Systems and methods for semantic inference and reasoning

1. A method of analyzing a corpus of data artifacts, the method comprising:

obtaining, by a computer, a semantic representation of the data artifacts, wherein the semantic representation indicates:

entities identified in the data artifacts; and

semantic relationships among the entities, as indicated by the data artifacts;

clustering the data artifacts, by a computer, into clusters of semantically related data artifacts based on the semantic representation; and

inferring, by a computer, additional semantic relationships between pairs of the entities, wherein the inferring comprises:

on a cluster-by-cluster basis, applying a multi-tiered network of inference engines to a portion of the semantic representation corresponding to the cluster, wherein the multi-tiered network of inference engines comprises a domain-independent inference tier and a domain-specific inference tier,

wherein applying the multi-tiered network of inference engines comprises:

applying two or more inference engines sequentially, in parallel, or iteratively according to a static or dynamic schedule.

2. The method of claim 1 , wherein obtaining the semantic representation comprises:

applying natural language processing techniques to extract the entities and relationships from natural language content contained of the data artifacts.

3. The method of claim 1 , wherein obtaining the semantic representation comprises:

determining that the same entity is identified in the artifacts using different identifiers; and

disambiguating the entity by replacing one or more of the different identifiers with a common identifier for the entity.

4. The method of claim 1 , wherein clustering the data artifacts comprises:

performing a semantic analysis to determine semantic interrelatedness of the data artifacts based on respective ones of the entities and relationships in the data artifacts.

5. The method of claim 1 , wherein clustering the data artifacts comprises: performing a syntactic analysis to determine syntactic interrelatedness of the data artifacts based on syntactic overlap of respective content of the data artifacts.

6. The method of claim 1 , wherein the static or dynamic schedule is defined in one or more runtime configuration files.

7. The method of claim 1 , wherein applying the multi-tiered network of inference engines comprises: applying a plurality of domain-independent inference engines in the domain-independent tier and subsequently applying a plurality of inference engines in the domain-specific tier.

8. The method of claim 1 , wherein the clustering and inferring are implemented using a parallel programming and execution model.

9. The method of claim 8 , wherein the parallel programming and execution model corresponds to a MapReduce framework.

10. The method of claim 1 , further comprising:

receiving, by a computer, a query identifying one or more of the entities;

identifying, by a computer in response to the query, a cluster of data artifacts most relevant to the one or more entities, wherein the identifying is dependent on one or more of the obtained or :inferred semantic relationships.

11. A system comprising:

a parallel processing facility comprising:

a plurality of computer processing cores; and

one or more memories coupled to the computer processing cores and storing program instructions executable by the processing cores to implement a semantic inference and reasoning engine configured to analyze a corpus of data artifacts by:

obtaining a semantic representation of the data artifacts, wherein the semantic representation indicates:

entities identified in the data artifacts; and

semantic relationships among the entities, as indicated by the data artifacts;

clustering the data artifacts into clusters of semantically related data artifacts based on the semantic representation; and

inferring additional semantic relationships between pairs of the entities, wherein the inferring comprises:

on a cluster-by-cluster basis, applying a multi-tiered network of inference engines to a portion of the semantic representation corresponding to the cluster, wherein the multi-tiered network of inference engines comprises a domain-independent inference tier and a domain-specific inference tier,

wherein applying the multi-tiered network of inference engines comprises applying two or more inference engines sequentially, in parallel, or iteratively according to a static or dynamic schedule.

12. The system of claim 11 further comprising a distributed storage facility coupled to the parallel processing facility and storing the corpus of data artifacts.

13. The system of claim 12 , wherein the storage facility comprises a distributed file system.

14. The system of claim 11 , wherein the parallel processing facility comprises at least one of: a compute cluster, a superscalar supercomputer, a desktop grid, or a compute cloud.

15. The system of claim 11 , wherein one or more memories further store program instructions executable to implement a parallel computation scheduling framework for executing the semantic inference and reasoning engine on the parallel processing facility using a MapReduce pattern.

16. The system of claim 11 , wherein obtaining the semantic representation comprises: applying natural language processing techniques to extract the entities and relationships from natural language content contained of the data artifacts.

17. The system of claim 11 , wherein clustering the data artifacts comprises at least one of:

performing a semantic analysis to determine semantic interrelatedness of the data artifacts based on respective ones of the entities and relationships in the data artifacts; and

performing a syntactic analysis to determine syntactic interrelatedness of the data artifacts based on syntactic overlap of respective content of the data artifacts.

18. The system of claim 11 , wherein applying the multi-tiered network of inference engines comprises: applying a plurality of domain-independent inference engines in the domain-independent tier and subsequently applying a plurality of inference engines in the domain-specific tier.

19. A method of analyzing a corpus of data artifacts, the method comprising:

obtaining, by a computer, a semantic representation of the data artifacts, wherein the semantic representation indicates:

entities identified in the data artifacts; and

semantic relationships among the entities, as indicated by the data artifacts;

wherein obtaining the semantic representation comprises:

determining that the same entity is identified in the artifacts using different identifiers; and

disambiguating the entity by replacing one or more of the different identifiers with a common identifier for the entity;

clustering the data artifacts, by a computer, into clusters of semantically related data artifacts based on the semantic representation; and

inferring, by a computer, additional semantic relationships between pairs of the entities, wherein the inferring comprises:

on a cluster-by-cluster basis, applying a multi-tiered network of inference engines to a portion of the semantic representation corresponding to the cluster, wherein the multi-tiered network of inference engines comprises a domain-independent inference tier and a domain-specific inference tier.

20. A method of analyzing a corpus of data artifacts, the method comprising:

obtaining, by a computer, a semantic representation of the data artifacts, wherein the semantic representation indicates:

entities identified in the data artifacts; and

semantic relationships among the entities, as indicated by the data artifacts;

clustering the data artifacts, by a computer, into clusters of semantically related data artifacts based on the semantic representation, wherein clustering the data artifacts comprises at least one of:

performing a semantic analysis to determine semantic interrelatedness of the data artifacts based on respective ones of the entities and relationships in the data artifacts; and

performing a syntactic analysis to determine syntactic interrelatedness of the data artifacts based on syntactic overlap of respective content of the data artifacts; and

inferring, by a computer, additional semantic relationships between pairs of the entities, wherein the inferring comprises:

on a cluster-by-cluster basis, applying a multi-tiered network of inference engines to a portion of the semantic representation corresponding to the cluster, wherein the multi-tiered network of inference engines comprises a domain-independent inference tier and a domain-specific inference tier.

21. A method of analyzing a corpus of data artifacts, the method comprising:

obtaining, by a computer, a semantic representation of the data artifacts, wherein the semantic representation indicates:

entities identified in the data artifacts; and

semantic relationships among the entities, as indicated by the data artifacts;

clustering the data artifacts, by a computer, into clusters of semantically related data artifacts based on the semantic representation; and

inferring, by a computer, additional semantic relationships between pairs of the entities, wherein the inferring comprises:

on a cluster-by-cluster basis, applying a multi-tiered network of inference engines to a portion of the semantic representation corresponding to the cluster, wherein the multi-tiered network of inference engines comprises a domain-independent inference tier and a domain-specific inference tier,

wherein applying the multi-tiered network of inference engines comprises:

applying a plurality of domain-independent inference engines in the domain-independent tier and subsequently applying a plurality of inference engines in the domain-specific tier.