Papers with entity representations
Relation Prediction for Unseen-Entities Using Entity-Word Graphs (D19-53)
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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. |
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. |
| Outcome: | The proposed model outperforms baseline models on WordNet, Freebase, and DBpedia. |
An Improved Baseline for Sentence-level Relation Extraction (2022.aacl-short)
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| Challenge: | Sentence-level relation extraction (RE) aims at identifying the relationship between two entities in a sentence. |
| Approach: | They propose to improve sentence-level relation extraction by adding entity representations with typed markers to the model. |
| Outcome: | The proposed model outperforms existing methods on entity representation and noisy labels on TACRED dataset. |
EntEval: A Holistic Evaluation Benchmark for Entity Representations (D19-1)
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| Challenge: | EntEval is a test suite of tasks that require nontrivial understanding of entities. |
| Approach: | They propose to encode the mention context or the Wikipedia hyperlink annotations to learn better entity representations. |
| Outcome: | The proposed model improves strong baselines on multiple EntEval tasks. |
EmRel: Joint Representation of Entities and Embedded Relations for Multi-triple Extraction (2022.naacl-main)
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| Challenge: | Existing studies only explore entity representations, but propose a novel triple perspective for relation extraction. |
| Approach: | They propose to explicitly introduce relation representation and jointly represent it with entities to identify valid triples. |
| Outcome: | The proposed method is based on ablations and document-level relation extraction and joint entity and relation extraction. |
Interpretable Entity Representations through Large-Scale Typing (2020.findings-emnlp)
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| Challenge: | In traditional methods for natural language processing, entities are embedded in dense vector spaces with pre-trained models. |
| Approach: | They propose an approach to creating entity representations that are human readable and achieve high performance on entity-related tasks out of the box. |
| Outcome: | The proposed representations are vectors whose values correspond to posterior probabilities over fine-grained entity types, indicating the confidence of a typing model’s decision that the entity belongs to the corresponding type. |
Mitigating Degree Bias in Hypergraphs via Attribute-as-Structure Approach (2026.eacl-long)
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| Challenge: | Existing “attribute-as-feature” approaches do not create new aggregation pathways for sparsely connected entities. |
| Approach: | They propose an “attribute-as-structure” approach specifically designed for heterogeneous hypergraphs that integrates attributes directly into the hypergraph topology as distinct node types. |
| Outcome: | The proposed approach integrates attributes directly into the hypergraph topology as distinct node types, creating new structural pathways to enrich sparsely connected entities while preserving semantic distinctiveness within complex complex interactions. |
Adaptive Convolution for Multi-Relational Learning (N19-1)
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| Challenge: | Existing convolutional neural networks fail to model full interactions between entities and relations, which limits the performance of link prediction. |
| Approach: | They propose a convolutional network that maximizes entity-relation interactions in a convergent fashion. |
| Outcome: | The proposed convolutional network performs better than baseline models on multiple datasets. |
Global Structure Knowledge-Guided Relation Extraction Method for Visually-Rich Document (2023.findings-emnlp)
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| Challenge: | Existing methods focus on manipulating entity features to find pairwise relations, yet neglect the more fundamental structural information that links disparate entity pairs together. |
| Approach: | They propose a Visual Relation Extraction framework that generates relation predictions on entity pairs extracted from scanned images and incorporates global structural knowledge into the representations of the entities. |
| Outcome: | The proposed framework outperforms existing methods in fine-tuning setting and yields stronger data-efficient performance in the low-resource setting. |
Language Models as Knowledge Bases: On Entity Representations, Storage Capacity, and Paraphrased Queries (2021.eacl-main)
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| Challenge: | Pretrained language models have been suggested as an alternative or complement to structured knowledge bases . however, this paradigm has only been considered in a very limited setting . |
| Approach: | They propose a paradigm that allows LMs to store a large number of entities . they propose LM-as-KB paradigm which allows querying stored facts . |
| Outcome: | The proposed paradigm allows handling 21k entities whose name is found in common LM vocabularies . the proposed paradigm has only been considered in a very limited setting . |
HyperCL: A Contrastive Learning Framework for Hyper-Relational Knowledge Graph Embedding with Hierarchical Ontology (2024.findings-acl)
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| Challenge: | Existing studies neglect the ontology of knowledge Graph (KG) embeddings and suffer from the dominance issue of facts over ontologies. |
| Approach: | They propose a framework for hyper-relational KG embeddings that captures the hierarchical ontology and a concept-aware contrastive loss to alleviate the dominance issue. |
