17963282. Knowledge Graph Rule Induction simplified abstract (INTERNATIONAL BUSINESS MACHINES CORPORATION)
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 17963282 titled 'Knowledge Graph Rule Induction
Simplified Explanation
The patent application describes mechanisms for automated rule set generation for identifying relations in knowledge graph data structures.
- The input knowledge graph is processed to extract tuples representing relations between entities.
- A set of rules is generated based on heuristics applied to tuples.
- Candidate rules are identified for adding to the set of rules.
- A linear programming computer model evaluates the modified set of rules to determine if adding candidate rules improves the objective function.
- The set of rules is expanded to include candidate rules if the evaluation indicates improvement in the objective function.
Potential Applications
This technology could be applied in various fields such as data analysis, machine learning, and artificial intelligence for improving relation identification in knowledge graphs.
Problems Solved
This technology solves the problem of manual rule generation for identifying relations in knowledge graph data structures, making the process more efficient and accurate.
Benefits
The benefits of this technology include automated rule generation, improved accuracy in relation identification, and increased efficiency in knowledge graph analysis.
Potential Commercial Applications
Potential commercial applications of this technology could include data mining software, knowledge graph tools, and AI-driven analytics platforms.
Possible Prior Art
One possible prior art for this technology could be the use of machine learning algorithms for relation extraction in text data.
Unanswered Questions
How does this technology compare to existing rule generation methods for knowledge graphs?
This article does not provide a direct comparison to existing rule generation methods for knowledge graphs. It would be interesting to see a side-by-side comparison of the efficiency and accuracy of this technology compared to traditional manual rule generation methods.
What are the limitations of the linear programming computer model in evaluating the candidate rules?
The article does not delve into the potential limitations of the linear programming computer model in evaluating candidate rules. It would be beneficial to understand any constraints or challenges faced by the model in determining the effectiveness of adding candidate rules to the set.
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.
