Adaptive Meta-Relationship Scoring in Semantic Graphs
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Solution Overview
Problem
Existing natural language processing (NLP) methods face challenges in accurately scoring and analyzing meta-relationships between entities, such as sentiment analysis, bias detection, and geo-spatial inference, due to limitations in quantifying complex relationships in semantic graphs.
Innovation Solution
A computer-implemented method for adaptive evaluation of meta-relationships in semantic graphs, which involves encoding weightings for meta-relationships in metadata, applying these weightings to a spreading activation signal, and producing a measure of meta-relationships across concepts, enabling complex scoring and analysis of interrelated entities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional spreading activation is used in semantic graphs, then basic semantic relationships can be evaluated, but meta-relationships (such as sentiment, bias, geo-spatial relevance) cannot be accurately scored
Solution Approach 1:
The patent segments the evaluation process by introducing a separate metadata layer that handles meta-relationship scoring independently from the core semantic graph structure. This allows traditional spreading activation to continue for basic semantics while a parallel metadata-based system handles sentiment, bias, and other meta-relationships, resolving the contradiction by dividing the evaluation function into specialized components.
Solution Approach 2:
The patent adds a metadata dimension to the semantic graph edges and nodes, transforming the evaluation from a single-dimensional semantic relationship assessment to a multi-dimensional framework that simultaneously captures semantic meaning and meta-relationship characteristics (sentiment, bias, geo-spatial relevance) without fundamentally altering the core graph structure.
2Adaptability or versatility
If semantic graphs are enhanced with multiple relationship types, then comprehensive analysis is possible, but the complexity of evaluating different relationship types increases
Solution Approach 1:
The patent creates a universal metadata schema that can represent multiple types of relationships (sentiment, bias, geo-spatial, etc.) using a common evaluation framework. This allows the system to handle diverse relationship types through a single unified process, enhancing versatility while controlling complexity by avoiding separate evaluation mechanisms for each relationship type.
Solution Approach 2:
The patent uses parameter-based metadata attributes to represent different relationship characteristics, allowing the evaluation system to adapt to various relationship types by changing which parameters are activated and how they are weighted, rather than requiring structurally different evaluation paths for each relationship type.
3Measurement precision
If metadata weightings are applied to spreading activation signals, then precise meta-relationship measures are produced, but computational overhead increases
Solution Approach 1:
The patent applies metadata weightings selectively based on the evaluation needs, activating only the necessary metadata attributes and calculations for the specific meta-relationships being queried. This partial action approach maintains measurement precision for required metrics while avoiding unnecessary computational overhead from evaluating all possible metadata dimensions in every graph activation.
Data Source
AI summary
A method and system are provided for adaptive evaluation of meta-relationships in semantic graphs. The method includes providing a semantic graph based on a knowledge base in which concepts in the form of graph nodes are linked by semantic relationships in the form of graph edges. Metadata are encoded in the edges and nodes of the semantic graph, of weightings for measuring a meta-relationship, wherein the meta-relationship applies to the concepts of the semantic graph and is independent of the semantic relationship defined by the edges of the semantic graph. A graph activation is carried out for an input context relating to one or more concepts of the semantic graph, wherein the weightings are applied to a spreading activation signal through the semantic graph to produce a measure of the meta-relationship for a sub-set of concepts of the semantic graph.