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dc.contributorDepartment of Computingen_US
dc.contributor.advisorHuang, Xiao (COMP)en_US
dc.contributor.advisorLi, Qing (COMP)en_US
dc.creatorChen, Shengyuan-
dc.identifier.urihttps://theses.lib.polyu.edu.hk/handle/200/13406-
dc.languageEnglishen_US
dc.publisherHong Kong Polytechnic Universityen_US
dc.rightsAll rights reserveden_US
dc.titleKnowledge graph reasoning : algorithms and applicationsen_US
dcterms.abstractKnowledge graphs (KGs) serve as powerful tools in various domains by capturing and organizing complex relationships between entities. Yet, despite their utility, KGs often remain incomplete, limiting their full potential. KG completion and KG alignment emerge as critical solutions to fill these gaps, and at the heart of both lies KG reasoning. Traditional reasoning methods, whether neural or symbolic, often falter when tasked with large-scale KGs due to challenges in scalability and interpretability. Hence, advancing the efficiency and effectiveness of KG reasoning algorithms is paramount for unlocking their broader applications.en_US
dcterms.abstractThis thesis presents several groundbreaking approaches designed to enhance KG reasoning. First, we introduce DiffLogic, a differentiable neuro-symbolic reasoning framework that fuses the strengths of symbolic rule-based inference with the flexibility of neural networks. By modeling the joint probability of truth scores through weighted logic rules embedded within a Markov random field, DiffLogic achieves efficient rule grounding, allowing it to perform scalable inference over expansive KGs with precision. Building upon this, we propose NeuSymEA, a robust entity alignment method that bridges the gap between symbolic and neural reasoning through variational inference. NeuSymEA is uniquely equipped to operate in low-resource environments, aligning entities even when limited labeled data is available. Through logic decomposition and deduction, NeuSymEA scales symbolic reasoning to handle long rule inferences, all while ensuring interpretability and improving precision in alignment tasks. Lastly, we introduce LLM4EA, a label-free entity alignment framework that harnesses the power of large language models (LLMs) to address noisy annotations. By actively selecting key entities for annotation and refining label accuracy through probabilistic reasoning, LLM4EA enhances alignment robustness, minimizing the effect of noise while maximizing performance.en_US
dcterms.abstractThe research presented in this thesis pushes the boundaries of what’s possible in KG reasoning, offering scalable and efficient solutions for constructing and exploiting large KGs. The advancements presented in this work open new doors for robust and interpretable applications across fields such as information retrieval, social computing, health informatics, and bioinformatics, enabling the extraction of deeper insights from the vast and interconnected data that knowledge graphs offer.en_US
dcterms.extentxiv, 95 pages : color illustrationsen_US
dcterms.isPartOfPolyU Electronic Thesesen_US
dcterms.issued2024en_US
dcterms.educationalLevelPh.D.en_US
dcterms.educationalLevelAll Doctorateen_US
dcterms.LCSHKnowledge representation (Information theory)en_US
dcterms.LCSHArtificial intelligenceen_US
dcterms.LCSHReasoningen_US
dcterms.LCSHHong Kong Polytechnic University -- Dissertationsen_US
dcterms.accessRightsopen accessen_US

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Please use this identifier to cite or link to this item: https://theses.lib.polyu.edu.hk/handle/200/13406