Papers with knowledge graphs
Hierarchical Relation-Guided Type-Sentence Alignment for Long-Tail Relation Extraction with Distant Supervision (2022.findings-naacl)
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| Challenge: | Distant supervision uses triple facts to label corpus for relation extraction, leading to wrong labeling and long-tail problems. |
| Approach: | They propose a model to enrich distantly-supervised sentences with entity types by injecting context-free and -related backgrounds into sentences to alleviate sentence-level wrong labeling. |
| Outcome: | The proposed model achieves state-of-the-art on benchmarks and in overall and long-tail performance. |
Adversarial Contrastive Estimation (P18-1)
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| Challenge: | Noise contrastive estimation (NCE) is a general strategy used in word embeddings and translations for knowledge graphs. |
| Approach: | They propose to augment negative sampler into mixture distribution with adversarially learned sampler and to combine it with noise contrastive estimation (NCE) they observe faster convergence and improved results on multiple metrics. |
| Outcome: | The proposed model performs better on word embeddings, order embedds and knowledge graph embeddments and faster convergence and improved results on multiple metrics. |
Learning Reasoning Patterns for Relational Triple Extraction with Mutual Generation of Text and Graph (2022.findings-acl)
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| Challenge: | Existing methods focused on learning text patterns from explicit mentions but failed to extract the implicitly implied triples. |
| Approach: | They propose to construct a relational graph from a sentence and apply multi-layer graph convolutions to capture the type inference logic of the paths. |
| Outcome: | The proposed framework can find multi-hop reasoning paths and capture type inference logic with the sentence's supplementary relational expressions. |
Poisoning Knowledge Graph Embeddings via Relation Inference Patterns (2021.acl-long)
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| Challenge: | Knowledge graph embeddings (KGE) models are increasingly deployed in domains with high stake decision making where it is critical to identify the potential security vulnerabilities that might cause failure. |
| Approach: | They propose to exploit the inductive abilities of knowledge graph embedding models by crafting adversarial additions that can improve model’s confidence on decoy facts. |
| Outcome: | The proposed attacks outperform state-of-the-art baselines on four KGE models for two publicly available datasets and generalize across all model-dataset combinations. |
Temporal Knowledge Graph Completion using a Linear Temporal Regularizer and Multivector Embeddings (2021.naacl-main)
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| Challenge: | Existing knowledge graphs involve evolving data, e.g., the fact (The President of the United States is Barack Obama) is valid only from 2009 to 2017. |
| Approach: | They propose a time-aware knowledge graph embebdding approach which performs 4th-order tensor factorization of a Temporal knowledge graph using a Linear temporal regularizer and Multivector embeddings. |
| Outcome: | The proposed model achieves state-of-the-art performance over four well-established temporal knowledge graph completion benchmarks. |
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. |
| Approach: | They propose to use attributed and non-attributed graphs to solve an out-of-sample representation learning problem for non-credited knowledge graphs. |
| Outcome: | The proposed model and baselines compare with existing models and baseline models. |
RulE: Knowledge Graph Reasoning with Rule Embedding (2024.findings-acl)
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| Challenge: | Knowledge graph reasoning is an important problem for knowledge graphs. |
| Approach: | They propose a framework that leverages logical rules to enhance KG reasoning by learning rule embeddings from existing triplets and first-order rules. |
| Outcome: | The proposed framework outperforms existing embedding-based and rule-based methods on multiple benchmarks. |
Knowing the No-match: Entity Alignment with Dangling Cases (2021.acl-long)
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| Challenge: | Existing approaches to find entities that cannot find alignment across knowledge graphs (KGs) despite their importance, knowledge graph is expensive and suffers from incompleteness. |
| Approach: | They propose a framework for entity alignment and dangling entity detection that can be used to abstain from predicting alignment for detected dangle entities. |
| Outcome: | The proposed framework can abstain from predicting alignment for detected dangling entities. |
Unsupervised Graph-Text Mutual Conversion with a Unified Pretrained Language Model (2023.acl-long)
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| Challenge: | Existing unsupervised approaches for learning knowledge graphs require multiple modules and require entity information or relation type for training. |
| Approach: | They propose a method that uses a unified pretrained language model to achieve fully unsupervised graph-text mutual conversion for the first time. |
