set of documents for said each of the logical proofs includes selecting the plurality of the identified documents to form the set of documents for said each of the logical proofs in order of the number of the premises of said each of the logical proofs included in each of the documents identified that include said each of the premises.

11. The system according to claim 7, wherein each of the plurality of logical proofs is a deductive proof, and the sequence of premises of said each of the logical proofs deductively establish the candidate answer.

12. An article of manufacture comprising:

at least one tangible non-transitory computer readable device having computer readable program code logic tangibly embodied therein to generate a score for a candidate answer to an input query, the computer readable program code logic, when executing, performing the following;

receiving an input query;

conducting a search in a data source to identify a candidate answer to the input query;

determining a set of documents using theorem proving for scoring the candidate answer, including

identifying a plurality of logical proofs of the candidate answer, each of the logical proofs including a conclusion and a sequence of premises that logically prove the conclusion, including

using the candidate answer as the conclusion of each of the logical proofs, and

wherein the sequence of the premises of each of the logical proofs forms a logical proof of the candidate answer;

for each of the logical proofs, identifying one or more of documents that establish all the premises of the each of the logical proofs, including

for each of the premises of said each of the logical proofs, searching through a group of documents to identify one or more of the documents of the group of documents that include said each of the premises, and

selecting a plurality of the identified documents of the group of documents to form a set of documents for said each of the logical proofs, wherein the set of documents for said each of the logical proofs includes all the premises of said each of the logical proofs; and

selecting, based on the specified criteria, one of the sets of documents for the logical proofs as the set of documents for scoring the candidate answer; and

using the set of documents selected for scoring the candidate answer to generate a score for the candidate answer.

13. The article of manufacture according to claim 12, wherein the selecting a plurality of the identified documents to form a set of documents for said each of the logical proofs includes using a defined algorithm to identify a quasi-minimal number of the identified documents to form the set of documents for said each of the logical proofs.

14. The article of manufacture according to claim 12, wherein:

for each of the premises of each of the logical proofs, each of the documents identified that include said each of the promises includes a given number of the premises of said each of the logical proofs; and

the selecting a plurality of the identified documents to form the set of documents for said each of the logical proofs includes;

using a defined algorithm to identify a minimum number of the identified documents needed to include all of the premises of said each of the logical proofs; and

selecting the plurality of the identified documents to form the set of documents for said each of the logical proofs in order of the number of the premises of said each of the logical proofs included in each of the documents identified that include said each of the premises.

15. The article of manufacture according to claim 12, wherein each of the plurality of logical proofs is a deductive proof, and the sequence of premises of said each of the logical proofs deductively establish the candidate answer.