| Outcome: | The proposed framework improves on three real-world datasets and shows that it can integrate with other embedding methods and improve link prediction performance. |
Named Entity Recognition for Entity Linking: What Works and What’s Next (2021.findings-emnlp)
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| Challenge: | Entity Linking (EL) systems have achieved impressive results on standard benchmarks thanks to the contextualized representations provided by recent pretrained language models. |
| Approach: | They propose to exploit Named Entity Recognition (NER) to narrow the gap between EL systems trained on high and low amounts of labeled data. |
| Outcome: | The proposed model can be exploited to narrow the gap between EL systems trained on high and low amounts of labeled data. |
SimKGC: Simple Contrastive Knowledge Graph Completion with Pre-trained Language Models (2022.acl-long)
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| Challenge: | Text-based methods lag behind graph embedding-based approaches for knowledge graph completion (KGC) |
| Approach: | They propose three types of negatives to improve contrastive learning to improve learning efficiency. |
| Outcome: | The proposed model outperforms embedding-based methods on several benchmark datasets. |
What do Entity-Centric Models Learn? Insights from Entity Linking in Multi-Party Dialogue (N19-1)
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| Challenge: | a recent study suggests that models that incorporate a bias towards learning entity representations are not effective at modeling entities. |
| Approach: | They propose to use two entity-centric models for a referential task . they show they outperform the state of the art and do better on lower frequency entities . |
| Outcome: | The proposed models outperform the state of the art on a referential task . they do better on lower frequency entities than a counterpart model not entity-centric . |
A Framework for Adapting Pre-Trained Language Models to Knowledge Graph Completion (2022.emnlp-main)
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| Challenge: | Recent work has demonstrated that entity representations can be extracted from pre-trained language models to develop knowledge graph completion models that are more robust to the naturally occurring sparsity found in knowledge graphs. |
| Approach: | They propose unsupervised and supervised methods to extract more informative representations from pre-trained language models to develop knowledge graph completion models. |
| Outcome: | The proposed model outperforms recent neural models in terms of performance and unsupervised processing methods. |
CateEA: Enhancing Entity Alignment via Implicit Category Supervision (2025.coling-main)
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| Challenge: | Existing Entity Alignment methods neglect the inherent semantic information of entities, limiting alignment precision and robustness. |
| Approach: | They propose to combine implicit category information into multi-modal representations by generating pseudo-category labels from entity embeddings and integrating them into a multi-task learning framework. |
| Outcome: | Experiments on benchmark datasets show that CateEA outperforms state-of-the-art methods in various settings. |
A Unified Generative Framework for Various NER Subtasks (2021.acl-long)
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| Challenge: | Named Entity Recognition (NER) is the task of identifying spans that represent entities in sentences. |
| Approach: | They propose to formulate NER subtasks as entity span sequence generation task . framework can be used to solve all three kinds of NER tasks without tagging schema . |
| Outcome: | The proposed framework achieves state-of-the-art (SoTA) or near SoTA performance on eight English NER datasets. |
mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models (2022.acl-long)
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| Challenge: | Existing methods for improving multilingual models only use entity information in pretraining and do not explicitly use entities in downstream tasks. |
| Approach: | They propose to leverage Wikipedia entity representations for downstream tasks . they train a multilingual language model with 24 languages with entity representation . |
| Outcome: | The proposed model outperforms word-based models in cross-lingual transfer tasks. |
Large Language Models Can Learn Representation in Natural Language (2024.findings-acl)
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| Challenge: | Large Language Models (LLMs) are unable to complete complex tasks involving multiple entities, such as tool APIs. |
| Approach: | They propose a method which uses natural language representations to refine entity descriptions for improved retrieval and LLM utilization. |
| Outcome: | The proposed method improves GPT-4's performance on classification tasks and API call tasks. |
Natural Evolution-based Dual-Level Aggregation for Temporal Knowledge Graph Reasoning (2024.findings-emnlp)
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| Challenge: | Existing models ignore asynchronous characteristics of event evolution, resulting in suboptimal performance. |
| Approach: | They propose a Natural Evolution-based Dual-level Aggregation framework for TKG reasoning that incorporates asynchronous characteristics of event evolution into the model. |
| Outcome: | The proposed model incorporates the asynchronous characteristics of event evolution for representation computation, thus improving prediction performance. |
DyERNIE: Dynamic Evolution of Riemannian Manifold Embeddings for Temporal Knowledge Graph Completion (2020.emnlp-main)
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| Challenge: | Existing embedding approaches for temporal knowledge graphs typically learn entity representations and their dynamic evolution in the Euclidean space. |
