Ontology-Based Digital Model for Specification Document Retrieval
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Solution Overview
Problem
Standard specifications documents, such as those published by ASTM, ANSI, and ISO, are cumbersome to access and navigate, making it difficult to find specific information within large collections, as seen in the initial keyword search for 'STEEL', 'HEX', and 'BOLT' which yielded 71 documents with only 16 relevant to threaded steel fasteners, 13 for testing, and 42 unrelated.
Innovation Solution
Converting standard specifications into an ontology-based digital model (OBDM) that allows for automated dissemination of knowledge, using a structured syntactic textual model extracted from documents, stored in an RDF database, and queried via a user-friendly interface, enabling efficient retrieval and organization of data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If standard specifications are published as documents (PDF, Word), then the complete knowledge is preserved, but the documents become cumbersome to access and navigate
Solution Approach 1:
The patent segments the monolithic specification documents into structured data models with separate components for requirements, subjects, and governing authorities. This segmentation allows the complete knowledge to be preserved in the structured model while enabling users to access only relevant portions through targeted queries, resolving the contradiction between information preservation and ease of access.
Solution Approach 2:
The patent introduces an intermediary structured data model (ontology-based digital model) that sits between the original document formats and the user interface. This intermediary model preserves all knowledge from the source documents while providing structured access points for efficient querying and navigation, thus mediating between complete knowledge preservation and ease of access.
2Adaptability or versatility
If keyword search is used on specification documents, then broad coverage is achieved, but precision is lost with many irrelevant results
Solution Approach 1:
The patent applies local quality by creating distinct structural components within the data model for different types of information (requirements, subjects, governing authorities). Each component has specific query interfaces tailored to its nature, allowing users to search with high precision for specific information types while maintaining broad coverage across all specification documents through the unified model.
Solution Approach 2:
The patent transitions from one-dimensional keyword searching to multi-dimensional querying by organizing data into structured categories (requirements, subjects, governing authorities) with hierarchical relationships. This dimensional restructuring enables precise targeting of specific information types while maintaining comprehensive search coverage across the entire specification library.
3Loss of information
If all specification documents are made available, then completeness is achieved, but complexity of navigation increases
Solution Approach 1:
The patent segments the navigation interface into distinct functional areas corresponding to the structured data model components (requirements, subjects, governing authorities). This segmentation presents the complete information set in organized, manageable sections, reducing navigation complexity while maintaining information completeness through systematic access to all segments.
Solution Approach 2:
The patent adds structural dimensions to the navigation system by organizing documents into hierarchical categories and relationships within the ontology-based model. This dimensional organization transforms the flat, overwhelming document collection into a multi-layered structure with clear navigation paths, reducing complexity while preserving access to all information through the structured framework.
Data Source
AI summary
A system for storing and disseminating knowledge contained in documents includes a document annotator that creates a structured syntactic textual model of each of the documents, an ontology directed extractor that extracts properties from the textual models, a database for storing the textual models and the properties, and an interface permitting queries to the database. The document annotator includes a plurality of data transformers and a plurality of custom annotator tools. The ontology directed extractor includes an ontology based schema definition and a plurality of ontology based data transformers. The user interface includes the ability to view, search, navigate, create, and exchange documents. The creation feature includes a transclusion function.