Papers with knowledge graph reasoning
MWP-BERT: Numeracy-Augmented Pre-training for Math Word Problem Solving (2022.findings-naacl)
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| Challenge: | Existing work on math word problem solvers replace real numbers with symbolic placeholders to focus on logic reasoning. |
| Approach: | They propose to inject numerical properties into symbolic placeholders with contextualized representation learning schema to solve number representation dilemma. |
| Outcome: | The proposed model can solve MWP problems on English and Chinese benchmarks. |
Query2Particles: Knowledge Graph Reasoning with Particle Embeddings (2022.findings-naacl)
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| Challenge: | Existing methods to answer complex logical queries on incomplete knowledge graphs with missing edges are needed to solve the problem. |
| Approach: | They propose a query embedding method that encodes queries and entities to the same embeddable space and then selects the answer entities based on similarities . |
| Outcome: | The proposed method can answer complex logical queries on incomplete knowledge graphs with missing edges. |
DIVINE: A Generative Adversarial Imitation Learning Framework for Knowledge Graph Reasoning (D19-1)
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| Challenge: | Existing knowledge graph reasoning methods require numerous trials for path-finding and require meticulous reward engineering to fit specific datasets. |
| Approach: | They propose a plug-and-play framework that uses generative adversarial imitation learning to enhance existing RL-based methods. |
| Outcome: | The proposed framework improves existing RL-based methods while eliminating reward engineering. |
LATENTLOGIC: Learning Logic Rules in Latent Space over Knowledge Graphs (2023.findings-emnlp)
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| Challenge: | Existing methods for learning logic rules for knowledge graph reasoning face limitations such as searching in vast search space and inefficient optimization. |
| Approach: | They propose a framework to efficiently mine logic rules by controllable generation in the latent space by a pre-trained VAE and a discriminator. |
| Outcome: | The proposed framework efficiently mines logic rules by controllable generation in the latent space. |
TimeR4 : Time-aware Retrieval-Augmented Large Language Models for Temporal Knowledge Graph Question Answering (2024.emnlp-main)
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| Challenge: | Temporal Knowledge Graph Question Answering (TKGQA) aims to answer temporal questions using knowledge in Temporal knowledge graphs (TKTs). |
| Approach: | They propose a Time-aware retrieve-rewrite-retrieve-rerank framework to integrate temporal knowledge from TKGs into Large Language Models (LLMs) to reduce temporal hallucination, they propose rewrite module to rew questions using background knowledge stored in TKG's, then implement a retrieve-rank module to retrieve semantically and temporally relevant facts from Tkgs and rerank them according to temporal constraints. |
| Outcome: | The proposed approach achieves relative gains of 47.8% and 22.5% on two datasets, underscoring its effectiveness in boosting the temporal reasoning abilities of LLMs. |
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 . |
| Approach: | They propose a novel knowledge graph reasoning approach that uses a query-related fusion gate unit to model the sequentiality of relation composition and a buffering update mechanism to alleviate lagged entity information propagation. |
| Outcome: | Experimental results show that the proposed approach is superior on both transductive and inductive link prediction tasks. |
Inductive Reasoning on Few-Shot Knowledge Graphs with Task-Aware Language Models (2025.findings-emnlp)
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| Challenge: | Current knowledge graph reasoning methods rely on extensive structural information to perform in these few-shot scenarios. |
| Approach: | They propose a task-aware language model that activates the language model’s in-context learning ability for structured KG tasks. |
| Outcome: | The proposed method achieves state-of-the-art in few-shot scenarios while reducing the inference time required by previous methods. |