| Approach: | They propose a non-Euclidean embedding approach that learns evolving entity representations in a product of Riemannian manifolds. |
| Outcome: | The proposed model improves on three real-world datasets showing that the embeddings on Riemannian manifolds can capture the evolution of temporal KGs. |
Single Sequence Prediction over Reasoning Graphs for Multi-hop QA (2023.acl-long)
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| Challenge: | Recent generative approaches for multi-hop question answering (QA) use fusion-in-decoder to generate a single sequence output . but, they often have difficulty accurately identifying passages corresponding to key entities in the context . |
| Approach: | They propose a single-sequence prediction method that integrates a graph structure linking key entities in each context passage to relevant subsequent passages for each question. |
| Outcome: | The proposed method improves answer exact-match/F1 scores and faithfulness of grounding on the hotpotQA dataset and achieves state-of-the-art numbers on the Musique dataset. |
Time-aware Graph Neural Network for Entity Alignment between Temporal Knowledge Graphs (2021.emnlp-main)
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| Challenge: | Existing embedding-based approaches disregard time information that exists in many large-scale knowledge graphs, leaving much room for improvement. |
| Approach: | They propose a Time-aware Entity Alignment approach that incorporates relation and time information into a vector space and uses Graph Neural Networks to learn entity representations. |
| Outcome: | The proposed approach outperforms the state-of-the-art methods on real-world TKG datasets due to the inclusion of time information. |
GS-Quant: Granular Semantic and Generative Structural Quantization for Knowledge Graph Completion (2026.acl-long)
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| Challenge: | Existing quantization-based approaches to knowledge Graph Completion (KGC) are incomplete. |
| Approach: | They propose a framework that generates semantically coherent discrete codes for KG entities . they introduce a Granular Semantic Enhancement module that injects hierarchical knowledge into the codebook . |
| Outcome: | The proposed framework outperforms existing text-based and embedding-based baselines in the KGC domain. |
Double-Branch Multi-Attention based Graph Neural Network for Knowledge Graph Completion (2023.acl-long)
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| Challenge: | Existing knowledge graph embedding methods cannot capture local and global information and are not designed well to learn representations of seen entities with sparse neighborhoods in isolated subgraphs. |
| Approach: | They propose a double-branch multi-attention based graph neural network to learn more expressive entity representations which contain rich global-local structural information. |
| Outcome: | The proposed method outperforms a general GNN-based approach for KGC. |
Medical Entity Disambiguation with Medical Mention Relation and Fine-grained Entity Knowledge (2024.lrec-main)
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| Challenge: | Existing methods for medical entity disambiguation (MED) fail to fully utilize the knowledge within medical knowledge bases (KBs) Existing models overlook essential interactions between medical mentions and candidate entities, resulting in knowledge- and interaction-inefficient modeling and suboptimal disambiguations performance. |
| Approach: | They propose to combine a mention relation fusion module and an entity knowledge fusion modules to map medical mentions to corresponding entities in a knowledge base (KB) |
| Outcome: | The proposed method outperforms state-of-the-art MED models on two publicly available real-world datasets. |
RAED: Retrieval-Augmented Entity Description Generation for Emerging Entity Linking and Disambiguation (2025.emnlp-main)
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| Challenge: | Entity Linking and Entity Disambiguation systems assume static knowledge bases are incomplete and up-to-date, rendering them incapable of handling entities not yet included in the knowledge base. |
| Approach: | They propose a model that retrieves external knowledge to improve factual grounding in entity descriptions. |
| Outcome: | The proposed model outperforms systems that require fixed knowledge sets on Entity Disambiguation and Wikipedia to improve factual grounding in entity descriptions. |
Evolving Beyond Snapshots: Harmonizing Structure and Sequence via Entity State Tuning for Temporal Knowledge Graph Forecasting (2026.acl-long)
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| Challenge: | Temporal knowledge graphs (TKGs) require predicting future facts by modeling structural dependencies within each snapshot and temporal evolution across snapshots. |
| Approach: | They propose an encoder-agnostic framework that provides persistent entity states . EST maintains a global state buffer and aligns structural evidence with sequential signals . |
| Outcome: | Experiments show that EST improves diverse backbones and achieves state-of-the-art performance. |
Factual Retrieval in LLMs Is a Redundant, Distributed and Non-Contiguous Process (2026.acl-long)
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| Challenge: | Existing work posits that factual knowledge is stored at the last entity token position, but the precise mechanics of how facts are retrieved from model parameters remain unclear. |
| Approach: | They propose an iterative patching protocol to identify a minimal subset of layers necessary for attribute retrieval. |
| Outcome: | The proposed method shows that models possess multiple paths for the same entity and fact, highlighting a high degree of redundancy in attribute computation. |