Semantic Relational Database Operations Using Vector Space Models
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Solution Overview
Problem
Current database operators only support value-based matching, failing to account for semantic proximity, which limits the ability to match entities based on their context and relationships.
Innovation Solution
Introducing semantic relational operators that utilize vector space models generated by neural networks for executing core relational operations like select, project, and join, enabling distance-based similarity measures for matching entities by their semantics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If value-based matching is used in database operators, then exact value matches can be achieved, but semantic proximity and contextual relationships are not accounted for
Solution Approach 1:
The patent transforms the matching parameter from exact value equality to distance-based similarity measurement in a vector space. Database records are represented as vectors, and matching is performed by computing distances between query vectors and record vectors, allowing semantic proximity to be captured while maintaining precise control over matching criteria through distance thresholds
Solution Approach 2:
The patent introduces a vector space dimension to represent semantic relationships. Instead of matching in the traditional scalar value domain, records and queries are embedded in a multi-dimensional vector space where geometric distance reflects semantic similarity, enabling contextual matching beyond exact value equality
2Adaptability or versatility
If semantic relational operators are introduced, then entities can be matched based on semantic relationships, but system complexity increases
Solution Approach 1:
The patent introduces a vector space model as an intermediary layer between the traditional database system and the query interface. This intermediary represents semantic relationships geometrically, allowing complex semantic matching to be performed through relatively simple distance computations, thereby managing system complexity while enhancing semantic matching capability
Solution Approach 2:
The patent replaces traditional mechanical value-based matching operations with geometric distance-based operations in a vector space. This substitution allows semantic relationships to be handled through mathematical vector operations rather than complex rule-based systems, simplifying the overall system architecture while enabling semantic matching
Data Source
AI summary
A method, computer program product and/or system is disclosed. According to an aspect of this invention, one or more processors receive a query of a first database, where the query includes: (i) an operand, and (ii) an operator indicating a distance-based similarity measure. One or more processors further determine a result set based on the query, wherein the result set includes a plurality of records, and wherein a record is included in the result set based on a vector nearest-neighbor computation between: (i) a first vector corresponding to the operand, and (ii) a second vector corresponding to the record, wherein the second vector is included in a vector space model that is based on a textual representation of the first database.