Parallel Semantic Reasoning via Knowledge Base Partitioning
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Solution Overview
Problem
Current semantic reasoners struggle with handling fast-changing data, leading to incomplete knowledge and high processing latency, especially when dealing with complex rules and streaming data in networking scenarios, which can result in missed actions or delayed responses.
Innovation Solution
The proposed solution involves dividing the knowledge base into multiple instances and using a window pane pre-processor to map data into these instances, allowing for parallel processing by multiple reasoning engines. This ensures knowledge completeness and reduces latency by applying the window pane method, which groups rules and individuals into sets for efficient inference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single semantic reasoner processes all rules and individuals sequentially, then knowledge completeness is maintained, but processing latency increases and real-time reasoning becomes infeasible
Solution Approach 1:
The patent divides the knowledge base into multiple instances (KB1, KB2, ..., KBP) by partitioning both the rule sets and individual sets across different reasoning engines. Each reasoning engine Ri processes a specific subset of rules and individuals in parallel, eliminating sequential processing bottlenecks while maintaining complete coverage of all knowledge through the union of all KB instances.
Solution Approach 2:
The patent introduces a new dimension of parallelism by distributing reasoning across multiple engines simultaneously processing different KB instances. This transforms the single-threaded sequential processing model into a multi-dimensional parallel processing architecture, where multiple reasoning operations occur concurrently across different engine instances.
2Loss of time
If multiple reasoning engines process different knowledge base instances in parallel, then processing latency is reduced, but ensuring knowledge completeness becomes more difficult
Solution Approach 1:
Each reasoning engine instance is designed to be universal in its capability to process any subset of rules and individuals assigned to it. The system ensures that the collective set of all KB instances across all engines covers the complete knowledge base, with each engine performing the same reasoning functions on its designated partition, thereby maintaining knowledge completeness through distributed universal processing.
3Productivity
If the knowledge base is partitioned into multiple instances for parallel processing, then processing speed increases, but system complexity increases
Solution Approach 1:
The system segments the monolithic knowledge base into smaller, manageable KB instances that can be processed independently in parallel. This segmentation reduces the computational burden on each individual reasoning engine while enabling simultaneous processing across multiple engines, thereby increasing overall processing speed despite the increased number of components.
Data Source
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AI summary
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.