Knowledge graphs (KGs), which store an extensive number of relational facts
(head, relation, tail), serve various applications. While many downstream tasks
highly rely on the expressive modeling and predictive embedding of KGs, most of
the current KG representation learning methods, where each entity is embedded
as a vector in the Euclidean space and each relation is embedded as a
transformation, follow an entity ranking protocol. On one hand, such an
embedding design cannot capture many-to-many relations. On the other hand, in
many retrieval cases, the users wish to get an exact set of answers without any
ranking, especially when the results are expected to be precise, e.g., which
genes cause an illness. Such scenarios are commonly referred to as "set
retrieval". This work presents a pioneering study on the KG set retrieval
problem. We show that the set retrieval highly depends on expressive modeling
of many-to-many relations, and propose a new KG embedding model SpherE to
address this problem. SpherE is based on rotational embedding methods, but each
entity is embedded as a sphere instead of a vector. While inheriting the high
interpretability of rotational-based models, our SpherE can more expressively
model one-to-many, many-to-one, and many-to-many relations. Through extensive
experiments, we show that our SpherE can well address the set retrieval problem
while still having a good predictive ability to infer missing facts. The code
is available at this https URL

本文首次研究知识图谱集合检索问题，通过提出一种新的知识图谱嵌入模型 SpherE，基于旋转嵌入方法，将每个实体嵌入为一个球体，可以更加准确地建模一对多、多对一和多对多的关系，并且有效解决集合检索问题。

SpherE: Set 检索的表达和可解释的知识图嵌入

SpherE: Expressive and Interpretable Knowledge Graph Embedding for Set  Retrieval

Knowledge graphs (KGs) have emerged as a powerful framework for representing
and integrating complex biomedical information. However, assembling KGs from
diverse sources remains a significant challenge in several aspects, including
entity alignment, scalability, and the need for continuous updates to keep pace
with scientific advancements. Moreover, the representative power of KGs is
often limited by the scarcity of multi-modal data integration. To overcome
these challenges, we propose Know2BIO, a general-purpose heterogeneous KG
benchmark for the biomedical domain. Know2BIO integrates data from 30 diverse
sources, capturing intricate relationships across 11 biomedical categories. It
currently consists of ~219,000 nodes and ~6,200,000 edges. Know2BIO is capable
of user-directed automated updating to reflect the latest knowledge in
biomedical science. Furthermore, Know2BIO is accompanied by multi-modal data:
node features including text descriptions, protein and compound sequences and
structures, enabling the utilization of emerging natural language processing
methods and multi-modal data integration strategies. We evaluate KG
representation models on Know2BIO, demonstrating its effectiveness as a
benchmark for KG representation learning in the biomedical field. Data and
source code of Know2BIO are available at
this https URL

我们提出了一个面向生物医学领域的异构知识图谱基准测试平台 Know2BIO，它从 30 个不同的数据源中整合数据，并捕捉了 11 个生物医学类别中的复杂关系。通过在 Know2BIO 上评估知识图谱表示模型，我们展示了其在生物医学领域中作为知识图谱表示学习基准测试的有效性。

Know2BIO: 演进中的生物医学知识图谱全面双视角基准

Know2BIO: A Comprehensive Dual-View Benchmark for Evolving Biomedical  Knowledge Graphs

Knowledge Graphs (KGs) are ubiquitous structures for information storagein
several real-world applications such as web search, e-commerce, social
networks, and biology. Querying KGs remains a foundational and challenging
problem due to their size and complexity. Promising approaches to tackle this
problem include embedding the KG units (e.g., entities and relations) in a
Euclidean space such that the query embedding contains the information relevant
to its results. These approaches, however, fail to capture the hierarchical
nature and semantic information of the entities present in the graph.
Additionally, most of these approaches only utilize multi-hop queries (that can
be modeled by simple translation operations) to learn embeddings and ignore
more complex operations such as intersection and union of simpler queries. To
tackle such complex operations, in this paper, we formulate KG representation
learning as a self-supervised logical query reasoning problem that utilizes
translation, intersection and union queries over KGs. We propose Hyperboloid
Embeddings (HypE), a novel self-supervised dynamic reasoning framework, that
utilizes positive first-order existential queries on a KG to learn
representations of its entities and relations as hyperboloids in a Poincar\'e
ball. HypE models the positive first-order queries as geometrical translation,
intersection, and union. For the problem of KG reasoning in real-world
datasets, the proposed HypE model significantly outperforms the state-of-the
art results. We also apply HypE to an anomaly detection task on a popular
e-commerce website product taxonomy as well as hierarchically organized web
articles and demonstrate significant performance improvements compared to
existing baseline methods. Finally, we also visualize the learned HypE
embeddings in a Poincar\'e ball to clearly interpret and comprehend the
representation space.

本文介绍了利用 Hyperboloid Embeddings 学习知识图谱表示的自监督逻辑查询推理方法，并将其应用于真实世界数据集和异常检测任务中，取得了显著的性能提升。