Systems and methods for semantic inference and reasoning

• US 10,423,881 B2
• Filed: 04/20/2015
• Issued: 09/24/2019
• Est. Priority Date: 03/16/2012
• Status: Active Grant

• Current Assignee: Contiem Incorporated
• Original Assignee: Orbis Technology
• Inventors: Joshi, Sameer, Pehle, Todd, Crochet, Larry
• Primary Examiner(s): Chang, Li Wu
• Application Number:
  US14/691,384
• Publication Number:
  US 20150227839A1
• Time in Patent Office: 1,618 Days
• Field of Search: None
• CPC Class Codes: G06F 16/248 Presentation of query results
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/04 Inference or reasoning models

Abstract

A method and system for analyzing a corpus of data artifacts is disclosed. The method comprises obtaining, by a computer, a semantic representation of the data artifacts, where the semantic representation indicates (1) entities identified in the data artifacts, and (2) semantic relationships among the entities as indicated by the data artifacts. The method further comprises 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. The inferring comprises applying, on a cluster-by-cluster basis, a multi-tiered network of inference engines to a portion of the semantic representation corresponding to the cluster, where the multi-tiered network of inference engines includes a domain-independent inference tier and a domain-specific inference tier.

Claims

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

receiving, by a computer, a query identifying one or more search terms;

formulating the query in a query language compatible with a semantic representation;

transmitting, by the computer, the formulated query to a semantic database for execution by a query execution engine configured to access the semantic database, wherein the semantic database comprises;

a semantic representation of one or more data artifacts, accessible by the query execution engine, wherein the semantic representation comprises an n-tuple and indicates entities identified in the data artifacts and semantic relationships among the entities, as indicated by the data artifacts;

one or more clusters of semantically related data artifacts based on the semantic representation; and

additional inferred semantic relationships between pairs of the entities that are added to the semantic representation, wherein the inferred semantic relationships are determined on a cluster-by-cluster basis by applying two or more inference engines of 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, and wherein the inference engines are applied sequentially, in parallel, or iteratively according to a static or dynamic schedule; and

receiving, by the computer one or more data artifacts relevant to the formulated query from the semantic database, wherein relevance is determined by semantic relevance or inferred relationships between search terms and entities.

2. The method of claim 1, further comprising:

identifying, by the computer in response to the formulated query, a cluster of data artifacts most relevant to the one or more search terms, wherein the identifying is dependent on one or more of the inferred semantic relationships in the semantic database.

3. The method of claim 1, further comprising:

processing the received one or more data artifacts relevant to the formulated query into a format for receipt by a client device.

4. The method of claim 1, further comprising:

generating, by the computer, a visualization of the one or more data artifacts relevant to the formulated query for display in a graphical user interface on a screen of a client device.

5. The method of claim 4, wherein the visualization comprises one or more clusters of data artifacts related to the one or more search terms.

6. The method of claim 5, wherein the visualization comprises an indication of a semantic relationship between the one or more search terms and the one or more clusters of data artifacts.

7. The method of claim 5, wherein the cluster comprises a set of two or more nodes, each node corresponding to a data artifact.

8. The method of claim 7, wherein the visualization comprises an indication of a strength of a relationship between two or more nodes of the cluster.

17. The method of claim 1, wherein the semantic representation is in a Resource Description Framework format.

9. A system for querying a corpus of data artifacts comprising:

a data processing system comprising one or more computer processors;

a network interface for receiving messages and transmitting messages over a network; and

,

a data storage system storing program instructions executable by the one or more computer processors to;

receive, over the network, a query identifying one or more search terms;

formulate the query in a query language compatible with a semantic representation;

transmit the formulated query to a semantic database for execution by a query execution engine configured to access the semantic database, wherein the semantic database comprises;

a semantic representation of one or more data artifacts, accessible by the query execution engine, wherein the semantic representation comprises an n-tuple and indicates entities identified in the data artifacts and semantic relationships among the entities, as indicated by the data artifacts;

one or more clusters of semantically related data artifacts based on the semantic representation; and

additional inferred semantic relationships between pairs of the entities that are added to the semantic representation, wherein the inferred semantic relationships are determined on a cluster-by-cluster basis by applying two or more inference engines of 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, and wherein the inference engines are applied sequentially, in parallel, or iteratively according to a static or dynamic schedule; and

receive one or more data artifacts relevant to the query from the semantic database, wherein relevance is determined by semantic relevance or inferred relationships between search terms and entities.

10. The system of claim 9, wherein the data storage system further stores program instructions executable by the one or more computer processors to:

identify, in response to the formulated query, a cluster of data artifacts most relevant to the one or more search terms, wherein the identifying is dependent on one or more of the inferred semantic relationships in the semantic database.

11. The system of claim 9, wherein the data storage system further stores program instructions executable by the one or more computer processors to:

process the received one or more data artifacts relevant to the formulated query into a format for receipt by a client device.

12. The system of claim 9, wherein the data storage system further stores program instructions executable by the one or more computer processors to:

generate a visualization of the one or more data artifacts relevant to the query for display in a graphical user interface on a screen of a client device.

13. The system of claim 12, wherein the visualization comprises one or more clusters of data artifacts related to the one or more search terms.

14. The system of claim 13, wherein the visualization comprises an indication of a semantic relationship between the one or more search terms and the one or more clusters of data artifacts.

15. The method of claim 13, wherein the cluster comprises a set of two or more nodes, each node corresponding to a data artifact.

16. The method of claim 15, wherein the visualization comprises an indication of a strength of a relationship between two or more nodes of the cluster.

18. The system of claim 9, wherein the semantic representation is in a Resource Description Framework format.