Ontology Maintenance for Semantic Enterprise Search
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Solution Overview
Problem
Conventional search engines fail to achieve true semantic enterprise searching, as they do not effectively utilize unstructured data and relationships within enterprise repositories, limiting the context and relevance of search results.
Innovation Solution
The development of systems and methods for knowledge extraction and automatic ontology maintenance, which involve mapping application data to ontology classes, using information extraction logic to identify and update ontologies, and establishing semantic links to enhance search capabilities beyond traditional keyword searches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional search engines use traditional keyword searching, then the search process is simple and fast, but the relevance and accuracy of search results are limited
Solution Approach 1:
The patent introduces an ontology as an intermediary layer between the search query and the actual data in enterprise repositories. The ontology serves as a mediator that maps user queries to structured concepts and relationships, enabling semantic search while maintaining system manageability through standardized classification schemas.
Solution Approach 2:
The patent transitions search from a single-dimensional keyword matching approach to a multi-dimensional semantic search space. By adding the ontology dimension that captures relationships, hierarchies, and contextual meanings, the system achieves more accurate search results without overwhelming complexity.
2Adaptability or versatility
If the system populates ontologies with structured and unstructured data from enterprise repositories, then search semantic capability is improved, but the complexity of data processing and ontology maintenance increases
Solution Approach 1:
The patent creates a universal ontology framework that can handle multiple data types (structured and unstructured) from various enterprise repositories through a single integrated approach. The ontology serves multiple functions: classification, relationship mapping, search indexing, and semantic querying, reducing the need for separate processing systems.
Solution Approach 2:
The system implements automated ontology maintenance where the ontology population process automatically extracts, maps, and maintains ontological knowledge from enterprise data without requiring manual intervention. The system self-updates and self-maintains the ontology structure based on incoming data from repositories.
3Measurement precision
If the system extracts and consolidates knowledge from application data to maintain ontologies, then the accuracy of semantic search is improved, but the time and resources required for knowledge extraction and consolidation increase
Solution Approach 1:
The patent implements continuous ontology maintenance where knowledge extraction and consolidation occur continuously as data is ingested from enterprise repositories, rather than through periodic batch processing. This continuous action ensures the ontology remains accurate and up-to-date while distributing the time cost over time.
Solution Approach 2:
The system incorporates feedback mechanisms where the quality and relevance of extracted knowledge are continuously evaluated and used to refine future extraction processes. The ontology accuracy feedback loop allows the system to learn from search results and adjust its knowledge consolidation strategies to improve efficiency.
Data Source
AI summary
Systems, methods, and other embodiments associated with extracting knowledge from application data and maintaining an ontology based on the extracted knowledge are described. One example system includes a mapping logic to store mappings between application objects and ontology classes and an information extraction (IE) logic that accesses the mapping logic to identify application data to process based on the mappings. The application data may be stored in application data repositories belonging to an enterprise and may be characterized by the application object. Having identified application data to process, the IE logic may locate data in the application data repositories and selectively manipulate an ontology based on selected application data elements.