| Challenge: | Existing knowledge embedding methods have limited performance on knowledge graph reasoning tasks . eureka is empowered to learn seen relations with sufficient training triples . |
| Approach: | They propose a neural insight learning framework called Eureka to bridge the “seen” to “unsea” gap . Eureca is empowered to learn seen relations with sufficient training triples while providing flexibility to learn unseen relations given only one trigger . |
| Outcome: | The proposed framework outperforms state-of-the-art models on seen and unseen relations . it can learn seen and unseen relationships with sufficient training triples . |
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| 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). |
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| Challenge: | Currently, machine learning is limited in scalability and is limited to specific training data. |
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One-Shot Relational Learning for Knowledge Graphs (D18-1)
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| Challenge: | Existing studies on knowledge graph completion require a large number of positive examples for each relation, but long-tail relations are more common in KGs and those newly added relations do not have many known triples for training. |
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| Challenge: | Existing knowledge graph completion methods learn a fixed embedding for every entity, which is suboptimal as it requires memorizing and generalizing to all possible entity relationships. |
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TriggerNER: Learning with Entity Triggers as Explanations for Named Entity Recognition (2020.acl-main)
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| Challenge: | Named entity recognition (NER) is a fundamental information extraction task that focuses on extracting entities from a given text and classifying them using pre-defined categories. |
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| Challenge: | Existing methods for Distantly Supervised Relation Extraction (DS-RE) with a UG may lead to degradation in performance. |
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Towards Enhancing Relational Rules for Knowledge Graph Link Prediction (2023.findings-emnlp)
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| Challenge: | Existing knowledge graph reasoning methods are inadequate for missing knowledge . Various methods are explored to facilitate reasoning for missing information . |
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zrLLM: Zero-Shot Relational Learning on Temporal Knowledge Graphs with Large Language Models (2024.naacl-long)
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| Challenge: | Existing methods to forecast links on temporal knowledge graphs are embedding-based . but they face a strong challenge in modeling the unseen zero-shot relations . |
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Out-of-Sample Representation Learning for Knowledge Graphs (2020.findings-emnlp)
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| Challenge: | Existing work considers attributed graphs for transductive reasoning, but this problem is under-explored for non-attributed graph. |
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KGPool: Dynamic Knowledge Graph Context Selection for Relation Extraction (2021.findings-acl)
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Abhishek Nadgeri, Anson Bastos, Kuldeep Singh, Isaiah Onando Mulang’, Johannes Hoffart, Saeedeh Shekarpour, Vijay Saraswat
| Challenge: | Existing methods for relation extraction (RE) use only expanded facts from the knowledge graph . |
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