@inproceedings{nayyeri-etal-2023-knowledge,
    title = "Knowledge Graph Embeddings using Neural {I}to Process: From Multiple Walks to Stochastic Trajectories",
    author = "Nayyeri, Mojtaba  and
      Xiong, Bo  and
      Mohammadi, Majid  and
      Akter, Mst. Mahfuja  and
      Alam, Mirza Mohtashim  and
      Lehmann, Jens  and
      Staab, Steffen",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-acl.448",
    doi = "10.18653/v1/2023.findings-acl.448",
    pages = "7165--7179",
    abstract = "Knowledge graphs mostly exhibit a mixture of branching relations, e.g., hasFriend, and complex structures, e.g., hierarchy and loop. Most knowledge graph embeddings have problems expressing them, because they model a specific relation r from a head h to tails by starting at the node embedding of h and transitioning deterministically to exactly one other point in the embedding space. We overcome this issue in our novel framework ItCAREToE by modeling relations between nodes by relation-specific, stochastic transitions. Our framework is based on stochastic ItCARETo processes, which operate on low-dimensional manifolds. ItCAREToE is highly expressive and generic subsuming various state-of-the-art models operating on different, also non-Euclidean, manifolds. Experimental results show the superiority of ItCAREToE over other deterministic embedding models with regard to the KG completion task.",
}

Knowledge graphs mostly exhibit a mixture of branching relations, e.g., hasFriend, and complex structures, e.g., hierarchy and loop. Most knowledge graph embeddings have problems expressing them, because they model a specific relation r from a head h to tails by starting at the node embedding of h and transitioning deterministically to exactly one other point in the embedding space. We overcome this issue in our novel framework ItCAREToE by modeling relations between nodes by relation-specific, stochastic transitions. Our framework is based on stochastic ItCARETo processes, which operate on low-dimensional manifolds. ItCAREToE is highly expressive and generic subsuming various state-of-the-art models operating on different, also non-Euclidean, manifolds. Experimental results show the superiority of ItCAREToE over other deterministic embedding models with regard to the KG completion task.
Nayyeri, Mojtaba Xiong, Bo Mohammadi, Majid Akter, Mst. Mahfuja Alam, Mirza Mohtashim Lehmann, Jens Staab, Steffen
Knowledge Graph Embeddings using Neural Ito Process: From Multiple Walks to Stochastic Trajectories