Papers with Knowledge graphs
Relation Prediction for Unseen-Entities Using Entity-Word Graphs (D19-53)
Copied to clipboard
Yuki Tagawa, Motoki Taniguchi, Yasuhide Miura, Tomoki Taniguchi, Tomoko Ohkuma, Takayuki Yamamoto, Keiichi Nemoto
| Challenge: | Knowledge graphs (KGs) are incomplete and miss some information. |
| Approach: | They propose to learn entity representations via a graph structure that uses Seen-entities, Unseen-Entities and words as nodes created from the descriptions of all entities. |
| Outcome: | The proposed method improves relation prediction for the entity pairs containing Unseen-entities. |
KGLM: Integrating Knowledge Graph Structure in Language Models for Link Prediction (2023.starsem-1)
Copied to clipboard
| Challenge: | Knowledge graphs are incomplete in the information they represent, necessitating knowledge graph completion tasks. |
| Approach: | They propose a new entity/relation embedding layer that learns to differentiate distinctive entity and relation types, thus allowing the model to learn the structure of the knowledge graph. |
| Outcome: | The proposed language model learns to differentiate distinct entity and relation types, thus learning the structure of the knowledge graph. |
LinkNBed: Multi-Graph Representation Learning with Entity Linkage (P18-1)
Copied to clipboard
| Challenge: | Knowledge graphs have emerged as an important model for studying complex multi-relational data. |
| Approach: | They propose a deep relational learning framework that learns entity and relationship representations across multiple graphs. |
| Outcome: | The proposed framework improves on the state-of-the-art relational learning approaches and identifies entity linkage across graphs. |
StATIK: Structure and Text for Inductive Knowledge Graph Completion (2022.findings-naacl)
Copied to clipboard
Elan Markowitz, Keshav Balasubramanian, Mehrnoosh Mirtaheri, Murali Annavaram, Aram Galstyan, Greg Ver Steeg
| Challenge: | Knowledge graphs (KGs) represent incomplete knowledge bases. |
| Approach: | They propose to use language models to extract semantic information from text descriptions while using Message Passing Neural Networks to capture structural information. |
| Outcome: | The proposed model achieves state of the art on three challenging inductive baselines. |
The Lifecycle of “Facts”: A Survey of Social Bias in Knowledge Graphs (2022.aacl-main)
Copied to clipboard
| Challenge: | Knowledge graphs are used in a variety of downstream tasks and in hybrid AI systems. |
| Approach: | They propose to examine the lifecycle of knowledge graphs with respect to bias influences. |
| Outcome: | The proposed models are based on the lifecycle of knowledge graphs and their embedded versions . they show that the KGs manifest biases and propagate harmful prejudices . |
REMATCH: Robust and Efficient Matching of Local Knowledge Graphs to Improve Structural and Semantic Similarity (2024.findings-naacl)
Copied to clipboard
| Challenge: | Existing AMR metrics are inefficient and struggle to capture semantic similarity . Existing metrics are not efficient and lack a systematic evaluation benchmark . |
| Approach: | They propose a new AMR similarity metric, rematch, which matches graphs structurally and semantically to each other. |
| Outcome: | The proposed metric is five times faster than the next most efficient metric. |
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. |
A Hierarchical N-Gram Framework for Zero-Shot Link Prediction (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to zero-shot link prediction use textual features of relations as auxiliary information to improve the encoded representation. |
| Approach: | They propose a Hierarchical N-gram framework for Zero-Shot Link Prediction that leverages character n-gram information for ZSLP. |
| Outcome: | The proposed method achieves state-of-the-art on two standard ZSLP datasets. |
Retrofitting Distributional Embeddings to Knowledge Graphs with Functional Relations (C18-1)
Copied to clipboard
| Challenge: | Existing methods for retrofitting knowledge graph embeddings assume connected entities have similar embeddments, but these assumptions are not true for large knowledge graphs. |
| Approach: | They propose to retrofit distributional and relational data to a knowledge graph structure . they propose to explicitly model pairwise relations to overcome these limitations . |
