International business machines corporation (20240126767). INTERPRETABILITY OF RESULTS FROM COGNITIVE SEMANTIC CLUSTERING QUERIES OVER RELATIONAL DATBASES simplified abstract

INTERPRETABILITY OF RESULTS FROM COGNITIVE SEMANTIC CLUSTERING QUERIES OVER RELATIONAL DATBASES

Organization Name

international business machines corporation

Inventor(s)

Apoorva Nitsure of Pittsburgh PA (US)

Rajesh Bordawekar of Westchester NY (US)

INTERPRETABILITY OF RESULTS FROM COGNITIVE SEMANTIC CLUSTERING QUERIES OVER RELATIONAL DATBASES - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240126767 titled 'INTERPRETABILITY OF RESULTS FROM COGNITIVE SEMANTIC CLUSTERING QUERIES OVER RELATIONAL DATBASES

Simplified Explanation

The patent application abstract describes a system and method for interpreting results of a semantic clustering structured query language (SQL) cognitive intelligence (CI) query by identifying dominant traits of the query input to determine a ranking of query results.

• The system includes a memory storing computer executable components and a processor executing these components, including an interpretability component that identifies dominant traits of the query input by analyzing influential tokens based on data statistics and observing dominant traits in influential tokens of the query output.
• The interpretability component can also incorporate co-occurrence measurements to further identify dominant traits of the query input.

Potential Applications

This technology could be applied in various industries such as data analytics, information retrieval systems, and artificial intelligence research.

Problems Solved

This technology helps in improving the accuracy and relevance of query results in semantic clustering SQL CI queries by identifying dominant traits of the query input.

Benefits

The system provides a more efficient and effective way of interpreting query results, leading to better decision-making processes and improved user experience.

Potential Commercial Applications

One potential commercial application of this technology could be in developing advanced search engines that provide more relevant and accurate results to users.

Possible Prior Art

One possible prior art could be the use of machine learning algorithms to improve query result interpretation in data analysis systems.

How does this technology compare to existing methods of query result interpretation in terms of accuracy and efficiency?

This article does not provide a direct comparison with existing methods of query result interpretation.

What are the potential limitations or challenges in implementing this technology in real-world applications?

The article does not address any potential limitations or challenges in implementing this technology.

Original Abstract Submitted

one or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to a process to interpret results of a semantic clustering structured query language (sql) cognitive intelligence (ci) query. a system can comprise a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory, wherein the computer executable components can comprise an interpretability component that can identify dominant traits of a query input to determine a ranking of query results by identifying influential tokens of the query input based on data statistics and observing the dominant traits in influential tokens of a query output. in one or more embodiments, the interpretability component can identify dominant traits of the query input by incorporating co-occurrence measurements.