RatE: Relation-Adaptive Translating Embedding for Knowledge Graph Completion (2020.coling-main)
Copied to clipboard
| Challenge: | Existing approaches for knowledge graph embedding have limitations in complex vector space . embeddability of one-to-many relations is not explicitly alleviated . |
| Approach: | They propose a relation-adaptive translating embedding function that can be extended to complex vector space. |
| Outcome: | The proposed translation function improves expressive power and alleviates embedding ambiguity problem. |
Similar Papers
Knowledge Graph Embedding by Adaptive Limit Scoring Loss Using Dynamic Weighting Strategy (2022.findings-acl)
Copied to clipboard
| Challenge: | Existing knowledge graph embedding models use a loss framework to distinguish between correct and incorrect triplets. |
| Approach: | They propose a loss framework that reweights each triplet to highlight the less-optimized triplets. |
| Outcome: | The proposed method performs on several knowledge graph embedding models, including TransE, TransH and ComplEx. |
PairRE: Knowledge Graph Embeddings via Paired Relation Vectors (2021.acl-long)
Copied to clipboard
| Challenge: | Existing knowledge graph embedding methods fail to solve two major problems at the same time, leading to unsatisfactory results. |
| Approach: | They propose a model with paired vectors for each relation representation that can be adaptively adjusted to fit for different complex relations. |
| Outcome: | Experiments on two knowledge graph datasets show the proposed model can handle complex relations and encode relation patterns. |
TranSHER: Translating Knowledge Graph Embedding with Hyper-Ellipsoidal Restriction (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing knowledge graph embedding methods restrict entities on hyper-ellipsoid surfaces, resulting in suboptimal knowledge graph completion. |
| Approach: | They propose a score function that leverages relation-specific translations between head and tail entities to relax constraints on hyper-ellipsoid surfaces. |
| Outcome: | The proposed method achieves state-of-the-art performance on link prediction and generalizes well to datasets in different domains and scales. |
Orthogonal Relation Transforms with Graph Context Modeling for Knowledge Graph Embedding (2020.acl-main)
Copied to clipboard
| Challenge: | Existing knowledge graph embeddings have improved the knowledge graph link prediction task, but complex relations such as N-to-1, 1-to-N and N- to-N remain challenging to predict. |
| Approach: | They propose to extend RotatE from 2D complex domain to high dimensional space with orthogonal transforms to model relations. |
| Outcome: | The proposed method improves on N-to-1, 1-to-N and N- to-N cases while maintaining the capability for modeling symmetric/anti-symmetric, inverse and compositional relations. |
A Semantic Filter Based on Relations for Knowledge Graph Completion (2021.emnlp-main)
Copied to clipboard
| Challenge: | Knowledge graph embedding is a new form of knowledge graphing that allows for better link prediction. |
| Approach: | They propose to use relational embedding to fit symmetry/antisymmetry and combination relationships. |
| Outcome: | The proposed model can fit symmetry/antisymmetry and combination relationships. |
Improving Knowledge Graph Embedding Using Affine Transformations of Entities Corresponding to Each Relation (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing knowledge graph embedding methods use k-dimensional vectors to represent each entity in a knowledge graph. |
| Approach: | They propose to use affine transformations to embed knowledge graphs using previous methods . they propose to add k additional variables to the existing methods to perform embedding . |
| Outcome: | The proposed method outperforms RotatE, Distmult and ComplEx on various data sets. |
Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs (P19-1)
Copied to clipboard
| Challenge: | Existing knowledge graphs (KGs) are incomplete or partial information, in the form of missing relations between entities, which gives rise to the task of knowledge base completion (also known as relation prediction). |
| Approach: | They propose to capture both entity and relation features in any given neighborhood and encapsulate relation clusters and multi-hop relations in their attention-based model. |
| Outcome: | The proposed model captures both entity and relation features in any given neighborhood and also encapsulates relation clusters and multi-hop relations. |
A Framework for Adapting Pre-Trained Language Models to Knowledge Graph Completion (2022.emnlp-main)
Copied to clipboard
| Challenge: | Recent work has demonstrated that entity representations can be extracted from pre-trained language models to develop knowledge graph completion models that are more robust to the naturally occurring sparsity found in knowledge graphs. |
| Approach: | They propose unsupervised and supervised methods to extract more informative representations from pre-trained language models to develop knowledge graph completion models. |
| Outcome: | The proposed model outperforms recent neural models in terms of performance and unsupervised processing methods. |
Connecting Language and Knowledge with Heterogeneous Representations for Neural Relation Extraction (N19-1)
Copied to clipboard
| Challenge: | Knowledge Bases (KBs) require constant updating to reflect changes to the world they represent. |
| Approach: | They propose a framework that unifies learning of RE and KBE models . the framework is based on a relation extraction task that uses a KB relation to a phrase . |
| Outcome: | The proposed framework unifies learning of RE and KBE models, leading to significant improvements over the state-of-the-art RE framework. |
Relation-aware Ensemble Learning for Knowledge Graph Embedding (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods to explore semantics of knowledge graphs have been proposed to explore these semantics in distinct ways. |
| Approach: | They propose to leverage existing methods in relation-aware manner to learn an ensemble by leveraging existing methods. |
| Outcome: | The proposed method has the same computation cost as general ensemble methods but with much better performance on benchmark datasets. |