Shrinking Embeddings for Hyper-Relational Knowledge Graphs

• URL: http://arxiv.org/abs/2306.02199v1
• Date: Sat, 3 Jun 2023 21:14:59 GMT
• Title: Shrinking Embeddings for Hyper-Relational Knowledge Graphs
• Authors: Bo Xiong, Mojtaba Nayyer, Shirui Pan, Steffen Staab
• Abstract summary: We present emphShrinkE, a geometric hyper-relational KG embedding method aiming to explicitly model these patterns. Experimental results demonstrate ShrinkE's superiority on three benchmarks of hyper-relational KGs.
• Score: 42.23862602535092
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Link prediction on knowledge graphs (KGs) has been extensively studied on binary relational KGs, wherein each fact is represented by a triple. A significant amount of important knowledge, however, is represented by hyper-relational facts where each fact is composed of a primal triple and a set of qualifiers comprising a key-value pair that allows for expressing more complicated semantics. Although some recent works have proposed to embed hyper-relational KGs, these methods fail to capture essential inference patterns of hyper-relational facts such as qualifier monotonicity, qualifier implication, and qualifier mutual exclusion, limiting their generalization capability. To unlock this, we present \emph{ShrinkE}, a geometric hyper-relational KG embedding method aiming to explicitly model these patterns. ShrinkE models the primal triple as a spatial-functional transformation from the head into a relation-specific box. Each qualifier ``shrinks'' the box to narrow down the possible answer set and, thus, realizes qualifier monotonicity. The spatial relationships between the qualifier boxes allow for modeling core inference patterns of qualifiers such as implication and mutual exclusion. Experimental results demonstrate ShrinkE's superiority on three benchmarks of hyper-relational KGs.

Related papers

• HyperMono: A Monotonicity-aware Approach to Hyper-Relational Knowledge Representation [27.28214706269035]
  In a hyper-relational knowledge graph (HKG), each fact is composed of a main triple associated with attribute-value qualifiers, which express additional factual knowledge. This paper proposes the HyperMono model for hyper-relational knowledge graph completion, which realizes stage reasoning and qualifier monotonicity.
  arXiv  Detail & Related papers  (2024-04-15T15:00:17Z)
• HyperFormer: Enhancing Entity and Relation Interaction for Hyper-Relational Knowledge Graph Completion [25.399684403558553]
  Hyper-relational knowledge graphs (HKGs) extend standard knowledge graphs by associating attribute-value qualifiers to triples. We propose HyperFormer, a model that considers local-level sequential information, which encodes the content of the entities, relations and qualifiers of a triple.
  arXiv  Detail & Related papers  (2023-08-12T09:31:43Z)
• A Dataset for Hyper-Relational Extraction and a Cube-Filling Approach [59.89749342550104]
  We propose the task of hyper-relational extraction to extract more specific and complete facts from text. Existing models cannot perform hyper-relational extraction as it requires a model to consider the interaction between three entities. We propose CubeRE, a cube-filling model inspired by table-filling approaches and explicitly considers the interaction between relation triplets and qualifiers.
  arXiv  Detail & Related papers  (2022-11-18T03:51:28Z)
• Learning Representations for Hyper-Relational Knowledge Graphs [35.380689788802776]
  We design a framework to learn representations for hyper-relational facts using multiple aggregators. Experiments demonstrate the effectiveness of our framework across multiple datasets. We conduct an ablation study that validates the importance of the various components in our framework.
  arXiv  Detail & Related papers  (2022-08-30T15:02:14Z)
• Geometry Interaction Knowledge Graph Embeddings [153.69745042757066]
  We propose Geometry Interaction knowledge graph Embeddings (GIE), which learns spatial structures interactively between the Euclidean, hyperbolic and hyperspherical spaces. Our proposed GIE can capture a richer set of relational information, model key inference patterns, and enable expressive semantic matching across entities.
  arXiv  Detail & Related papers  (2022-06-24T08:33:43Z)
• ExpressivE: A Spatio-Functional Embedding For Knowledge Graph Completion [78.8942067357231]
  ExpressivE embeds pairs of entities as points and relations as hyper-parallelograms in the virtual triple space. We show that ExpressivE is competitive with state-of-the-art KGEs and even significantly outperforms them on W18RR.
  arXiv  Detail & Related papers  (2022-06-08T23:34:39Z)
• Modeling Heterogeneous Hierarchies with Relation-specific Hyperbolic Cones [64.75766944882389]
  We present ConE (Cone Embedding), a KG embedding model that is able to simultaneously model multiple hierarchical as well as non-hierarchical relations in a knowledge graph. In particular, ConE uses cone containment constraints in different subspaces of the hyperbolic embedding space to capture multiple heterogeneous hierarchies. Our approach yields new state-of-the-art Hits@1 of 45.3% on WN18RR and 16.1% on DDB14 (0.231 MRR)
  arXiv  Detail & Related papers  (2021-10-28T07:16:08Z)
• On the Role of Conceptualization in Commonsense Knowledge Graph Construction [59.39512925793171]
  Commonsense knowledge graphs (CKGs) like Atomic and ASER are substantially different from conventional KGs. We introduce to CKG construction methods conceptualization to view entities mentioned in text as instances of specific concepts or vice versa. Our methods can effectively identify plausible triples and expand the KG by triples of both new nodes and edges of high diversity and novelty.
  arXiv  Detail & Related papers  (2020-03-06T14:35:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.