# Type-Constrained Representation Learning in Knowledge Graphs

arXiv: https://arxiv.org/abs/1508.02593 · HTML: https://mlsanity.com/paper/1508.02593/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Aug 2015.

## Built on by (most cited first, 30 of 34)

- [Universal Representation Learning of Knowledge Bases by Jointly Embedding Instances and Ontological Concepts](https://mlsanity.com/paper/2103.08115/) (2021)
- [Role-Aware Modeling for N-ary Relational Knowledge Bases](https://mlsanity.com/paper/2104.09780/) (2021)
- [How to Turn Your Knowledge Graph Embeddings into Generative Models](https://mlsanity.com/paper/2305.15944/) (2023)
- [Link Prediction on N-ary Relational Data Based on Relatedness Evaluation](https://mlsanity.com/paper/2104.10424/) (2021)
- [Does Negative Sampling Matter? A Review with Insights into its Theory and Applications](https://mlsanity.com/paper/2402.17238/) (2024)
- [CAKE: A Scalable Commonsense-Aware Framework For Multi-View Knowledge Graph Completion](https://mlsanity.com/paper/2202.13785/) (2022)
- [Positive-Unlabeled Learning with Adversarial Data Augmentation for Knowledge Graph Completion](https://mlsanity.com/paper/2205.00904/) (2022)
- [KGBoost: A Classification-based Knowledge Base Completion Method with Negative Sampling](https://mlsanity.com/paper/2112.09340/) (2021)
- [Sem@K: Is my knowledge graph embedding model semantic-aware?](https://mlsanity.com/paper/2301.05601/) (2023)
- [GreenKGC: A Lightweight Knowledge Graph Completion Method](https://mlsanity.com/paper/2208.09137/) (2022)
- [Negative Sampling in Knowledge Graph Representation Learning: A Review](https://mlsanity.com/paper/2402.19195/) (2024)
- [A shallow neural model for relation prediction](https://mlsanity.com/paper/2101.09090/) (2021)
- [Out-of-Vocabulary Entities in Link Prediction](https://mlsanity.com/paper/2105.12524/) (2021)
- [Treat Different Negatives Differently: Enriching Loss Functions with Domain and Range Constraints for Link Prediction](https://mlsanity.com/paper/2303.00286/) (2023)
- [Schema First! Learn Versatile Knowledge Graph Embeddings by Capturing Semantics with MASCHInE](https://mlsanity.com/paper/2306.03659/) (2023)
- [A Systematic Investigation of KB-Text Embedding Alignment at Scale](https://mlsanity.com/paper/2106.01586/) (2021)
- [LogicRec: Recommendation with Users' Logical Requirements](https://mlsanity.com/paper/2304.11722/) (2023)
- [Exploiting Global Semantic Similarities in Knowledge Graphs by Relational Prototype Entities](https://mlsanity.com/paper/2206.08021/) (2022)
- [Walk-and-Relate: A Random-Walk-based Algorithm for Representation Learning on Sparse Knowledge Graphs](https://mlsanity.com/paper/2209.08769/) (2022)
- [Enhancing PyKEEN with Multiple Negative Sampling Solutions for Knowledge Graph Embedding Models](https://mlsanity.com/paper/2508.05587/) (2025)
- [Role Support in Knowledge-Graph Error Ranking: Predictor Regimes and Evaluation Policies](https://mlsanity.com/paper/2512.22318/) (2025)
- [Link Prediction or Perdition: the Seeds of Instability in Knowledge Graph Embeddings](https://mlsanity.com/paper/2606.03365/) (2026)
- [Combining Inductive and Deductive Reasoning for Query Answering over Incomplete Knowledge Graphs](https://mlsanity.com/paper/2106.14052/) (2021)
- [Hierarchical Relation-Guided Type-Sentence Alignment for Long-Tail Relation Extraction with Distant Supervision](https://mlsanity.com/paper/2109.09036/) (2021)
- [DegreEmbed: incorporating entity embedding into logic rule learning for knowledge graph reasoning](https://mlsanity.com/paper/2112.09933/) (2021)
- [Type-based Neural Link Prediction Adapter for Complex Query Answering](https://mlsanity.com/paper/2401.16045/) (2024)
- [Are We Wasting Time? A Fast, Accurate Performance Evaluation Framework for Knowledge Graph Link Predictors](https://mlsanity.com/paper/2402.00053/) (2024)
- [A Pluggable Common Sense-Enhanced Framework for Knowledge Graph Completion](https://mlsanity.com/paper/2410.04488/) (2024)
- [Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models](https://mlsanity.com/paper/2411.14858/) (2024)
- [FlowNeg: GFlowNet-Guided Diverse Hard Negative Sampling for Knowledge Graph Embedding](https://mlsanity.com/paper/2608.23849/) (2026)

## Abstract (arXiv)

Large knowledge graphs increasingly add value to various applications that require machines to recognize and understand queries and their semantics, as in search or question answering systems. Latent variable models have increasingly gained attention for the statistical modeling of knowledge graphs, showing promising results in tasks related to knowledge graph completion and cleaning. Besides storing facts about the world, schema-based knowledge graphs are backed by rich semantic descriptions of entities and relation-types that allow machines to understand the notion of things and their semantic relationships. In this work, we study how type-constraints can generally support the statistical modeling with latent variable models. More precisely, we integrated prior knowledge in form of type-constraints in various state of the art latent variable approaches. Our experimental results show that prior knowledge on relation-types significantly improves these models up to 77% in link-prediction tasks. The achieved improvements are especially prominent when a low model complexity is enforced, a crucial requirement when these models are applied to very large datasets. Unfortunately, type-constraints are neither always available nor always complete e.g., they can become fuzzy when entities lack proper typing. We show that in these cases, it can be beneficial to apply a local closed-world assumption that approximates the semantics of relation-types based on observations made in the data.