International business machines corporation (20240135205). Knowledge Graph Rule Induction simplified abstract
Contents
- 1 Knowledge Graph Rule Induction
Knowledge Graph Rule Induction
Organization Name
international business machines corporation
Inventor(s)
Sanjeeb Dash of Croton on Hudson NY (US)
Joao P. Goncalves of Wappingers Falls NY (US)
Knowledge Graph Rule Induction - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240135205 titled 'Knowledge Graph Rule Induction
Simplified Explanation
The patent application describes mechanisms for automated rule set generation to identify relations in knowledge graph data structures. Here is a simplified explanation of the abstract:
- An input knowledge graph is analyzed to extract tuples representing relations between entities.
- Rules are generated based on heuristics applied to tuples.
- Candidate rules are identified for potential addition to the rule set.
- A linear programming model evaluates the modified rule set to determine if adding candidate rules improves the model's objective function.
- The rule set is expanded to include candidate rules if the evaluation indicates improvement.
Potential Applications
The technology described in the patent application could be applied in various fields such as:
- Data analysis
- Knowledge graph construction
- Machine learning
Problems Solved
This technology addresses the following issues:
- Automating rule generation for knowledge graph analysis
- Improving the efficiency of identifying relations between entities
- Enhancing the accuracy of data processing
Benefits
The benefits of this technology include:
- Streamlining the process of rule set generation
- Increasing the effectiveness of knowledge graph analysis
- Enhancing decision-making based on extracted relations
Potential Commercial Applications
With its capabilities, this technology could be valuable in industries such as:
- Data analytics
- Artificial intelligence
- Information retrieval
Possible Prior Art
One possible prior art related to this technology is the use of linear programming models in data analysis and optimization processes.
Unanswered Questions
How does this technology compare to existing methods of rule set generation for knowledge graph analysis?
This article does not provide a direct comparison with other methods or technologies in the field.
What are the specific heuristics used in the generation of rules based on tuples extracted from the input knowledge graph?
The article does not delve into the specific heuristics employed in the rule generation process.
Original Abstract Submitted
mechanisms are provided for automated rule set generation for identifying relations in knowledge graph data structures. an input knowledge graph is processed to extract tuples representing relations between entities present in the input knowledge graph. a set of rules is generated based on one or more heuristics applied to tuples, and candidate rule(s) are identified that are candidates for adding to the set of rules. a linear programming computer model is evaluated for a modified set of rules comprising the set of rules and the candidate rule(s) to determine whether or not adding the candidate rule(s) improves an objective function of the linear programming model. the set of rules is expanded to include the candidate rule(s) in response to the evaluation of the linear programming computer model indicating that the addition of the candidate rule(s) improves the objective function of the linear programming computer model.