| Outcome: | The proposed framework outperforms existing retrofitting methods on complex knowledge graphs and loses no accuracy on simpler graphs. |
CAKE: A Scalable Commonsense-Aware Framework For Multi-View Knowledge Graph Completion (2022.acl-long)
Copied to clipboard
| Challenge: | Existing knowledge graph embedding techniques rely on fact-view data to predict missing links between entities, limiting their performance. |
| Approach: | They propose a commonsense-aware knowledge embedding framework which generates commonsensense from factual triples with entity concepts for a KGC task. |
| Outcome: | The proposed framework could produce high-quality negative triples and joint commonsense and fact-view link prediction. |
Edge: Enriching Knowledge Graph Embeddings with External Text (2021.naacl-main)
Copied to clipboard
| Challenge: | Knowledge graphs suffer from sparsity which degrades the quality of representations generated by various methods. |
| Approach: | They propose a knowledge graph enrichment framework called Edge to enhance knowledge graphs based on "hard" co-occurrence of words in knowledge graph entities and external text. |
| Outcome: | The proposed framework achieves "soft" augmentation by combining external text with knowledge graph entities. |
One-Shot Relational Learning for Knowledge Graphs (D18-1)
Copied to clipboard
| 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. |
| Approach: | They propose a one-shot relational learning framework that utilizes the knowledge distilled by embedding models and learns a matching metric by considering both the learned embeddments and one-hop graph structures. |
| Outcome: | The proposed framework improves on existing embedding models and eliminates the need for retraining when dealing with newly added relations. |
SAC-KG: Exploiting Large Language Models as Skilled Automatic Constructors for Domain Knowledge Graph (2024.acl-long)
Copied to clipboard
| Challenge: | Existing KG construction methods rely on human intervention to attain qualified KGs, which severely hinders the practical application of domain KG. |
| Approach: | They propose a general KG construction framework that uses large language models as "S**killed" A**utomatic C**onstructors for domain knowledge (G**raph) |
| Outcome: | The proposed framework generates specialized multi-level knowledge graphs at the scale of over one million nodes and achieves 89.32% precision rate compared to state-of-the-art methods. |
Faithfully Explainable Recommendation via Neural Logic Reasoning (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing models for explainable recommendation have neglected faithfulness of KG reasoning . |
| Approach: | They propose to draw on interpretable logical rules to guide path-reasoning process for explanation generation. |
| Outcome: | The proposed method delivers high-quality recommendations and ascertains the faithfulness of the derived explanation. |
DIVINE: A Generative Adversarial Imitation Learning Framework for Knowledge Graph Reasoning (D19-1)
Copied to clipboard
| 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. |
GMH: A General Multi-hop Reasoning Model for KG Completion (2021.emnlp-main)
Copied to clipboard
| Challenge: | Knowledge graphs are incomplete with many facts missing, causing performance bottlenecks in many applications. |
| Approach: | They propose a general multi-hop reasoning task that can be formulated as a search process and can be extended to long-distance reasoning scenarios. |
| Outcome: | The proposed model improves on baselines in short and long distance reasoning scenarios. |
Wikontic: Constructing Wikidata-Aligned, Ontology-Aware Knowledge Graphs with Large Language Models (2026.eacl-long)
Copied to clipboard
| Challenge: | Knowledge graphs provide structured, verifiable grounding for large language models . current LLMs use KGs as auxiliary structures for text retrieval . |
| Approach: | They propose a pipeline that constructs KGs from open-domain texts using triplets and qualifiers. |
| Outcome: | The proposed pipeline outperforms existing methods in retrieval-augmented generation. |
Does Pre-trained Language Model Actually Infer Unseen Links in Knowledge Graph Completion? (2024.naacl-long)
Copied to clipboard
