# Convolutional Complex Knowledge Graph Embeddings

arXiv: https://arxiv.org/abs/2008.03130 · HTML: https://mlsanity.com/paper/2008.03130/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Aug 2020.

## Built on by (most cited first, 21 of 21)

- [A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodal](https://mlsanity.com/paper/2212.05767/) (2022)
- [EvoLearner: Learning Description Logics with Evolutionary Algorithms](https://mlsanity.com/paper/2111.04879/) (2021)
- [Convolutional Hypercomplex Embeddings for Link Prediction](https://mlsanity.com/paper/2106.15230/) (2021)
- [I Know What You Do Not Know: Knowledge Graph Embedding via Co-distillation Learning](https://mlsanity.com/paper/2208.09828/) (2022)
- [Learning Concept Lengths Accelerates Concept Learning in ALC](https://mlsanity.com/paper/2107.04911/) (2021)
- [A Review of Knowledge Graph Completion](https://mlsanity.com/paper/2208.11652/) (2022)
- [A shallow neural model for relation prediction](https://mlsanity.com/paper/2101.09090/) (2021)
- [Neural Class Expression Synthesis](https://mlsanity.com/paper/2111.08486/) (2021)
- [LitCQD: Multi-Hop Reasoning in Incomplete Knowledge Graphs with Numeric Literals](https://mlsanity.com/paper/2304.14742/) (2023)
- [Trustworthy Knowledge Graph Completion Based on Multi-sourced Noisy Data](https://mlsanity.com/paper/2201.08580/) (2022)
- [Linked Papers With Code: The Latest in Machine Learning as an RDF Knowledge Graph](https://mlsanity.com/paper/2310.20475/) (2023)
- [Out-of-Vocabulary Entities in Link Prediction](https://mlsanity.com/paper/2105.12524/) (2021)
- [DRILL-- Deep Reinforcement Learning for Refinement Operators in ALC](https://mlsanity.com/paper/2106.15373/) (2021)
- [Two-view Graph Neural Networks for Knowledge Graph Completion](https://mlsanity.com/paper/2112.09231/) (2021)
- [Hardware-agnostic Computation for Large-scale Knowledge Graph Embeddings](https://mlsanity.com/paper/2207.08544/) (2022)
- [CoNO: Complex Neural Operator for Continous Dynamical Physical Systems](https://mlsanity.com/paper/2406.02597/) (2024)
- [Kronecker Decomposition for Knowledge Graph Embeddings](https://mlsanity.com/paper/2205.06560/) (2022)
- [Universal Knowledge Graph Embeddings](https://mlsanity.com/paper/2310.14899/) (2023)
- [HybridFC: A Hybrid Fact-Checking Approach for Knowledge Graphs](https://mlsanity.com/paper/2409.06692/) (2024)
- [Learning from Litigation: Graphs and LLMs for Retrieval and Reasoning in eDiscovery](https://mlsanity.com/paper/2405.19164/) (2024)
- [Performance Evaluation of Knowledge Graph Embedding Approaches under Non-adversarial Attacks](https://mlsanity.com/paper/2407.06855/) (2024)

## Abstract (arXiv)

In this paper, we study the problem of learning continuous vector representations of knowledge graphs for predicting missing links. We present a new approach called ConEx, which infers missing links by leveraging the composition of a 2D convolution with a Hermitian inner product of complex-valued embedding vectors. We evaluate ConEx against state-of-the-art approaches on the WN18RR, FB15K-237, KINSHIP and UMLS benchmark datasets. Our experimental results show that ConEx achieves a performance superior to that of state-of-the-art approaches such as RotatE, QuatE and TuckER on the link prediction task on all datasets while requiring at least 8 times fewer parameters. We ensure the reproducibility of our results by providing an open-source implementation which includes the training, evaluation scripts along with pre-trained models at https://github.com/conex-kge/ConEx.