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2 articles for “Knowledge Graph Alignment”
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Deep Learning Approaches for Graph Similarity Computation: A Review
Abstract: Graph-structured data is increasingly prevalent in many real-world applications such as social networks, biological systems, molecular chemistry, and knowledge graphs. Measuring the similarity between graphs is a fundamental task in graph analytics, enabling applications including pattern recognition, recommendation systems, network alignment, and drug discovery. Traditional approaches for graph similarity computation, such as Graph Edit Distance and graph kernels, provide effective structural comparison but often suffer from high computational complexity and …
Published in Journal of Computer Technology & Applications · Vol. 17, Issue 2, 2026 Read article
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Neuro-Symbolic Agentic AI for Autonomous Scientific Discovery: Integrating Deep Reinforcement Learning, Quantum Simulation, and XAI-Audited LLM Hypothesis Generation in Drug Target Identification
Abstract: The exponential growth of multi-omics data and the increasing complexity of disease-associated protein interactomes have rendered conventional drug target identification pipelines computationally and epistemologically inadequate. This paper presents the Neuro-Symbolic Agentic AI for Scientific Discovery (NS-AASD) framework, a unified architecture that cohesively integrates deep reinforcement learning (DRL) exploration strategies, variational quantum simulation (VQS) of protein conformational dynamics, and XAI-audited large language model (LLM) hypothesis generation within an autonomous scientific discovery …
Published in Research and Reviews : Journal of Computational Biology · Vol. 15, Issue 2, 2026 · pp. 15–24 Read article