| Challenge: | Knowledge Graph Completion (KGC) is a task that infers unseen relationships between entities . traditional embedding-based methods infer missing links using only training data . a pre-trained language model (PLM)-based KGC may be ineffective in practical applications . |
| Approach: | They propose to use knowledge Graph Completion (KGC) to infer unseen relationships . traditional embedding-based KGC methods infer missing links only from training data . they argue that pre-trained language models acquire inference abilities through pre-training . |
| Outcome: | The proposed method improves performance even though it does not use memorized knowledge. |
Knowledge Graph Embeddings using Neural Ito Process: From Multiple Walks to Stochastic Trajectories (2023.findings-acl)
Copied to clipboard
Mojtaba Nayyeri, Bo Xiong, Majid Mohammadi, Mst. Mahfuja Akter, Mirza Mohtashim Alam, Jens Lehmann, Steffen Staab
| Challenge: | Existing knowledge graph embeddings have problems expressing knowledge graphs because they model a specific relation r from a head h to tails by transitioning deterministically to exactly one other point in the embeddable space. |
| Approach: | They propose a framework that models relations between nodes by relation-specific, stochastic transitions. |
| Outcome: | The proposed framework is expressive and generic subsuming state-of-the-art models operating on low-dimensional manifolds. |
VISTA: Visual-Textual Knowledge Graph Representation Learning (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing knowledge graph embedding methods only consider the structure of a knowledge graph, but some recent proposed methods utilize images or text descriptions of entities in a VTKG. |
| Approach: | They propose a visual-textual knowledge graph (VTKG) where triplets can be explained using images and entities and relations can accompany text descriptions. |
| Outcome: | The proposed method outperforms state-of-the-art knowledge graph completion methods in real-world knowledge graphs. |
TuckER: Tensor Factorization for Knowledge Graph Completion (D19-1)
Copied to clipboard
| Challenge: | Knowledge graphs contain only a small subset of all possible facts . link prediction is a task of inferring missing facts based on existing facts - knowledge graphs are expensive and lack of information is needed to add new information. |
| Approach: | They propose a linear model based on Tucker decomposition of knowledge graph triples . they show that the model is expressive and has sufficient bounds on its embedding dimensionalities . |
| Outcome: | The proposed model outperforms state-of-the-art models across standard datasets and acts as a strong baseline for more elaborate models. |
An Unsupervised Joint System for Text Generation from Knowledge Graphs and Semantic Parsing (2020.emnlp-main)
Copied to clipboard
| Challenge: | Knowledge graphs (KGs) vary greatly from one domain to another, resulting in a lack of domain-specific parallel graph-text data. |
| Approach: | They propose an unsupervised approach to graph-to-text generation and text-to graph knowledge extraction using WebNLG v2.1 and a new benchmark leveraging scene graphs from Visual Genome. |
| Outcome: | The proposed approach outperforms baselines on WebNLG v2.1 and a new benchmark leveraging scene graphs from Visual Genome. |
Are Message Passing Neural Networks Really Helpful for Knowledge Graph Completion? (2023.acl-long)
Copied to clipboard
| Challenge: | Existing knowledge graphs are far from complete with large portions of triplets missing. |
| Approach: | They propose to use Graph Neural Networks to learn powerful embeddings to improve model performance. |
| Outcome: | The proposed models achieve comparable performance to MLP models, suggesting that MP may not be as crucial as previously thought. |
Generating Domain-Specific Knowledge Graphs from Large Language Models (2025.findings-acl)
Copied to clipboard
| Challenge: | Large language models (LLMs) have shown impressive world knowledge across different benchmarks and domains but their knowledge is inconveniently scattered across their billions of parameters. |
| Approach: | They propose a prompt-based method to extract knowledge solely from LLMs’ parameters to construct domain-specific KGs by a schema-based process. |