| Outcome: | The proposed method outperforms state-of-the-art methods for G2T and T2G tasks by fine-tuning only one pretrained model. |
MCMH: Learning Multi-Chain Multi-Hop Rules for Knowledge Graph Reasoning (2020.findings-emnlp)
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| Challenge: | Existing work on knowledge graphs infers a missing relationship between entities with a multi-hop rule . Empirical results show that our multi-chain multi-homing (MCMH) rules yield superior results compared to the standard single-chain approaches. |
| Approach: | They propose to use a generalized form of multi-hop rules to learn generalized rules efficiently . they propose to select a small set of relation chains as a rule and evaluate confidence . |
| Outcome: | The proposed method outperforms the existing methods and the existing frameworks. |
An End-to-End Progressive Multi-Task Learning Framework for Medical Named Entity Recognition and Normalization (2021.acl-long)
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| Challenge: | Existing models for medical named entity recognition and named entity normalization suffer from error propagation between the two tasks. |
| Approach: | They propose an end-to-end progressive multi-task learning model for jointly modeling medical named entity recognition and normalization in an effective way. |
| Outcome: | The proposed model reduces error propagation by exploiting the learnable features for both tasks to improve performance. |
Structure Aware Negative Sampling in Knowledge Graphs (2020.emnlp-main)
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| Challenge: | Existing methods for learning low-dimensional representations of entities and relations in knowledge graphs employing corruption distributions that generate hard negative samples. |
| Approach: | They propose a structure-aware negative sampling strategy that utilizes the rich graph structure by selecting negative samples from a node’s k-hop neighborhood. |
| Outcome: | The proposed method finds semantically meaningful negatives and is competitive with SOTA approaches while requires no additional parameters nor difficult adversarial optimization. |
Learning Sequence Encoders for Temporal Knowledge Graph Completion (D18-1)
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| Challenge: | Existing work on link prediction in knowledge graphs has focused on static multi-relational data. |
| Approach: | They propose to learn latent entity and relation type representations to incorporate temporal information into knowledge graphs. |
| Outcome: | The proposed approach is robust to common challenges in real-world KGs. |
KICGPT: Large Language Model with Knowledge in Context for Knowledge Graph Completion (2023.findings-emnlp)
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| Challenge: | Existing knowledge graph completion methods struggle with long-tail entities due to limited structural information and imbalanced distributions of entities. |
| Approach: | They propose a framework that integrates a large language model and a triple-based KGC retriever to alleviate the long-tail problem without incurring additional training overhead. |
| Outcome: | The proposed model reduces training overhead and finetuning costs on benchmark datasets. |
Enhancing Distractor Generation for Multiple-Choice Questions with Retrieval Augmented Pretraining and Knowledge Graph Integration (2024.findings-acl)
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Han Cheng Yu, Yu An Shih, Kin Man Law, KaiYu Hsieh, Yu Chen Cheng, Hsin Chih Ho, Zih An Lin, Wen-Chuan Hsu, Yao-Chung Fan
| Challenge: | Existing LMs undergo task-agnostic pertaining, but task-specific pretraining has gained prominence. |
| Approach: | They propose retrieval augmented pretraining and task-specific pretraining for DG . they propose to refine language model pretraining to align it more closely with downstream task . |
| Outcome: | The proposed method improves the performance of multiple-choice questions by integrating knowledge graphs and language models. |
Improving Multi-hop Logical Reasoning in Knowledge Graphs with Context-Aware Query Representation Learning (2024.findings-acl)
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| Challenge: | Existing methods rely on linear sequential operations to solve First-Order Logic queries. |
| Approach: | They propose a model-agnostic approach that fully integrates the context of the query graph. |
| Outcome: | The proposed method improves performance on two datasets by 19.5%. |
RG-VQA: Leveraging Retriever-Generator Pipelines for Knowledge Intensive Visual Question Answering (2025.findings-emnlp)
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Settaluri Lakshmi Sravanthi, Pulkit Agarwal, Debjyoti Mondal, Rituraj Singh, Subhadarshi Panda, Ankit Mishra, Kiran Pradeep, Srihari K B, Godawari Sudhakar Rao, Pushpak Bhattacharyya
| Challenge: | Existing methods to improve the reasoning capabilities of VQA systems are limited due to complexity of graph neural networks and end-to-end training. |
| Approach: | They propose a method to integrate Dense Passage Retrievers with Vision Language Models to boost the reasoning capabilities of VQA systems. |
| Outcome: | The proposed method outperforms human accuracy and GPT-4 in the ScienceQA dataset. |