# Inductive Entity Representations from Text via Link Prediction

arXiv: https://arxiv.org/abs/2010.03496 · HTML: https://mlsanity.com/paper/2010.03496/

## Key facts

- Cited by 62 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2020.

## Built on by (most cited first, 30 of 62)

- [SimKGC: Simple Contrastive Knowledge Graph Completion with Pre-trained Language Models](https://mlsanity.com/paper/2203.02167/) (2022)
- [Towards Foundation Models for Knowledge Graph Reasoning](https://mlsanity.com/paper/2310.04562/) (2023)
- [NodePiece: Compositional and Parameter-Efficient Representations of Large Knowledge Graphs](https://mlsanity.com/paper/2106.12144/) (2021)
- [Large Language Models and Knowledge Graphs: Opportunities and Challenges](https://mlsanity.com/paper/2308.06374/) (2023)
- [InGram: Inductive Knowledge Graph Embedding via Relation Graphs](https://mlsanity.com/paper/2305.19987/) (2023)
- [Knowledge Enhanced Pretrained Language Models: A Compreshensive Survey](https://mlsanity.com/paper/2110.08455/) (2021)
- [Prompting as Probing: Using Language Models for Knowledge Base Construction](https://mlsanity.com/paper/2208.11057/) (2022)
- [Relational World Knowledge Representation in Contextual Language Models: A Review](https://mlsanity.com/paper/2104.05837/) (2021)
- [An Open Challenge for Inductive Link Prediction on Knowledge Graphs](https://mlsanity.com/paper/2203.01520/) (2022)
- [Scientific Language Models for Biomedical Knowledge Base Completion: An Empirical Study](https://mlsanity.com/paper/2106.09700/) (2021)
- [Zero-shot and Few-shot Learning with Knowledge Graphs: A Comprehensive Survey](https://mlsanity.com/paper/2112.10006/) (2021)
- [Improving Inductive Link Prediction Using Hyper-Relational Facts](https://mlsanity.com/paper/2107.04894/) (2021)
- [Geodesic Graph Neural Network for Efficient Graph Representation Learning](https://mlsanity.com/paper/2210.02636/) (2022)
- [Federated Graph Machine Learning: A Survey of Concepts, Techniques, and Applications](https://mlsanity.com/paper/2207.11812/) (2022)
- [Generalizing to Unseen Elements: A Survey on Knowledge Extrapolation for Knowledge Graphs](https://mlsanity.com/paper/2302.01859/) (2023)
- [Federated Graph Learning with Structure Proxy Alignment](https://mlsanity.com/paper/2408.09393/) (2024)
- [Type-aware Embeddings for Multi-Hop Reasoning over Knowledge Graphs](https://mlsanity.com/paper/2205.00782/) (2022)
- [Universal Link Predictor By In-Context Learning on Graphs](https://mlsanity.com/paper/2402.07738/) (2024)
- [Reasoning Through Memorization: Nearest Neighbor Knowledge Graph Embeddings](https://mlsanity.com/paper/2201.05575/) (2022)
- [RAILD: Towards Leveraging Relation Features for Inductive Link Prediction In Knowledge Graphs](https://mlsanity.com/paper/2211.11407/) (2022)
- [Entity-aware Transformers for Entity Search](https://mlsanity.com/paper/2205.00820/) (2022)
- [Vertical Federated Learning: Taxonomies, Threats, and Prospects](https://mlsanity.com/paper/2302.01550/) (2023)
- [SEMMA: A Semantic Aware Knowledge Graph Foundation Model](https://mlsanity.com/paper/2505.20422/) (2025)
- [E2EG: End-to-End Node Classification Using Graph Topology and Text-based Node Attributes](https://mlsanity.com/paper/2208.04609/) (2022)
- [Large Language Model Enhanced Knowledge Representation Learning: A Survey](https://mlsanity.com/paper/2407.00936/) (2024)
- [CascadER: Cross-Modal Cascading for Knowledge Graph Link Prediction](https://mlsanity.com/paper/2205.08012/) (2022)
- [BioBLP: A Modular Framework for Learning on Multimodal Biomedical Knowledge Graphs](https://mlsanity.com/paper/2306.03606/) (2023)
- [Linking Surface Facts to Large-Scale Knowledge Graphs](https://mlsanity.com/paper/2310.14909/) (2023)
- [Subgraph-Aware Training of Language Models for Knowledge Graph Completion Using Structure-Aware Contrastive Learning](https://mlsanity.com/paper/2407.12703/) (2024)
- [A Survey of Knowledge Enhanced Pre-trained Models](https://mlsanity.com/paper/2110.00269/) (2021)

## Abstract (arXiv)

Knowledge Graphs (KG) are of vital importance for multiple applications on the web, including information retrieval, recommender systems, and metadata annotation. Regardless of whether they are built manually by domain experts or with automatic pipelines, KGs are often incomplete. Recent work has begun to explore the use of textual descriptions available in knowledge graphs to learn vector representations of entities in order to preform link prediction. However, the extent to which these representations learned for link prediction generalize to other tasks is unclear. This is important given the cost of learning such representations. Ideally, we would prefer representations that do not need to be trained again when transferring to a different task, while retaining reasonable performance. In this work, we propose a holistic evaluation protocol for entity representations learned via a link prediction objective. We consider the inductive link prediction and entity classification tasks, which involve entities not seen during training. We also consider an information retrieval task for entity-oriented search. We evaluate an architecture based on a pretrained language model, that exhibits strong generalization to entities not observed during training, and outperforms related state-of-the-art methods (22% MRR improvement in link prediction on average). We further provide evidence that the learned representations transfer well to other tasks without fine-tuning. In the entity classification task we obtain an average improvement of 16% in accuracy compared with baselines that also employ pre-trained models. In the information retrieval task, we obtain significant improvements of up to 8.8% in NDCG@10 for natural language queries. We thus show that the learned representations are not limited KG-specific tasks, and have greater generalization properties than evaluated in previous work.