Automated Ontology Updates via User Tagging
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Solution Overview
Problem
The construction and maintenance of ontologies for data access are tedious and time-consuming, requiring significant human expertise and are often specific to particular information domains, making it difficult to efficiently access and harmonize data from diverse sources.
Innovation Solution
A method and system that automatically updates ontologies based on user tags and identifies commonalities between multiple ontologies to build a super-ontology, enabling efficient data access and harmonization across domains by propagating elements and relationships, and using deep Web data sources to enhance data extraction and aggregation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ontologies are constructed manually by human experts, then the quality and accuracy of the ontology are improved, but the time and effort required for construction increase significantly
Solution Approach 1:
The system enables automatic ontology construction by allowing the ontology to update itself through user tagging interactions. Users tag data elements while performing normal search operations, and the system automatically processes these tags to refine the ontology without requiring manual expert intervention, thus achieving self-service ontology maintenance
Solution Approach 2:
The system implements a feedback mechanism where user tagging behavior is continuously monitored and fed back into the ontology construction process. The ontology is automatically updated based on aggregated user tags, creating a closed-loop system that progressively improves ontology quality through real-world usage data
2Measurement precision
If ontologies are made domain-specific, then the precision of data access in that domain is improved, but the complexity of creating and maintaining multiple ontologies increases
Solution Approach 1:
The system creates a universal ontology framework that can serve multiple information domains simultaneously. By building a super-ontology from commonalities across domains and allowing automatic specialization through user tagging, a single system structure handles diverse domains without requiring separate manual ontology construction for each, thus achieving multi-functionality
Solution Approach 2:
The ontology system is segmented into a hierarchical structure with a super-ontology containing common elements across domains and domain-specific ontologies that inherit from it. This segmentation allows each domain to have its own specialized vocabulary while sharing the common framework, reducing the overall complexity of managing multiple ontologies
3Productivity
If automatic updates are implemented, then the productivity of ontology maintenance is improved, but the reliability of the ontology may deteriorate due to potential errors in automated processes
Solution Approach 1:
The system performs self-service ontology maintenance by automatically processing user tags to update the ontology. This eliminates the need for manual expert review of every change while maintaining reliability through the collective wisdom of multiple user taggings that converge on accurate classifications
Solution Approach 2:
The system merges multiple user taggings for the same data element to determine the most accurate ontology classification. By aggregating and combining user inputs, the system achieves higher reliability through consensus while maintaining high productivity through automated processing of the aggregated data
Data Source
AI summary
A method for data access includes defining an ontology (26) pertaining to a given sphere of knowledge. A computer (22) receives a search query generated using the ontology and provides to a user of the computer at least one document in response to the query. The computer receives tags that the user has associated with data elements in the at least one document and automatically updates the ontology responsively to the tags.