Using ontological information in open domain type coercion

1. A computer program product for automatically generating answers to questions, the computer program product comprising a non-transitory storage medium, said medium not a propagating signal, said non-transitory storage medium readable by a processing circuit and storing instructions run by the processing circuit for performing a method, the method comprising:

receiving an input query;

determining a lexical answer type (LAT) for the input query;

receiving a candidate answer for the input query;

matching the candidate answer to a first instance in an ontology;

matching the first instance to a first instance type in the ontology;

matching the LAT to a second instance type in the ontology;

identifying one or more logical rules in the ontology that relate the second instance type to the first instance type;

producing a score for the candidate answer based on the matching steps; and

modifying the score for the candidate answer based on the logical rule identified that relates the second instance type and the first instance type.

2. The computer program product of claim 1 , wherein said identifying one or more logical rules comprises:

identifying an axiom in said ontology indicating a disjoint relation between the first instance type and the second instance type.

3. The computer program product of claim 1 , wherein said identifying one or more logical rules comprises:

identifying an axiom in said ontology indicating one of:

an equivalence relation between the first instance type and the second instance type,

a subsumption relation between the first instance type and the second instance type, and

a sibling relation between the first instance type and the second instance type.

4. The computer program product of claim 1 , wherein said lexical answer type is a word concept, said ontology providing one or more senses associated with a word concept.

5. The computer program product of claim 4 , wherein said matching the lexical answer type to the second instance type in the ontology comprises:

accessing a resource having ontological content;

obtaining, from said resource, a ranking of senses of the obtained type or concept instance.

6. The computer program product of claim 1 , wherein said lexical answer type comprises a query LAT string, said matching the lexical answer type to the second instance in the ontology comprises:

accessing a resource having ontological content;

obtaining one or more types from said resource that match the query LAT string; and

accessing a data corpus, and obtaining from said data corpus a ranked list of second instances based on said obtained one or more types therein.

7. The computer program product of claim 6 , wherein said matching the lexical answer type to a second instance type in the ontology further comprises:

accessing said resource having ontological content;

identifying a context in said input query; and

computing a similarity measure between the identified context of said input query and contextual data associated with the obtained types from the ontological content.

8. The computer program product of claim 7 , wherein said computing a similarity measure comprises:

obtaining a representation of said context; and

implementing a function to compute said similarity between the identified context and contextual data based on the representation.

9. The computer program product of claim 8 , wherein said implementing a function comprises:

aligning the context using a BOW (bag of words) approach; or,

applying a vector model to compute said similarity.

10. The computer program product of claim 8 , further comprising:

computing a confidence score based on a weighted or un-weighted combination of one or more of: said sense rank, said ranked list of said second instances, and said computed similarity measure.

11. The computer program product of claim 4 , wherein said candidate answer comprises a candidate answer string, said matching the candidate answer to a first instance in the ontology comprises:

accessing a resource having ontological content;

obtaining one or more types from said resource that match said candidate answer string; and

accessing a data corpus, and obtaining from said data corpus a ranked list of first instances based on a frequency of occurrence of said one or more types therein.

12. The computer program product of claim 11 , wherein said matching the candidate answer to a first instance in the ontology further comprises:

accessing said resource having ontological content;

identifying a context in said input query;

computing a similarity measure between the identified context of said input query and contextual data associated with the obtained types from the ontological content.

13. The computer program product of claim 12 , further comprising:

computing a confidence score based on a weighted or un-weighted combination of said ranked list of first instances and said computed similarity measure.

14. A system for generating answers to questions comprising:

a memory;

a hardware processor in communication with the memory configured for:

receiving an input query;

determining a lexical answer type (LAT) for the input query;

receiving a candidate answer for the input query;

matching the candidate answer to a first instance in an ontology;

matching the first instance to a first instance type in the ontology;

matching the LAT to a second instance type in the ontology;

identifying one or more logical rules in the ontology that relate the second instance type to the first instance type;

producing a score for the candidate answer based on the matching steps; and

modifying the score for the candidate answer based on the logical rule identified that relates the second instance type and the first instance type.

15. The system of claim 14 , wherein said identifying one or more logical rules comprises:

identifying an axiom in said ontology indicating a disjoint relation between the first instance type and the second instance type.

16. The system of claim 14 , wherein said identifying one or more logical rules comprises:

identifying an axiom in said ontology indicating one of:

an equivalence relation between the first instance type and the second instance type,

a subsumption relation between the first instance type and the second instance type, and

a sibling relation between the first instance type and the second instance type.

17. The system of claim 14 , wherein said lexical answer type is a word concept, said ontology providing one or more senses associated with a word concept.

18. The system of claim 17 , wherein said matching the lexical answer type to the second instance type in the ontology comprises:

accessing a resource having ontological content;

obtaining, from said resource, a ranking of senses of the obtained type or concept instance.

19. The system of claim 18 , wherein said lexical answer type comprises a query LAT string, said matching the lexical answer type to the second instance in the ontology comprises:

accessing a resource having ontological content;

obtaining one or more types from said resource that match the query LAT string; and

accessing a data corpus, and obtaining from said data corpus a ranked list of second instances based on said obtained one or more types therein.

20. The system of claim 19 , wherein said matching the lexical answer type to a second instance type in the ontology further comprises:

accessing said resource having ontological content;

identifying a context in said input query; and

computing a similarity measure between the identified context of said input query and contextual data associated with the obtained types from the ontological content.

21. The system of claim 20 , wherein said computing a similarity measure comprises:

obtaining a representation of said context; and

implementing a function to compute said similarity between the identified context and contextual data based on the representation.

22. The system of claim 21 , wherein said implementing a function comprises:

aligning the context using a BOW (bag of words) approach; or,

applying a vector model to compute said similarity.

23. The system of claim 21 , further comprising:

computing a confidence score based on a weighted or un-weighted combination of one or more of: said sense rank, said ranked list of said second instances, and said computed similarity measure.

24. The system of claim 17 , wherein said candidate answer comprises a candidate answer string, said matching the candidate answer to a first instance in the ontology comprises:

accessing a resource having ontological content;

obtaining one or more types from said resource that match said candidate answer string; and

accessing a data corpus, and obtaining from said data corpus a ranked list of first instances based on a frequency of occurrence of said one or more types therein.

25. The system of claim 24 , wherein said matching the candidate answer to a first instance in the ontology further comprises:

accessing said resource having ontological content;

identifying a context in said input query;

computing a similarity measure between the identified context of said input query and contextual data associated with the obtained types from the ontological content.

26. The system of claim 25 , wherein the hardware processor is further configured for:

computing a confidence score based on a weighted or un-weighted combination of said ranked list of first instances and said computed similarity measure.