Custom Ontology Integration for Semantic Search Efficiency
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Solution Overview
Problem
Existing semantic search systems face challenges in providing customized search experiences over commonly enriched corpora without the computational cost of rerunning natural language processing (NLP) over the entire corpus, as custom enrichment is costly and resource-intensive.
Innovation Solution
Integrating a custom ontology into the semantic search function, allowing users to view a commonly enriched corpus through their own custom lens by determining congruences between entities and relationships in the custom and separate ontologies, and using these congruences to support input queries, thereby providing a customized search experience without the need for reprocessing the entire corpus.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If custom ontology enrichment is performed on the entire corpus, then search customization and user-specific semantic understanding are improved, but computational cost and processing time increase significantly
Solution Approach 1:
The system segments the ontology enrichment process by maintaining a separate custom ontology mapping layer that operates independently from the base corpus enrichment. This allows the corpus to remain enriched with a base ontology while users can apply custom ontologies without reprocessing the entire corpus, thus reducing computational cost while maintaining search customization capability.
Solution Approach 2:
The system creates a universal base enriched corpus that serves multiple users with different custom ontologies. The base corpus enrichment is performed once and reused across multiple user sessions, while custom ontologies are overlaid on top. This multi-functional approach allows the same enriched corpus to support multiple customization scenarios without repeated processing.
2Measurement precision
If custom ontology enrichment is performed on the entire corpus, then user-specific semantic search accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary ontology mapping by pre-processing the corpus with a base ontology and pre-building the custom ontology mappings. These mappings are stored and reused during search operations. This preliminary action ensures semantic search accuracy is maintained while avoiding time-consuming reprocessing during actual search execution.
Solution Approach 2:
The system creates a virtual copy of the enrichment process through ontology mapping files that can be applied to the base enriched corpus. Instead of physically re-enriching the corpus for each custom ontology, the system copies and adapts the base enrichment through mapping transformations, significantly reducing processing time while maintaining semantic accuracy.
3Adaptability or versatility
If separate custom enriched corpora are created for multiple users, then individualized search experiences are provided, but system complexity and resource requirements increase
Solution Approach 1:
The system merges multiple custom ontologies into a unified base enriched corpus structure. Instead of maintaining separate physical corpora for each user, the system combines the base corpus enrichment with multiple custom ontology mappings that can be selectively applied. This reduces system complexity by consolidating resources while maintaining the ability to provide individualized search experiences through ontology selection.
Solution Approach 2:
The system introduces ontology mapping files as intermediaries between the base enriched corpus and user-specific custom ontologies. These mapping files act as a mediator layer that translates between the base corpus structure and various custom ontology schemas, enabling individualized search experiences without requiring separate enriched corpora for each user.
4Productivity
If the base ontology is used for all users, then system efficiency is maintained, but search customization and user-specific relevance are reduced
Solution Approach 1:
The system implements a dynamic ontology application mechanism where the base ontology provides the efficient foundation for all users, but custom ontologies can be dynamically applied based on user preferences, query context, or organizational requirements. This dynamic approach maintains system efficiency through the shared base enrichment while preserving the ability to inject custom semantic context when needed.
Solution Approach 2:
The system uses ontology mapping files as intermediaries that can be selectively applied to the base enriched corpus. These mappings preserve custom semantic context by providing an additional layer of semantic interpretation on top of the base ontology, without requiring replacement of the efficient base enrichment. The intermediary mappings are applied only when customization is required, maintaining system efficiency while preventing loss of custom semantic context.
Data Source
AI summary
Techniques include integrating a custom ontology into a semantic search function, the semantic search function being configured to perform a semantic search over a corpus enriched with a separate ontology. The semantic search function is executed using the custom ontology to perform the semantic search of the corpus. Results are generated from the semantic search of the corpus based on input received by the semantic search function.