Parallel processing of data by multiple semantic reasoning engines

Patent metadata

Field	Value
Publication number	US-2016267384-A1
Application number	US-201514657930-A
Country	US
Kind code	A1
Filing date	Mar 13, 2015
Priority date	Mar 13, 2015
Publication date	Sep 15, 2016
Grant date	—

Abstract

An example method executed by a semantic reasoner is disclosed. The method includes identifying, from a plurality of rules, one or more pairs of chained rules, and, from the one or more pairs of chained rules, assigning rules chained together to a respective rule-set of P rule-sets. The method also includes assigning individuals, from a plurality of individuals referenced by the plurality of rules, referenced by each rule-set of the P rule-sets to an individual-set associated with the each rule-set and mapping the rules from the each rule-set and the individuals from the individual-set associated with the each rule-set into a respective knowledge base instance associated with the each rule-set. Such a method ensures knowledge completeness and sound inference while allowing parallel semantic reasoning within a given stream window.

First claim

What is claimed is:

1 . A method executed by a semantic reasoner, comprising:
identifying, from a plurality of rules, one or more pairs of chained rules; from the one or more pairs of chained rules, assigning rules chained together to a respective rule-set of P rule-sets; from a plurality of individuals referenced by the plurality of rules, assigning individuals referenced by each rule-set of the P rule-sets to an individual-set associated with the each rule-set; and mapping the rules from the each rule-set and the individuals from the individual-set associated with the each rule-set into a respective knowledge base (KB) instance associated with the each rule-set.

2 . The method according to claim 1 , wherein a pair of rules from the plurality of rules are identified as chained when:
execution of one rule of the pair of rules affects or is affected by execution of another rule of the pair of rules, or at least one class atom comprised in a head of one rule of the pair of rules is comprised in a body of another rule of the pair of rules, or at least one property atom comprised in a head of one rule of the pair of rules is a property atom in a body of another rule of the pair of rules and a subject of the property atom comprised in the head of one rule references at least one individual that is referenced by a subject of the property atom in the body of another rule, or a subject of at least one class atom comprised in a head of one rule of the pair of rules references at least one individual that is referenced by a subject of at least one property atom comprised in a body of another rule of the pair of rules.

3 . The method according to claim 1 , further comprising:
identifying data corresponding to each rule-set; automatically mapping the data corresponding to the each rule-set to a KB instance associated with the each rule-set; feeding contents of each KB instance to a respective reasoning engine instance associated with the each KB instance; and performing machine reasoning over the data in each KB instance using the reasoning engine instance associated with the each KB instance.

4 . The method according to claim 3 , wherein performing machine reasoning comprises at least two reasoning engine instances performing machine reasoning in parallel.

5 . The method according to claim 3 , wherein the machine reasoning is performed by each reasoning engine instance according to the rule-set associated with the reasoning engine instance.

6 . The method according to claim 3 , further comprising:
for each reasoning engine instance, providing results of processing the contents of each KB instance by an associated reasoning engine instance to a shared inference database.

7 . The method according to claim 3 , further comprising:
generating a fully populated semantics model of a network from the data according to a ontology, wherein the automatically mapping the data corresponding to the each rule-set to the KB instance associated with the each rule-set comprises automatically mapping a portion of the fully populated semantics model comprising the data corresponding to the each rule-set.

8 . One or more computer readable storage media encoded with software comprising computer executable instructions and, when the software is executed, operable to:
identify, from a plurality of rules, one or more pairs of chained rules; from the one or more pairs of chained rules, assign rules chained together to a respective rule-set of P rule-sets; from a plurality of individuals referenced by the plurality of rules, assign individuals referenced by each rule-set of the P rule-sets to an individual-set associated with the each rule-set; and map the rules from the each rule-set and the individuals from the individual-set associated with the each rule-set into a respective knowledge base (KB) instance associated with the each rule-set.

9 . The one or more computer readable storage media according to claim 8 , wherein a pair of rules from the plurality of rules are identified as chained when:
execution of one rule of the pair of rules affects or is affected by execution of another rule of the pair of rules, or at least one class atom comprised in a head of one rule of the pair of rules is comprised in a body of another rule of the pair of rules, or at least one property atom comprised in a head of one rule of the pair of rules is a property atom in a body of another rule of the pair of rules and a subject of the property atom comprised in the head of one rule references at least one individual that is referenced by a subject of the property atom in the body of another rule, or a subject of at least one class atom comprised in a head of one rule of the pair of rules references at least one individual that is referenced by a subject of at least one property atom comprised in a body of another rule of the pair of rules.

10 . The one or more computer readable storage media according to claim 8 , further operable to:
identify data corresponding to each rule-set; automatically map the data corresponding to the each rule-set to a KB instance associated with the each rule-set; feed contents of each KB instance to a respective reasoning engine instance associated with the each KB instance; and perform machine reasoning over the data in each KB instance using the reasoning engine instance associated with the each KB instance.

11 . The one or more computer readable storage media according to claim 10 , wherein performing machine reasoning comprises at least two reasoning engine instances performing machine reasoning in parallel.

12 . The one or more computer readable storage media according to claim 10 , further operable to:
for each reasoning engine instance, provide results of processing the contents of each KB instance by an associated reasoning engine instance to a shared inference database.

13 . The one or more computer readable storage media according to claim 10 , further operable to:
generate a fully populated semantics model of a network from the data according to a ontology, wherein the automatically mapping the data corresponding to the each rule-set to the KB instance associated with the each rule-set comprises automatically mapping a portion of the fully populated semantics model comprising the data corresponding to the each rule-set.

14 . A system for enabling semantic reasoning, the system comprising:
at least one memory configured to store computer executable instructions, and at least one processor coupled to the at least one memory and configured, when executing the instructions, to:
identify, from a plurality of rules, one or more pairs of chained rules;
from the one or more pairs of chained rules, assign rules chained together to a respective rule-set of P rule-sets;
from a plurality of individuals referenced by the plurality of rules, assign individuals referenced by each rule-set of the P rule-sets to an individual-set associated with the each rule-set; and
map the rules from the each rule-set and the individuals from the individual-set associated with the each rule-set into a respective knowledge base (KB) instance associated with the each rule-set.

15 . The system according to claim 14 , wherein a pair of rules from the plurality of rules are identified as chained when:
execution of one rule of the pair of rules affects or is affected by execution of another rule of the pair of rules, or at least one class atom comprised in a head of one rule of the pair of rules is comprised in a body of another rule of the pair of rules, or at least one property ato

Assignees

• Cisco Tech Inc

Inventors

• Salam Samer
• Voit Eric A

Classifications

• H04L41/0873

  Checking configuration conflicts between network elements · CPC title

• G06F40/30

  Semantic analysis · CPC title

• H04L41/145

  involving simulating, designing, planning or modelling of a network · CPC title

• G06N5/025 Primary

  Extracting rules from data · CPC title

• H04L41/122

  of virtualised topologies, e.g. software-defined networks [SDN] or network function virtualisation [NFV] · CPC title