| Outcome: | The proposed method generates large domain-specific KGs containing tens of thousands of entities and relations, and then evaluates against Wikidata, an open-source human-created KG. |
Tree-of-Traversals: A Zero-Shot Reasoning Algorithm for Augmenting Black-box Language Models with Knowledge Graphs (2024.acl-long)
Copied to clipboard
Elan Markowitz, Anil Ramakrishna, Jwala Dhamala, Ninareh Mehrabi, Charith Peris, Rahul Gupta, Kai-Wei Chang, Aram Galstyan
| Challenge: | Knowledge graphs (KGs) complement Large Language Models (LLMs) by providing reliable, structured, domain-specific, and up-to-date external knowledge. |
| Approach: | They propose a zero-shot reasoning algorithm that augments black-box LLMs with one or more KGs. |
| Outcome: | The proposed algorithm significantly improves performance on question answering and KG question answering tasks. |
A Framework of Knowledge Graph-Enhanced Large Language Model Based on Question Decomposition and Atomic Retrieval (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods to enhance LLMs with knowledge graphs have limited results . knowledge graph question answering (KGQA) provides interpretable reasoning for large language models . |
| Approach: | They propose a framework for KG-enhanced LLM based on question decomposition and atomic retrieval . they propose question decomposing tree as framework for LLM reasoning . |
| Outcome: | The proposed framework outperforms existing reasoning-based baselines on KGQA datasets. |
Inductive Reasoning on Few-Shot Knowledge Graphs with Task-Aware Language Models (2025.findings-emnlp)
Copied to clipboard
| 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. |
Faithful Knowledge Graph Explanations in Commonsense Question Answering (2022.emnlp-main)
Copied to clipboard
| Challenge: | Knowledge graphs are used to express explanations for the model's answer choice. |
| Approach: | They propose to use knowledge graphs to encode facts separately from the question and combine them to select an answer. |
| Outcome: | The proposed architectures can be used to express the facts used to answer a question in a graph-based explanation, but they will not include reasoning done by the transformer encoding the question, and will be incomplete. |
Context-Aware Adapter Tuning for Few-Shot Relation Learning in Knowledge Graphs (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods to predict instances for missing relations on knowledge graphs are limited by their limited training examples. |
| Approach: | They propose a context-aware adapter for few-shot relation learning in KGs . they propose tunable relation adaptation and contextual information for each relation . |
| Outcome: | Experiments on three benchmark KGs validate the superiority of RelAdapter over state-of-the-art methods. |
HyperKGR: Knowledge Graph Reasoning in Hyperbolic Space with Graph Neural Network Encoding Symbolic Path (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for linking knowledge graphs are incomplete and rely on Euclidean embeddings . a hyperbolic GNN framework embeds recursive learning trees in hyperbolical space . |
| Approach: | They propose a hyperbolic GNN framework that embeds recursive learning trees in hyperbolical space and generates query-specific embeddings. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on multiple benchmark datasets. |
LLMs as Knowledge Graph Refiners: Mitigating Factual Inconsistencies in Generative Knowledge Extraction (2026.acl-long)
Copied to clipboard
| Challenge: | Knowledge graphs (KGs) represent real-world entities and their relations in a structured form. |
| Approach: | They propose a framework that performs triple-level refinement on KGs constructed via GKE. |
| Outcome: | The proposed framework improves KG quality from diverse perspectives. |
LogosKG: Hardware-Optimized Scalable and Interpretable Knowledge Graph Retrieval (2026.acl-long)
Copied to clipboard
He Cheng, Yifu Wu, Saksham Khatwani, Maya Kruse, Dmitriy Dligach, Timothy A. Miller, Majid Afshar, Yanjun Gao
| Challenge: | Existing systems struggle to balance efficiency, scalability, and interpretability. |
| Approach: | They propose a hardware-aligned framework that enables scalable and interpretable k-hop retrieval on large KGs. |
| Outcome: | The proposed framework scales to billion-edge graphs without loss of retrieval fidelity. |