Valuation Logic and VC Investment Strategies for AI Startups: A Knowledge Graph-Based Approach

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Published: 2026-02-22
Keywords:
AI Startups, Venture Capital, Valuation, Knowledge Graph, Investment Strategy, Due Diligence, Artificial Intelligence

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Julian S. Thorne
Drexel University, Philadelphia, PA, US
Hao-Ran Chen
Temple University, Philadelphia, PA, USA

Abstract

This review paper explores the intersection of valuation logic and venture capital (VC) investment strategies within the domain of Artificial Intelligence (AI) startups, employing a knowledge graph-based approach. Traditional valuation techniques often fall short in capturing the unique characteristics and inherent uncertainties associated with AI ventures, including rapidly evolving technologies, data dependencies, and talent scarcity. A knowledge graph provides a structured framework for integrating diverse data sources, representing complex relationships between entities (e.g., technology, teams, market trends), and facilitating nuanced valuation assessments. The paper synthesizes existing literature on AI startup valuation, VC decision-making, and knowledge graph applications, identifying key valuation drivers and investment criteria. We examine how knowledge graphs can enhance due diligence processes, support dynamic risk assessment, and improve the accuracy of financial projections. Specifically, we investigate applications to forecasting AI’s impact on different sectors. We explore the strategic implications for VC firms seeking to capitalize on the growth potential of AI, discussing how they can leverage knowledge graphs to identify promising startups, optimize investment portfolios, and generate superior returns. The review addresses the challenges and limitations of adopting a knowledge graph-based approach, including data quality issues, computational complexity, and the need for specialized expertise. Finally, it highlights future research directions, such as the development of automated valuation models and the integration of explainable AI (XAI) techniques to enhance transparency and trust in VC investment decisions. Ultimately, this review argues that a knowledge graph-based approach offers a powerful tool for navigating the complexities of AI startup valuation and informing more effective VC investment strategies, creating win-win outcomes for investors, entrepreneurs and society.

Article Details

Issue

Vol. 2 No. 1 (2026)

Section

Articles

How to Cite

Valuation Logic and VC Investment Strategies for AI Startups: A Knowledge Graph-Based Approach. (2026). Journal of Science, Innovation & Social Impact, 2(1), 146-154. https://sagespress.com/index.php/JSISI/article/view/90

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