Development of an ML-Based Text Analysis and Knowledge Graph Platform for Manufacturing Sector

The client struggles with the exponential growth of diverse unstructured textual data, including patents, standards, scientific papers, and internal reports, which hampers efficient knowledge retrieval and decision-making. Manual processing is time-consuming and prone to oversight, impacting areas such as compliance, innovation, and product lifecycle management.

A mid-to-large manufacturing enterprise aiming to enhance product management, compliance, R&D, and legal analysis by leveraging advanced semantic text processing and knowledge visualization tools.

• Create an automated system capable of processing varied unstructured document formats to generate a comprehensive knowledge graph representing their content.
• Implement semantic similarity and topic modeling to facilitate intelligent document clustering and retrieval.
• Enable smart search capabilities that allow users to find relevant documents quickly based on context and content similarity.
• Visualize knowledge graphs to support rapid comprehension of document relationships and content clusters.
• Reduce manual document processing time and improve accuracy of knowledge extraction, aiming to minimize research and analysis timeframes.
• Support compliance monitoring, R&D insights, and legal assessments by providing reliable, machine-readable structured data from complex textual sources.

• Multi-format document ingestion including text files, spreadsheets, presentations, and PDFs.
• Machine learning-powered topic modeling to identify core themes and clusters within document collections.
• Semantic similarity scoring to assess relatedness between documents and within document sets.
• Automated keyword extraction and recurrence analysis to identify essential terms and concepts.
• Knowledge graph generation to visually represent relationships and content structure.
• Smart search functionality using semantic understanding to enable B2B search scenarios similar to web search engines.
• Visual interface for intuitive browsing and exploration of knowledge graphs.

• Enterprise Document Management Systems for seamless document ingestion.
• External data sources such as scientific repositories, standards libraries, and patent databases.
• Existing compliance and legal tracking tools for cross-referenced analysis.
• User authentication and role-based access control systems.

• Scalability to process and analyze increasing volumes of documents with minimal performance degradation.
• Response time for semantic search queries under 2 seconds for large datasets.
• High accuracy in keyword extraction and similarity assessments, with an accuracy target of over 85%.
• Data security and compliance with relevant data protection standards.
• Highly available system architecture with 99.9% uptime.

The implementation of this system is expected to significantly reduce manual document processing time, while increasing the accuracy and comprehensiveness of knowledge retrieval. It will enable faster decision-making in compliance, R&D, and legal contexts, supporting better product lifecycle management and innovation initiatives. The platform aims to improve operational efficiency and provide strategic insights by visualizing complex document relationships, ultimately contributing to a more agile and informed manufacturing process.