# Differentiating Concepts and Instances for Knowledge Graph Embedding

arXiv: https://arxiv.org/abs/1811.04588 · HTML: https://mlsanity.com/paper/1811.04588/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2018.

## Built on by (most cited first, 27 of 27)

- [A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodal](https://mlsanity.com/paper/2212.05767/) (2022)
- [Knowledge Graph Embedding: A Survey from the Perspective of Representation Spaces](https://mlsanity.com/paper/2211.03536/) (2022)
- [Universal Representation Learning of Knowledge Bases by Jointly Embedding Instances and Ontological Concepts](https://mlsanity.com/paper/2103.08115/) (2021)
- [COPEN: Probing Conceptual Knowledge in Pre-trained Language Models](https://mlsanity.com/paper/2211.04079/) (2022)
- [OntoEA: Ontology-guided Entity Alignment via Joint Knowledge Graph Embedding](https://mlsanity.com/paper/2105.07688/) (2021)
- [Acquiring and Modelling Abstract Commonsense Knowledge via Conceptualization](https://mlsanity.com/paper/2206.01532/) (2022)
- [Context-aware Entity Typing in Knowledge Graphs](https://mlsanity.com/paper/2109.07990/) (2021)
- [Ontology Embedding: A Survey of Methods, Applications and Resources](https://mlsanity.com/paper/2406.10964/) (2024)
- [Sem@K: Is my knowledge graph embedding model semantic-aware?](https://mlsanity.com/paper/2301.05601/) (2023)
- [Knowledge Graph Embedding: An Overview](https://mlsanity.com/paper/2309.12501/) (2023)
- [Text-Augmented Open Knowledge Graph Completion via Pre-Trained Language Models](https://mlsanity.com/paper/2305.15597/) (2023)
- [KGSynNet: A Novel Entity Synonyms Discovery Framework with Knowledge Graph](https://mlsanity.com/paper/2103.08893/) (2021)
- [Node Classification Meets Link Prediction on Knowledge Graphs](https://mlsanity.com/paper/2106.07297/) (2021)
- [Treat Different Negatives Differently: Enriching Loss Functions with Domain and Range Constraints for Link Prediction](https://mlsanity.com/paper/2303.00286/) (2023)
- [AsyncET: Asynchronous Learning for Knowledge Graph Entity Typing with Auxiliary Relations](https://mlsanity.com/paper/2308.16055/) (2023)
- [Embedding Ontologies via Incorporating Extensional and Intensional Knowledge](https://mlsanity.com/paper/2402.01677/) (2024)
- [Arbitrary Time Information Modeling via Polynomial Approximation for Temporal Knowledge Graph Embedding](https://mlsanity.com/paper/2405.00358/) (2024)
- [Diagnosing and Mitigating Semantic Inconsistencies in Wikidata's Classification Hierarchy](https://mlsanity.com/paper/2511.04926/) (2025)
- [KGRefiner: Knowledge Graph Refinement for Improving Accuracy of Translational Link Prediction Methods](https://mlsanity.com/paper/2106.14233/) (2021)
- [Link Analysis meets Ontologies: Are Embeddings the Answer?](https://mlsanity.com/paper/2111.11710/) (2021)
- [KB-Plugin: A Plug-and-play Framework for Large Language Models to Induce Programs over Low-resourced Knowledge Bases](https://mlsanity.com/paper/2402.01619/) (2024)
- [KC-GenRe: A Knowledge-constrained Generative Re-ranking Method Based on Large Language Models for Knowledge Graph Completion](https://mlsanity.com/paper/2403.17532/) (2024)
- [Subgraph2vec: A random walk-based algorithm for embedding knowledge graphs](https://mlsanity.com/paper/2405.02240/) (2024)
- [Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods](https://mlsanity.com/paper/2506.05626/) (2025)
- [Return of the Schema: Building Complete Datasets for Machine Learning and Reasoning on Knowledge Graphs](https://mlsanity.com/paper/2602.14795/) (2026)
- [SparseTransX: Efficient Training of Translation-Based Knowledge Graph Embeddings Using Sparse Matrix Operations](https://mlsanity.com/paper/2502.16949/) (2025)
- [CORE: Cyclic Orthotope Relation Embedding for Knowledge Graph Completion](https://mlsanity.com/paper/2605.11159/) (2026)

## Abstract (arXiv)

Concepts, which represent a group of different instances sharing common properties, are essential information in knowledge representation. Most conventional knowledge embedding methods encode both entities (concepts and instances) and relations as vectors in a low dimensional semantic space equally, ignoring the difference between concepts and instances. In this paper, we propose a novel knowledge graph embedding model named TransC by differentiating concepts and instances. Specifically, TransC encodes each concept in knowledge graph as a sphere and each instance as a vector in the same semantic space. We use the relative positions to model the relations between concepts and instances (i.e., instanceOf), and the relations between concepts and sub-concepts (i.e., subClassOf). We evaluate our model on both link prediction and triple classification tasks on the dataset based on YAGO. Experimental results show that TransC outperforms state-of-the-art methods, and captures the semantic transitivity for instanceOf and subClassOf relation. Our codes and datasets can be obtained from https:// github.com/davidlvxin/TransC.