Thesis Proposal: Uncertainty in Knowledge Graph Embeddings (2025)

Abstract: Knowledge Graph Embedding (KGE) methods are widely used to map entities and relations from knowledge graphs (KGs) into continuous vector spaces, enabling non-classical reasoning over knowledge structures. Despite their effectiveness, the uncertainty of KGE methods has not been extensively studied in the literature. This gap poses significant challenges, particularly when deploying KGE models in high-stakes domains like medicine, where reliability and risk assessment are critical. This dissertation seeks to investigate various types of uncertainty in KGE methods and explore strategies to quantify, mitigate, and reason under uncertainty effectively. The outcomes of this research will contribute to enhancing the reliability of KGE methods, providing greater confidence in their use beyond benchmark datasets, and supporting their application in real-world, high-stakes domains.

## introduction knowledge graphs (kgs) encode factual knowledge about real-world entities and their relationships, represented as @xmath0, predicate, tail entity>. these structures provide semantically rich information, playing a crucial role in advancing intelligent systems @xcite . ontologies and logic rules, as standard knowledge representation formalisms, are commonly used to reason about the semantics in kgs @xcite . however, management and updating of rules can be cumbersome and the inherently symbolic nature of such systems complicates their integration with machine learning tasks. knowledge graph embedding (kge) methods map entities and predicates into numerical vectors (a.k.a embeddings), providing non-classical reasoning capability by exploiting similarities and analogies over knowledge structure @xcite . while kge methods have demonstrated effectiveness in various downstream tasks, including triple classification @xcite , link prediction @xcite and recommendation @xcite , the uncertainty of kge methods remains largely under-explored. handling uncertainty in kge methods is critical because kge models often encounter significant uncertainty in their predictions (predictive uncertainty) @xcite . this predictive uncertainty can stem from several procedures throughout the kge pipeline shown in figure 1 . during kg construction, noise and errors may arise from inconsistent or ambiguous data aggregated from multiple sources @xcite , or from inaccurate automated knowledge extraction processes @xcite . additionally, some knowledge is inherently uncertain, such as molecular interactions, which are random process by nature @xcite . this uncertainty, associated with kgs before training the kge model, is referred to as knowledge uncertainty. furthermore, algorithmic uncertainty can emerge during model development, caused by randomness and variability in the kge training process. understanding and dealing with these types of uncertainty is especially critical in high-stakes domains such as medicine, where reliable predictions and robust risk assessment are imperative. despite the relevance, research on uncertainty in kge methods remains limited. for instance, studies by @xcite ; @xcite ; @xcite model algorithmic uncertainty and predictive uncertainty using probabilistic embeddings. while these approaches have improved overall accuracy, the quality of the modeled uncertainty has not been systematically studied. moreover, these methods often demand additional parameters, incur high computational costs due to the need for calculating distance between probability distribution, and are challenging to adapt to other kge methods image generated by flourish: https://flourish.studio/ , logo generate by flaticon: https://www.flaticon.com/ knowledge graph construction (1) noise and error knowledge graph embedding development knowledge graph embedding deployment (2) inherent randomness knowledge uncertainty algorithmic uncertainty predictive uncertainty (1) initialization entity 1 predicate 1 batch 1 negative batch batch n negative batch … … … (2) sequence of triples … … (3) negative sampling … model 1 model 2 random seed 1 random seed 2 pre-traind kge model query: <bob, hasdisease, ?> 1: pneumonia 2: bronchitis 3: tuberculosis 4: lungcancer … 1.4 0.9 0.7 0.5 … ranking of possible answers plausibility score without substantial modifications. to address these gaps, this dissertation plans to systematically explore various types of uncertainty in kge methods and aim to propose modelagnostic and easy-to-implement approaches to deal with uncertainty. the remainder of this dissertation proposal is structured as follows: section 2 provides an overview of kge methods and related work relevant to this research. section 3 details the research questions and the proposed methodologies to address them. section 4 concludes the proposal and outlines the anticipated contributions. ## research questions the primary objective of this dissertation is to systematically investigate various types of uncertainty in kge methods and to develop model-agnostic approaches for effectively managing them. specifically, this work focuses on the following research questions: rq1: for the reducible component of predictive uncertainty caused by algorithmic uncertainty, how can we effectively reduce it? rq2: for the irreducible component of predictive uncertainty, how can we reliably quantify it with statistical guarantees? rq3: when knowledge uncertainty is explicitly present in the input kgs, how can kge methods effectively and efficiently reason under such uncertainty? in this section, i will elaborate on each research question, introduce sub-research questions, outline tentative solutions, and describe the preliminary results or the expected contributions for each. ## conclusion in summary, this research seeks to address the critical yet underexplored challenge of uncertainty in kge methods. by investigating knowledge, algorithmic, and predictive uncertainty, the dissertation aims to enhance the reliability of kge methods, particularly in high-stakes applications. the anticipated contributions include novel methodologies and theoretical insights for reducing, quantifying and reasoning under uncertainty. these advancements will not only bridge significant gaps in current research but also support the deployment of more reliable kge systems in real-world scenarios.