@inproceedings{huang-etal-2023-concept2box,
    title = "{C}oncept2{B}ox: Joint Geometric Embeddings for Learning Two-View Knowledge Graphs",
    author = "Huang, Zijie  and
      Wang, Daheng  and
      Huang, Binxuan  and
      Zhang, Chenwei  and
      Shang, Jingbo  and
      Liang, Yan  and
      Wang, Zhengyang  and
      Li, Xian  and
      Faloutsos, Christos  and
      Sun, Yizhou  and
      Wang, Wei",
    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.642",
    doi = "10.18653/v1/2023.findings-acl.642",
    pages = "10105--10118",
    abstract = "Knowledge graph embeddings (KGE) have been extensively studied to embed large-scale relational data for many real-world applications. Existing methods have long ignored the fact many KGs contain two fundamentally different views: high-level ontology-view concepts and fine-grained instance-view entities. They usually embed all nodes as vectors in one latent space. However, a single geometric representation fails to capture the structural differences between two views and lacks probabilistic semantics towards concepts{'} granularity. We propose Concept2Box, a novel approach that jointly embeds the two views of a KG using dual geometric representations. We model concepts with box embeddings, which learn the hierarchy structure and complex relations such as overlap and disjoint among them. Box volumes can be interpreted as concepts{'} granularity. Different from concepts, we model entities as vectors. To bridge the gap between concept box embeddings and entity vector embeddings, we propose a novel vector-to-box distance metric and learn both embeddings jointly. Experiments on both the public DBpedia KG and a newly-created industrial KG showed the effectiveness of Concept2Box.",
}

Knowledge graph embeddings (KGE) have been extensively studied to embed large-scale relational data for many real-world applications. Existing methods have long ignored the fact many KGs contain two fundamentally different views: high-level ontology-view concepts and fine-grained instance-view entities. They usually embed all nodes as vectors in one latent space. However, a single geometric representation fails to capture the structural differences between two views and lacks probabilistic semantics towards concepts{'} granularity. We propose Concept2Box, a novel approach that jointly embeds the two views of a KG using dual geometric representations. We model concepts with box embeddings, which learn the hierarchy structure and complex relations such as overlap and disjoint among them. Box volumes can be interpreted as concepts{'} granularity. Different from concepts, we model entities as vectors. To bridge the gap between concept box embeddings and entity vector embeddings, we propose a novel vector-to-box distance metric and learn both embeddings jointly. Experiments on both the public DBpedia KG and a newly-created industrial KG showed the effectiveness of Concept2Box.
Huang, Zijie Wang, Daheng Huang, Binxuan Zhang, Chenwei Shang, Jingbo Liang, Yan Wang, Zhengyang Li, Xian Faloutsos, Christos Sun, Yizhou Wang, Wei
Concept2Box: Joint Geometric Embeddings for Learning Two-View Knowledge Graphs