Jiyao Wei, Saiping Guan, Da Li, Zhongni Hou, Miao Su, Yucan Guo, Xiaolong Jin, Jiafeng Guo, Xueqi Cheng
| Challenge: | N-ary Knowledge Graphs (NKGs) capture n-ary facts containing more than two entities. |
| Approach: | They present the first comprehensive survey of link prediction in NKGs . they provide an overview of the field and analyze their performance and application scenarios . |
| Outcome: | The proposed methods provide an overview of the field and analyze performance and application scenarios. |
Similar Papers
Link Prediction on N-ary Relational Facts: A Graph-based Approach (2021.findings-acl)
Copied to clipboard
| 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. |
Inductive Link Prediction in N-ary Knowledge Graphs (2025.coling-main)
Copied to clipboard
| Challenge: | Existing methods to predict missing elements in NKGs are fixed and therefore cannot be used in real-world situations. |
| Approach: | They propose a task to predict missing elements in unseen facts involving unseent entities and roles in emerging NKGs by embedding unseense entities and role-encoding neural networks. |
| Outcome: | The proposed task outperforms representative models across all datasets. |
A Decade of Knowledge Graphs in Natural Language Processing: A Survey (2022.aacl-main)
Copied to clipboard
| Challenge: | Knowledge graphs (KGs) are a representation of semantic relations between entities . despite their popularity, there is still no general understanding of what exactly a KG is or for what tasks it is applicable. |
| Approach: | They analyze 507 papers on knowledge graphs in natural language processing (NLP) they provide a taxonomy of tasks and review the maturity of individual research streams . |
| Outcome: | The findings summarize the literature and highlight directions for future work. |
NeuInfer: Knowledge Inference on N-ary Facts (2020.acl-main)
Copied to clipboard
| Challenge: | Existing studies on knowledge inference on binary facts have focused on finding out connotative valid facts. |
| Approach: | They propose a neural network model, NeuInfer, for knowledge inference on n-ary facts. |
| Outcome: | The proposed model can cope with the task to infer an unknown element in a whole fact, while ignoring the binary facts. |
FactKG: Fact Verification via Reasoning on Knowledge Graphs (2023.acl-long)
Copied to clipboard
| Challenge: | knowledge graphs (KGs) have not been fully utilized as a knowledge source for fact verification. |
| Approach: | They propose a dataset to enable the community to better use knowledge graphs . they propose 108k natural language claims with five types of reasoning . |
| Outcome: | The proposed dataset consists of 108k natural language claims with five types of reasoning . authors believe the proposed method can advance reliability and practicality . |
OKGIT: Open Knowledge Graph Link Prediction with Implicit Types (2021.findings-acl)
Copied to clipboard
| Challenge: | Open Knowledge Graphs (OpenKGs) are sparse and not directly usable in an end task. |
| Approach: | They propose a method that bootstraps OpenKGs from a corpus using OpenIE tools. |
| Outcome: | The proposed method achieves state-of-the-art performance while producing type compatible NPs in the link prediction task. |
A Re-evaluation of Knowledge Graph Completion Methods (2020.acl-main)
Copied to clipboard
| Challenge: | Knowledge Graph Completion (KGC) aims at automatically predicting missing links for large-scale knowledge graphs. |
| Approach: | They propose a protocol to evaluate KGC methods that is robust to handle bias in the model, which can substantially affect the final results. |
| Outcome: | The proposed evaluation protocol is robust to handle bias in the model, which can substantially affect the final results. |
Predictive Multiplicity of Knowledge Graph Embeddings in Link Prediction (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Knowledge graph embeddings (KGE) models are often used to predict missing links for knowledge graphs (KGs) however, multiple KG embedds can give conflicting predictions for unseen queries. |
| Approach: | They define predictive multiplicity in link prediction and introduce evaluation metrics to measure it using commonly used benchmark datasets. |
| Outcome: | The proposed methods significantly mitigat conflicts by 66% to 78% in link prediction. |
Can We Predict New Facts with Open Knowledge Graph Embeddings? A Benchmark for Open Link Prediction (2020.acl-main)
Copied to clipboard
| Challenge: | Existing methods for predicting knowledge graphs rely on the rich structure of the knowledge graph. |
| Approach: | They propose an evaluation protocol and a methodology for creating the open link prediction benchmark OlpBench. |
| Outcome: | The proposed model predicts test facts by completing questions in open link prediction task. |
Graph Pattern Entity Ranking Model for Knowledge Graph Completion (N19-1)
Copied to clipboard
| Challenge: | Knowledge graph embedding models are so called-black box and are hard to interpret. |
| Approach: | They propose to use graph patterns to construct an entity ranking system for each graph pattern and evaluate them using a ranking system. |
| Outcome: | The proposed model outperforms other state-of-the-art models on standard metrics such as HITS@n and MRR. |