Overlay Graph Search for Heterogeneous Knowledge Graphs
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Solution Overview
Problem
Current AI applications face inefficiencies in searching multiple ontologic knowledge graphs separately, which is time-consuming and not optimized for heterogeneous data sources.
Innovation Solution
The method involves generating overlay graphs that map entities and relations to subsets of source knowledge graphs, allowing for a unified search across multiple graphs by translating search requests into knowledge graph specific queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If multiple ontologic knowledge graphs are searched separately, then comprehensive search coverage is achieved, but search time increases significantly
Solution Approach 1:
The patent merges multiple separate knowledge graph searches into a single unified search operation by creating an overlay graph that integrates multiple source knowledge graphs. The search system translates a single search request into coordinated queries across multiple source graphs simultaneously, combining results into a unified response, thereby reducing total search time while maintaining comprehensive coverage.
Solution Approach 2:
The overlay graph serves as an intermediary layer between the user's search request and multiple source knowledge graphs. This mediator translates high-level search requests into graph-specific queries, manages the coordination of parallel searches, and aggregates results, eliminating the need for users to manually search each graph separately.
2Productivity
If overlay graphs are generated to unify multiple knowledge graphs, then search efficiency improves, but system complexity increases
Solution Approach 1:
The system segments the complexity by separating concerns into distinct layers: the overlay graph layer handles unified search semantics, while the source knowledge graphs layer handles data storage and graph-specific operations. This segmentation allows each layer to be developed and maintained independently, reducing overall system complexity despite the integration benefits.
Solution Approach 2:
The overlay graph structure provides universal functionality across multiple heterogeneous knowledge graphs through standardized entity and relation mappings. By defining common interfaces and translation mechanisms, the system achieves multi-functionality where a single search framework can query diverse source graphs without requiring separate handling logic for each.
3Measurement precision
If search requests are translated to knowledge graph specific queries, then accurate results are obtained, but processing overhead increases
Solution Approach 1:
The system performs preliminary actions by pre-defining the overlay graph structure, entity mappings, and relation mappings before search execution. Translation rules and query templates are prepared in advance, allowing the system to quickly instantiate graph-specific queries during search operations without performing complex translations in real-time, thus maintaining accuracy while reducing processing overhead.
Data Source
AI summary
Provided are techniques for a search using an overlay graph mapping to source knowledge graphs. A plurality of overlay graphs are generated, where each overlay graph comprises entities represented by nodes and relations represented by edges, and where the entities and the relations map to a subset of entities and relations in a plurality of source knowledge graphs. A search request comprising an entity and a relation is received. An overlay graph is selected from the plurality of overlay graphs based on the entity and the relation. The search request is issued against the overlay graph, where the search request is translated to knowledge graph specific queries, and where the knowledge graph specific queries are issued against the plurality of source knowledge graphs. Search results are received from the plurality of source knowledge graphs. The search results are used to respond to the search request.