Semantic Table Embeddings for Heterogeneous Data Retrieval
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing data unification methods, such as integrated schemas and ontologies, are inflexible and inefficient for finding compatible data across disparate data stores due to privacy and performance considerations, requiring anticipation of user needs and lacking flexibility.
Innovation Solution
A process using a trained document embedding model generates table embeddings and query embeddings, identifying responsive tables with similarity scores above a threshold, and computes compatibility and uniqueness scores to provide relevant data across heterogeneous datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If integrated schemas and ontologies are used for data unification, then data compatibility across disparate data stores is improved, but system flexibility and adaptability deteriorate due to rigid pre-defined structures
Solution Approach 1:
The patent replaces rigid mechanical schema-based data unification with embedding-based semantic representation. Instead of relying on pre-defined integrated schemas, the system uses document embeddings to capture semantic meaning, enabling flexible data matching across heterogeneous sources without rigid structural constraints.
Solution Approach 2:
The patent changes the fundamental parameter of data representation from structured schema-based formats to embedding vectors. This parameter transformation allows the system to adapt to any data structure by capturing semantic essence through embeddings, thereby improving flexibility while reducing the complexity of maintaining integrated schemas.
2Productivity
If traditional data unification methods are used, then data organization is improved, but retrieval efficiency and user need anticipation deteriorate
Solution Approach 1:
The patent performs preliminary action by pre-computing and storing embeddings for all dataset descriptions before queries arrive. This allows the system to quickly match new queries against pre-indexed embeddings without needing to process and interpret data structures in real-time, significantly improving retrieval efficiency.
Solution Approach 2:
The patent substitutes traditional mechanical search methods (based on schema matching and pre-defined ontologies) with semantic embedding-based retrieval. This substitution enables the system to understand and match queries based on semantic meaning rather than rigid structural patterns, improving both efficiency and adaptability to user needs.
3Adaptability or versatility
If schema-based metadata is used for data description, then data structure organization is improved, but ability to handle heterogenous data across different catalogs deteriorates
Solution Approach 1:
The patent introduces embedding vectors as an intermediary representation layer between raw heterogeneous data and query requirements. This intermediary transformation converts diverse data structures from multiple catalogs into a unified semantic space, enabling consistent handling and comparison of heterogenous data without requiring complex metadata normalization.
Solution Approach 2:
The patent changes the metadata representation parameter from structured schema descriptions to embedding vectors. This parameter change eliminates the complexity of handling heterogeneous metadata formats by transforming all data descriptions into a unified vector representation that captures semantic meaning, thereby simplifying the system's ability to handle data from different catalogs.
Data Source
AI summary
An embodiment generates, using a trained document embedding model, a plurality of table embeddings, each table embedding in the plurality of table embeddings representing a table in a dataset. An embodiment generates, using the trained document embedding model, a query embedding, the query embedding representing a natural language query regarding the dataset. An embodiment identifies a set of responsive tables, the set of responsive tables comprising a first table in the dataset, the first table in the dataset represented by a first table embedding, the first table embedding and the query embedding having a similarity score above a similarity score threshold.