GTransE: Generalizing Translation-Based Model on Uncertain Knowledge Graph Embedding

Kertkeidkachorn, Natthawut; Liu, Xin; Ichise, Ryutaro

doi:10.1007/978-3-030-39878-1_16

Cited by 14 publications

(3 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They then optimize using a margin-based ranking loss function such that triples with higher confidence levels receive greater optimization weights. Similarly, the GtransE [43] model utilizes the TransE scoring function but adjusts the margin between positive and negative triples dynamically based on their confidence levels. This allows triples with higher confidence levels to have a larger margin, while those with lower confidence levels have a smaller margin, making the model more focused on learning from high-confidence triples.…”

Section: Uncertain Knowledge Graph Representation Learning Modelsmentioning

confidence: 99%

CosUKG: A Representation Learning Framework for Uncertain Knowledge Graphs

Shen,

2024

Mathematics

View full text Add to dashboard Cite

Knowledge graphs have been extensively studied and applied, but most of these studies assume that the relationship facts in the knowledge graph are correct and deterministic. However, in the objective world, there inevitably exist uncertain relationship facts. The existing research lacks effective representation of such uncertain information. In this regard, we propose a novel representation learning framework called CosUKG, which is specifically designed for uncertain knowledge graphs. This framework models uncertain information by measuring the cosine similarity between transformed vectors and actual target vectors, effectively integrating uncertainty into the embedding process of the knowledge graph while preserving its structural information. Through multiple experiments on three public datasets, the superiority of the CosUKG framework in representing uncertain knowledge graphs is demonstrated. It achieves improved representation accuracy of uncertain information without increasing model complexity or weakening structural information.

show abstract

Section: Uncertain Knowledge Graph Representation Learning Modelsmentioning

confidence: 99%

CosUKG: A Representation Learning Framework for Uncertain Knowledge Graphs

Shen,

2024

Mathematics

View full text Add to dashboard Cite

show abstract

“…By incorporating uncertainty into the model, knowledge graph embeddings can improve the accuracy and robustness of models. Uncertain-aware embeddings can incorporate probabilistic uncertainty into the embedding process, which allows for more precise representations [140,141,142,147]. It can be seen from Table 2 that, in addition to performance indicators, uncertainty estimation is mainly exploited for uncertainty calibration indicators.…”

Section: Uncertainty and Performancementioning

confidence: 99%

Domain-Invariant Feature Distillation for Cross-Domain Sentiment Classification

Hu¹,

Wu²,

Zhao³

et al. 2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

View full text Add to dashboard Cite

Cross-domain sentiment classification has drawn much attention in recent years. Most existing approaches focus on learning domaininvariant representations in both the source and target domains, while few of them pay attention to the domain-specific information. Despite the non-transferability of the domainspecific information, simultaneously learning domain-dependent representations can facilitate the learning of domain-invariant representations. In this paper, we focus on aspectlevel cross-domain sentiment classification, and propose to distill the domain-invariant sentiment features with the help of an orthogonal domain-dependent task, i.e. aspect detection, which is built on the aspects varying widely in different domains. We conduct extensive experiments on three public datasets and the experimental results demonstrate the effectiveness of our method.

show abstract

“…To facilitate automated knowledge acquisition for UKGs, some UKG embedding models (Chen et al, 2019;Kertkeidkachorn et al, 2019) have recently been proposed. Inspired by the works about deterministic KG embeddings (Yang et al, 2015;Bordes et al, 2013), existing approaches model entities and relations as points in low-dimensional vector space, measure triple plausibility with vector similarity (eg.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Box Embeddings for Uncertain Knowledge Graph Reasoning

Chen¹,

Boratko²,

Chen³

et al. 2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

View full text Add to dashboard 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

show abstract

GTransE: Generalizing Translation-Based Model on Uncertain Knowledge Graph Embedding

Cited by 14 publications

References 14 publications

CosUKG: A Representation Learning Framework for Uncertain Knowledge Graphs

CosUKG: A Representation Learning Framework for Uncertain Knowledge Graphs

Domain-Invariant Feature Distillation for Cross-Domain Sentiment Classification

Probabilistic Box Embeddings for Uncertain Knowledge Graph Reasoning

Contact Info

Product

Resources

About