Challenge: Knowledge graph inference has been studied extensively due to its wide applications.
Approach: They propose a framework that restricts logical rules to be definite Horn rules and can exploit the knowledge in logical rule-based reasoning and KGE in an extremely efficient way.
Outcome: The proposed framework can exploit the knowledge in logical rules and improve KGE in an extremely efficient way.

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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.
Towards Unified Representations of Knowledge Graph and Expert Rules for Machine Learning and Reasoning (2022.aacl-main)

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Challenge: Empirical study shows superiority of proposed method over time-tested knowledge-driven and data-driven methods.
Approach: They propose a cognitive knowledge graph that unifies expert rules and relational facts as the substrate of machine learning and reasoning models.
Outcome: Empirical results show the proposed method superior to time-tested methods . the proposed model can perform both learning and reasoning with labeled data .
MQuinE: a Cure for “Z-paradox” in Knowledge Graph Embedding (2024.emnlp-main)

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Challenge: Existing knowledge graph embedding models suffer from Z-paradox, a deficiency in expressiveness . Embedding-based models map each entity and relation into a vector or matrix .
Approach: They propose a new knowledge graph embedding model that does not suffer from Z-paradox while preserves strong expressiveness to model various relation patterns with theoretical justification.
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An Adaptive Logical Rule Embedding Model for Inductive Reasoning over Temporal Knowledge Graphs (2022.emnlp-main)

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Challenge: Existing methods for temporal knowledge graphs (TKGs) are incomplete and therefore lack interpretability.
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Perform like an Engine: A Closed-Loop Neural-Symbolic Learning Framework for Knowledge Graph Inference (2022.coling-1)

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Challenge: Existing knowledge graphs are incomplete and therefore lack interpretability.
Approach: They propose a closed-loop neural-symbolic learning framework EngineKG to address the natural incompleteness of knowledge graphs.
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Pretrain-KGE: Learning Knowledge Representation from Pretrained Language Models (2020.findings-emnlp)

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Challenge: Existing knowledge graph embedding models suffer from limited knowledge representation due to sparse and noisy dataset annotations.
Approach: They propose to use pretrained language models to enhance knowledge representation by leveraging world knowledge from pretrained models.
Outcome: Extensive experiments show that the proposed framework can improve results over existing models.
Improving Knowledge Graph Embedding Using Simple Constraints (P18-1)

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Challenge: Recent efforts focused on designing more complicated models or incorporating extra information beyond triples.
Approach: They propose to use non-negativity constraints on entity representations and approximate entailment constraints on relation representations to improve KG embedding.
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A New Pipeline for Knowledge Graph Reasoning Enhanced by Large Language Models Without Fine-Tuning (2024.emnlp-main)

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Challenge: Conventional knowledge Graph Reasoning models learn the embeddings of KG components over the structure of a KG.
Approach: They propose a pipeline to integrate knowledge from LLMs into KGs without fine-tuning . they propose knowledge alignment, KG reasoning and entity reranking to enhance conventional models .
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Sequence-to-Sequence Knowledge Graph Completion and Question Answering (2022.acl-long)

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Challenge: Knowledge graph embedding (KGE) models represent each entity and relation of a knowledge graph (KG) with low-dimensional embeddable vectors.
Approach: They propose to use an off-the-shelf encoder-decoder Transformer model to generate a knowledge graph embedding model that can be used for KG link prediction and incomplete KG question answering.
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Knowledge GeoGebra: Leveraging Geometry of Relation Embeddings in Knowledge Graph Completion (2024.lrec-main)

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Challenge: Knowledge graph embedding models are limited to the algebra and geometry of the entity embeddable space, the algebra of the relation embeddible space, and the interaction between relation and entity embeds.
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