Technology Mapping with Large Language Models

2025-01-25

AI Summary

Traditional keyword-based approaches for enterprise technology stack identification struggle with emerging technologies, semantic ambiguity, and cross-industry heterogeneity. To address this, we propose STARS (Semantic-Driven Cross-Industry Technology Mapping System), an end-to-end framework integrating Chain-of-Thought prompting, Sentence-BERT–based dense semantic retrieval, and named entity recognition to accurately extract, semantically link, and rank technology entities from unstructured text. Compared to baseline methods, STARS significantly improves accuracy and coverage while reducing noise, enabling high-fidelity, interpretable technology profiling across diverse industrial domains. Its core innovation lies in the synergistic modeling of reasoning guidance (CoT) and dense semantic retrieval—supporting both discovery of novel technologies and operational-level importance assessment. STARS demonstrates strong generalizability and readiness for industrial-scale deployment.

Technology Category

• Natural Language Processing: Information Extraction
• Data Mining & Knowledge Management: Semantic Web
• Knowledge Representation and Reasoning: Ontologies

Application Category

• Semantics and Knowledge: Applications of semantic technologies for improving search, browsing, recommendation, personalization
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

In today's fast-evolving business landscape, having insight into the technology stacks that organizations use is crucial for forging partnerships, uncovering market openings, and informing strategic choices. However, conventional technology mapping, which typically hinges on keyword searches, struggles with the sheer scale and variety of data available, often failing to capture nascent technologies. To overcome these hurdles, we present STARS (Semantic Technology and Retrieval System), a novel framework that harnesses Large Language Models (LLMs) and Sentence-BERT to pinpoint relevant technologies within unstructured content, build comprehensive company profiles, and rank each firm's technologies according to their operational importance. By integrating entity extraction with Chain-of-Thought prompting and employing semantic ranking, STARS provides a precise method for mapping corporate technology portfolios. Experimental results show that STARS markedly boosts retrieval accuracy, offering a versatile and high-performance solution for cross-industry technology mapping.

Problem

• Keyword Search
• Complex Information Handling
• Emerging Technologies

Innovation

• STARS system
• Large Language Model
• Sentence-BERT