| Challenge: | Existing knowledge graphs have large amount of missing links, which limits their application . a recent study has proposed to design an automated inference model to complete the missing links in large knowledge graph. |
| Approach: | They propose to use variation inference to solve missing links in knowledge graphs . they use a posterior approximator, prior (path finder) and likelihood (path reasoner) |
| Outcome: | The proposed model achieves state-of-the-art on multiple datasets and is highly accurate. |
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Inductive Relation Inference of Knowledge Graph Enhanced by Ontology Information (2023.findings-emnlp)
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| Challenge: | Existing methods to inference knowledge graphs lack ontology information, which is often too sparse. |
| Approach: | They propose a knowledge graph inductive inference method that fuses ontology information to learn the semantic information of entities. |
| Outcome: | The proposed method outperforms large language models like ChatGPT on two benchmark datasets and improves the MRR metrics by 15.4% and 44.1%, respectively. |
Open-Domain Contextual Link Prediction and its Complementarity with Entailment Graphs (2021.findings-emnlp)
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| Challenge: | Existing methods for linking knowledge graphs only use textual contexts . contextual link prediction is useful for finding context-dependent entailments . |
| Approach: | They propose a task of open-domain contextual link prediction which uses textual context and KG structure to perform link prediction. |
| Outcome: | The proposed model can ground the triples in the context of the original dataset and infer missing relations in context. |
A Semantic Filter Based on Relations for Knowledge Graph Completion (2021.emnlp-main)
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| 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. |
RelWalk - A Latent Variable Model Approach to Knowledge Graph Embedding (2021.eacl-main)
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| Challenge: | Existing methods for embedding entities and relations in knowledge graphs are heuristically motivated and theoretical understanding of such embeddables is underdeveloped. |
| Approach: | They extend the random walk model of word embeddings to Knowledge Graph Embeddings (KGEs) they propose a learning objective motivated by the theoretical analysis to learn KGEs from a given knowledge graph. |
| Outcome: | The proposed learning objective is motivated by the theoretical analysis to learn KGEs from a given knowledge graph. |
Representation Learning with Ordered Relation Paths for Knowledge Graph Completion (D19-1)
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| Challenge: | Existing knowledge graphs are incomplete and lack the order of relations in paths. |
| Approach: | They propose a method which takes relation paths into account but ignores order of relations in paths which is important for reasoning. |
| Outcome: | The proposed method performs better than state-of-the-art methods on two benchmark datasets. |
Link Prediction on N-ary Relational Facts: A Graph-based Approach (2021.findings-acl)
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| Challenge: | Existing work on knowledge graphs (KGs) focused on binary relations, but higher-arity relations are ubiquitous in real-world KGs. |
| Approach: | They propose a graph-based approach to link prediction on knowledge graphs using n-ary relational facts and edge-biased fully-connected attention. |
| Outcome: | The proposed approach performs substantially better than current state-of-the-art across a variety of n-ary relational benchmarks. |
How Sememic Components Can Benefit Link Prediction for Lexico-Semantic Knowledge Graphs? (2025.emnlp-main)
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| Challenge: | Existing methods to predict missing triples in Knowledge Graphs are limited by semantic information. |
| Approach: | They propose a method to leverage sememe knowledge to enhance LP . LP is a technique that integrates structural and textual information into a Knowledge Graph . |
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Does Pre-trained Language Model Actually Infer Unseen Links in Knowledge Graph Completion? (2024.naacl-long)
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| Challenge: | Knowledge Graph Completion (KGC) is a task that infers unseen relationships between entities . traditional embedding-based methods infer missing links using only training data . a pre-trained language model (PLM)-based KGC may be ineffective in practical applications . |
| Approach: | They propose to use knowledge Graph Completion (KGC) to infer unseen relationships . traditional embedding-based KGC methods infer missing links only from training data . they argue that pre-trained language models acquire inference abilities through pre-training . |
| Outcome: | The proposed method improves performance even though it does not use memorized knowledge. |
Shrinking Embeddings for Hyper-Relational Knowledge Graphs (2023.acl-long)
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| Challenge: | Existing studies have focused on binary relational KGs where each fact is represented by a triple. |
| Approach: | They propose a geometric hyper-relational KG embedding method that explicitly models qualifier monotonicity, qualifier implication, and qualifier mutual exclusion. |
| Outcome: | The proposed method outperforms existing methods on three benchmarks of hyper-relational KGs. |
Meta Relational Learning for Few-Shot Link Prediction in Knowledge Graphs (D19-1)
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| Challenge: | Empirically, our model achieves state-of-the-art results on few-shot link prediction KG benchmarks. |
| Approach: | They propose a Meta Relational Learning framework to do few-shot link prediction in KGs by observing only a few associative triples. |
| Outcome: | The proposed model achieves state-of-the-art results on few-shot link prediction KG benchmarks. |