Knowledge Association with Hyperbolic Knowledge Graph Embeddings

Boratko

et al. 2021

Preprint

Self Cite

Knowledge bases often consist of facts which are harvested from a variety of sources, many of which are noisy and some of which conflict, resulting in a level of uncertainty for each triple. Knowledge bases are also often incomplete, prompting the use of embedding methods to generalize from known facts, however existing embedding methods only model triple-level uncertainty and reasoning results lack global consistency. To address these shortcomings, we propose BEUrRE , a novel uncertain knowledge graph embedding method with calibrated probabilistic semantics. BEUrRE models each entity as a box (i.e. axis-aligned hyperrectangle), and relations between two entities as affine transforms on the head and tail entity boxes. The geometry of the boxes allows for efficient calculation of intersections and volumes, endowing the model with calibrated probabilistic semantics and facilitating the incorporation of relational constraints. Extensive experiments on two benchmark datasets show that BEUrRE consistently outperforms baselines on confidence prediction and fact ranking due to it's probabilistic calibration and ability to capture high-order dependencies among facts. 1

Section: Discussionmentioning

confidence: 99%

Probabilistic Box Embeddings for Uncertain Knowledge Graph Reasoning

Boratko

et al. 2021

Preprint

Self Cite

Findings of the Association for Computational Linguistics: EMNLP 2021

“…Similarly, Kolyvakis et al (Kolyvakis et al, 2020) extend the translational models by learning embeddings of KG entities and relations in the hyperbolic Poincaré-ball model. Sun et al (Sun et al, 2020) propose a hyperbolic relational graph neural network to capture knowledge associations for the KG entity alignment task. Chami et al (Chami et al, 2020) employ rotation and reflection operations to replace the multiplication operation between the head entity and relation vectors, and propose a series of hyperbolic KGE models with trainable curvature, including RotH, RefH, and AttH.…”

Section: Hyperbolic Embeddingsmentioning

confidence: 99%

“…Recently, low-dimensional KGE models based on hyperbolic vector space have drawn some attention (Sun et al, 2020). The work of the first such model, MuRP, indicates that hyperbolic embeddings can capture hierarchical patterns in KGs and generate high-fidelity and parsimonious representations (Balazevic et al, 2019b).…”

Section: Introductionmentioning

confidence: 99%

Hyperbolic Geometry is Not Necessary: Lightweight Euclidean-Based Models for Low-Dimensional Knowledge Graph Embeddings

Wang¹,

Liu²,

Lin³

et al. 2021

Recent knowledge graph embedding (KGE) models based on hyperbolic geometry have shown great potential in a low-dimensional embedding space. However, the necessity of hyperbolic space in KGE is still questionable, because the calculation based on hyperbolic geometry is much more complicated than Euclidean operations. In this paper, based on the state-of-the-art hyperbolic-based model RotH, we develop two lightweight Euclideanbased models, called RotL and Rot2L. The RotL model simplifies the hyperbolic operations while keeping the flexible normalization effect. Utilizing a novel two-layer stacked transformation and based on RotL, the Rot2L model obtains an improved representation capability, yet costs fewer parameters and calculations than RotH. The experiments on link prediction show that Rot2L achieves the stateof-the-art performance on two widely-used datasets in low-dimensional knowledge graph embeddings. Furthermore, RotL achieves similar performance as RotH but only requires half of the training time.

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

“…Knowledge graph alignment. Knowledge graph alignment techniques have attracted active research in the last decade Sun et al, 2020a;Berrendorf et al, 2021b,a) and can be broadly classified into two categories: (1) Translation-based techniques, which denote entities by computing the plausibility of relational facts measured by a specific fact plausibility scoring function, including MtransE (Chen et al, 2017a), IPTransE (Zhu et al, 2017), JAPE , BootEA (Sun et al, 2018), RSNs (Guo et al, 2019, NAEA (Zhu et al, 2019), OTEA (Pei et al, 2019b), TransEdge (Sun et al, 2019), Hy-perKA (Sun et al, 2020c). The idea of this kind of methods are originated from cross-lingual word embedding techniques.…”

Section: Related Workmentioning

confidence: 99%

Adversarial Attack against Cross-lingual Knowledge Graph Alignment

Zhang¹,

Zhang²,

Zhou³

et al. 2021

Recent literatures have shown that knowledge graph (KG) learning models are highly vulnerable to adversarial attacks. However, there is still a paucity of vulnerability analyses of cross-lingual entity alignment under adversarial attacks. This paper proposes an adversarial attack model with two novel attack techniques to perturb the KG structure and degrade the quality of deep cross-lingual entity alignment. First, an entity density maximization method is employed to hide the attacked entities in dense regions in two KGs, such that the derived perturbations are unnoticeable. Second, an attack signal amplification method is developed to reduce the gradient vanishing issues in the process of adversarial attacks for further improving the attack effectiveness.