Adaptive evaluation of meta-relationships in semantic graphs

Patent metadata

Field	Value
Publication number	US-11176325-B2
Application number	US-201715632564-A
Country	US
Kind code	B2
Filing date	Jun 26, 2017
Priority date	Jun 26, 2017
Publication date	Nov 16, 2021
Grant date	Nov 16, 2021

Abstract

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.

First claim

What is claimed is:

1. A computer system for adaptive evaluation of meta-relationships in semantic graphs, the computer system comprising:
a processor and a memory configured to provide computer program instructions to the processor to execute a method comprising:
deriving a semantic graph from a natural language source based on a knowledge base in which concepts in a form of graph nodes are linked by semantic relationships in a form of graph edges;
encoding in metadata of the edges and nodes of the semantic graph, weightings as feature vectors 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, wherein the feature vectors comprise an intensity of the meta-relationship and a confidence score of the intensity, and wherein the intensity uses runtime inputs for the nodes;
carrying out a graph activation 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;
scoring the edges and nodes of the semantic graph in response to the graph activation;
seeding the graph with seed weightings for measures of the meta-relationship obtained from a set of resources independent of the knowledge base on which the semantic graph is based; and
in response to additions to the set of resources independent of the knowledge base on which the semantic graph is based updating the seed weightings.

2. The computer system of claim 1 , wherein carrying out the graph activation for the input context relating to one or more concepts of the semantic graph further comprises:
discovering instances of concepts in the input context; and
activating nodes corresponding to the concepts in the semantic graph, traversing a signal outward to adjacent nodes activating the adjacent nodes in turn whilst applying the weightings to the signal, and determining one or more focus nodes with a highest resultant activation signal.

3. The computer system of claim 2 , further comprising outputting a resultant activated portion of the semantic graph reflecting a measurement of the meta-relationship in the input context.

4. The computer system of claim 3 , further comprising outputting an activated sub-graph where the activation weightings on nodes and edges represent the measurement of the meta-relationship.

5. The computer system of claim 1 , further comprising:
in response to additions to the input context, updating the seed weightings.

6. The computer system of claim 1 , wherein the weightings are feature vectors and the method further comprises calculating the feature vectors in response to runtime inputs for the nodes for instances of concepts of the input context.

7. The computer system of claim 6 , wherein the feature vectors include relevance factors to be applied to the runtime inputs for the nodes.

8. The computer system of claim 6 , wherein the feature vectors include semantic and/or lexical features for instances of concepts in the input context in addition to the meta-relationship.

9. The computer system of claim 6 , wherein the feature vectors define confidence scores for the weightings.

10. The computer system of claim 1 , wherein the meta-relationship relates to a phenomenon in a form of one of a group consisting of:
sentiment analysis, bias evaluation, bias in predictive analysis, query expansion using information retrieval, risk assessment, geo-spatial inference, and suitability of treatment, use or handling including clinical trial matching.

11. A computer program product for adaptive evaluation of meta-relationships in semantic graphs, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to:
deriving a semantic graph from a natural language source based on a knowledge base in which concepts in a form of graph nodes are linked by semantic relationships in a form of graph edges;
encode in metadata of the edges and nodes of the semantic graph, weightings as feature vectors 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, wherein the feature vectors comprise an intensity of the meta-relationship and a confidence score of the intensity, and wherein the intensity uses runtime inputs for the nodes;
carry out a graph activation 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;
score the edges and nodes of the semantic graph in response to the graph activation;
seed the graph with seed weightings for measures of the meta-relationship obtained from a set of resources independent of the knowledge base on which the semantic graph is based; and
in response to additions to the set of resources independent of the knowledge base on which the semantic graph is based, update the seed weightings.

Assignees

• IBM

Inventors

• Mcateer Seamus R
• Mccloskey Daniel
• Mohan Aditya
• Sogrin Mikhail

Classifications

• G06F40/30 Primary

  Semantic analysis · CPC title

• G06N5/022 Primary

  Knowledge engineering; Knowledge acquisition · CPC title

• G06F16/3338 Primary

  Query expansion · CPC title

• G06N5/04

  Inference or reasoning models · CPC title

• G16H10/20

  for electronic clinical trials or questionnaires · CPC title