Organization-Level Ontology Generation for Knowledge Retrieval
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Solution Overview
Problem
The management and retrieval of digital documentation within organizations are inefficient due to redundancy and the complexity of searching for specific information, as existing tools rely heavily on indexes, tags, or large data models, which can be challenging to implement at an organizational level.
Innovation Solution
A system and method for automatically generating an organization-level ontology that includes an input/output unit, memory unit, ontology generation unit, and knowledge retrieval system, which extracts nodes and relationships from documents, performs syntactic, semantic, and pragmatic assessments, and integrates refined document ontologies to facilitate efficient knowledge retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If indexes, tags, or labels are used to retrieve information, then information retrieval can be facilitated, but implementation at organizational level becomes challenging and inefficient
Solution Approach 1:
The system enables self-service information retrieval by automatically generating ontologies from document collections without requiring manual curation or complex configuration. The ontology generation process is autonomous, extracting entities, relationships, and hierarchies directly from the documents themselves, making the system easy to implement at organizational level
Solution Approach 2:
The system transforms information retrieval from traditional keyword/tag-based approaches to ontology-based semantic search. By changing the fundamental parameter from simple text matching to structured semantic relationships, the system achieves both ease of operation and scalability at organizational level
2Measurement precision
If vast amounts of data are used to generate models for associating documents with queries, then information retrieval accuracy improves, but system complexity and resource requirements increase
Solution Approach 1:
The system performs preliminary action by automatically generating ontologies from document collections before retrieval operations. This pre-processing step creates structured knowledge representations (entities, relationships, hierarchies) that enable accurate semantic search without requiring complex real-time processing or vast training data
Solution Approach 2:
The ontology serves as an intermediary between documents and queries. Instead of directly matching queries against vast document collections, the system uses the generated ontology as a mediator that captures semantic relationships, thereby improving retrieval accuracy while reducing system complexity
3Ease of operation
If digital documents are stored repeatedly in multiple repositories, then document accessibility is improved, but searching for specific information becomes complex and inefficient
Solution Approach 1:
The system segments the distributed document repositories into structured ontology components (entities, relationships, hierarchies). By organizing information from multiple repositories into a unified ontological framework, the system maintains document accessibility while enabling efficient semantic search across the entire organization
Solution Approach 2:
The generated ontology serves as a universal index that works across all document repositories simultaneously. This multi-functional structure enables the system to handle accessibility requirements for multiple repositories while providing consistent, efficient search capabilities across the entire organization
Data Source
AI summary
A system and method for automatically generating organization level ontology for knowledge retrieval, are provided. An input/output unit receives a plurality of documents from document sources and an ontology generation system generates the organization level ontology based on the documents. The ontology generation system extracts one or more nodes and directed relationships from each document and generates an intermediate document ontology for each document. A combination of syntactic, semantic, and pragmatic assessment of intermediate document ontology is performed to assess at least structure and adaptability of the ontology. The ontology generation system further generates a refined document ontology, based on assessment, to satisfy one or more quality metrics. Each of the refined document ontologies is integrated together to generate the organization level ontology. Further, a knowledge retrieval system is operatively coupled to the ontology generation system and processes one or more search queries using the generated organization level ontology.