Abstract

This research paper presents a novel machine learning approach for automated knowledge acquisition from unstructured data sources. The proposed model combines natural language processing, deep learning, and knowledge graph techniques to extract, structure, and represent domain knowledge efficiently.

Core Components

Neural Architecture

Advanced transformer-based models for semantic understanding and entity relationship extraction from text.

Knowledge Graphs

Structured representation of domain knowledge enabling efficient querying and reasoning capabilities.

Data Processing

Scalable pipelines for ingesting and processing large volumes of unstructured financial data.

Inference Engine

Real-time reasoning and decision-making based on acquired knowledge and contextual information.

Key Research Contributions

Hybrid Learning Approach

A novel hybrid model combining supervised, unsupervised, and reinforcement learning techniques for comprehensive knowledge acquisition across diverse domains.

Contextual Entity Recognition

Advanced named entity recognition system that understands domain-specific terminology and contextual relationships in financial services.

Automated Ontology Construction

Machine learning-driven approach to automatically build and update domain ontologies from new data sources.

Performance Optimization

Novel techniques for reducing computational complexity while maintaining high accuracy in knowledge extraction and representation.

Applications in Financial Services

The knowledge acquisition model has been successfully applied to several real-world use cases in banking and financial services:

Intelligent Customer Support

Automated extraction of product knowledge and policies enables AI assistants to provide accurate answers to customer queries.

Regulatory Compliance

Continuous monitoring and understanding of regulatory changes for automated compliance checking.

Risk Assessment

Knowledge graphs of customer relationships and transaction patterns enable sophisticated fraud detection.

Product Recommendations

Deep understanding of product features and customer needs powers personalized recommendation engines.

Development of a Machine Learning Model for Knowledge Acquisition