
Bo Xiong
Postdoctoral Scholar, Biomedical Informatics
Bio
I am a Postdoctoral Scholar in AI at Stanford University, under the supervision of Prof. Mark Musen. Prior to that, I received my Ph.D. (with summa cum laude) in computer science at University of Stuttgart, Germany and the Intl. Max Plank Research School for Intelligent Systems (IMPRS-IS), under the supervision of Prof. Steffen Staab. My PhD was funded by the prestigious Marie Curie PhD Fellowship. I was also an associate member of the TrustAGI Lab at Griffith University, Australia, advised by Prof. Shirui Pan. I have published 20+ papers and/or served as PC in premier AI conferences such as NeurIPS, ICLR, KDD, ACL, WWW, EMNLP, NAACL, SIGIR, AAAI, ECAI, ISWC, etc., and received the Best Student Paper Award of ISWC’22.
I conduct research in AI and NLP with a special emphasis on advancing the representation of human knowledge (i.e., knowledge representation and knowledge engineering). In particular, I explore two core paradigms of representation:
1) Symbolic representation: Knowledge graphs, ontology, logic, semantic web, etc.
2) Neural representation: Large language models, graph embeddings.
By bridging these two forms of representations (neuro-symbolic), I aim to develop reliable and interpretable AI systems allowing for learning, reasoning, and adapting into real domains.
Honors & Awards
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Best Dissertation Award in Computer Science, University of Stuttgart (2024)
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Publication Prize in 2022, University of Stuttgart (2023)
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Spotlight Scholar, International Max Plan Research School for Intelligent Systems (IMPRS) (2023)
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Best Student Paper Award, International Semantic Web Conference (ISWC) (2022)
Professional Education
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Ph.D., Intl. Max Plank Research School for Intelligent Systems (IMPRS-IS), Computer Science (2024)
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Ph.D., University of Stuttgart, Computer Science (2024)
Current Research and Scholarly Interests
AI, LLMs, Knowledge Graphs, Biomedical Ontologies
All Publications
- From Tokens to Lattices: Emergent Lattice Structures in Language Models International Conference of Learning Representation 2025
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HypMix: Hyperbolic Representation Learning for Graphs with Mixed Hierarchical and Non-hierarchical Structures.
Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management
2024; 2024: 3852-3856
Abstract
Heterogeneous networks contain multiple types of nodes and links, with some link types encapsulating hierarchical structure over entities. Hierarchical relationships can codify information such as subcategories or one entity being subsumed by another and are often used for organizing conceptual knowledge into a tree-structured graph. Hyperbolic embedding models learn node representations in a hyperbolic space suitable for preserving the hierarchical structure. Unfortunately, current hyperbolic embedding models only implicitly capture the hierarchical structure, failing to distinguish between node types, and they only assume a single tree. In practice, many networks contain a mixture of hierarchical and non-hierarchical structures, and the hierarchical relations may be represented as multiple trees with complex structures, such as sharing certain entities. In this work, we propose a new hyperbolic representation learning model that can handle complex hierarchical structures and also learn the representation of both hierarchical and non-hierarchic structures. We evaluate our model on several datasets, including identifying relevant articles for a systematic review, which is an essential tool for evidence-driven medicine and node classification.
View details for DOI 10.1145/3627673.3679940
View details for PubMedID 40018085
View details for PubMedCentralID PMC11867734
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NestE: Modeling Nested Relational Structures for Knowledge Graph Reasoning
ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE. 2024: 9205-9213
View details for Web of Science ID 001239938200145
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Reasoning beyond Triples: Recent Advances in Knowledge Graph Embeddings
ASSOC COMPUTING MACHINERY. 2023: 5228-5231
View details for DOI 10.1145/3583780.3615294
View details for Web of Science ID 001161549505049
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HiPrompt: Few-Shot Biomedical Knowledge Fusion via Hierarchy-Oriented Prompting
ASSOC COMPUTING MACHINERY. 2023: 2052-2056
Abstract
Medical decision-making processes can be enhanced by comprehensive biomedical knowledge bases, which require fusing knowledge graphs constructed from different sources via a uniform index system. The index system often organizes biomedical terms in a hierarchy to provide the aligned entities with fine-grained granularity. To address the challenge of scarce supervision in the biomedical knowledge fusion (BKF) task, researchers have proposed various unsupervised methods. However, these methods heavily rely on ad-hoc lexical and structural matching algorithms, which fail to capture the rich semantics conveyed by biomedical entities and terms. Recently, neural embedding models have proved effective in semantic-rich tasks, but they rely on sufficient labeled data to be adequately trained. To bridge the gap between the scarce-labeled BKF and neural embedding models, we propose HiPrompt, a supervision-efficient knowledge fusion framework that elicits the few-shot reasoning ability of large language models through hierarchy-oriented prompts. Empirical results on the collected KG-Hi-BKF benchmark datasets demonstrate the effectiveness of HiPrompt.
View details for DOI 10.1145/3539618.3591997
View details for Web of Science ID 001118084002020
View details for PubMedID 38352127
View details for PubMedCentralID PMC10863609
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Hyperbolic Graph Neural Networks: A Tutorial on Methods and Applications
ASSOC COMPUTING MACHINERY. 2023: 5843-5844
View details for DOI 10.1145/3580305.3599562
View details for Web of Science ID 001118896305105
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Shrinking Embeddings for Hyper-Relational Knowledge Graphs
ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2023: 13306-13320
View details for Web of Science ID 001190962505005
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Pseudo-Riemannian Graph Convolutional Networks
NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2022
View details for Web of Science ID 001215469507014
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Hyperbolic Embedding Inference for Structured Multi-Label Prediction
NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2022
View details for Web of Science ID 001213927504027
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Faithful Embeddings for ε<i>L</i><SUP>++</SUP> Knowledge Bases
SPRINGER INTERNATIONAL PUBLISHING AG. 2022: 22-38
View details for DOI 10.1007/978-3-031-19433-7_2
View details for Web of Science ID 000886782800002
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Ultrahyperbolic Knowledge Graph Embeddings
ASSOC COMPUTING MACHINERY. 2022: 2130-2139
View details for DOI 10.1145/3534678.3539333
View details for Web of Science ID 001119000302019
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Time-aware Entity Alignment using Temporal Relational Attention
ASSOC COMPUTING MACHINERY. 2022: 788-797
View details for DOI 10.1145/3485447.3511922
View details for Web of Science ID 000852713000079