# The DLCC Node Classification Benchmark for Analyzing Knowledge Graph Embeddings

arXiv: https://arxiv.org/abs/2207.06014 · HTML: https://mlsanity.com/paper/2207.06014/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 26 references that are in the map.
- First posted on arXiv 13 Jul 2022.

## Builds on (most cited first, 10 of 10)

- [Complex Embeddings for Simple Link Prediction](https://mlsanity.com/paper/1606.06357/) (2016)
- [Convolutional 2D Knowledge Graph Embeddings](https://mlsanity.com/paper/1707.01476/) (2017)
- [DGL-KE: Training Knowledge Graph Embeddings at Scale](https://mlsanity.com/paper/2004.08532/) (2020)
- [Open-World Knowledge Graph Completion](https://mlsanity.com/paper/1711.03438/) (2017)
- [Knowledge Graphs on the Web -- an Overview](https://mlsanity.com/paper/2003.00719/) (2020)
- [RDF2Vec Light -- A Lightweight Approach for Knowledge Graph Embeddings](https://mlsanity.com/paper/2009.07659/) (2020)
- [Walk Extraction Strategies for Node Embeddings with RDF2Vec in Knowledge Graphs](https://mlsanity.com/paper/2009.04404/) (2020)
- [Putting RDF2vec in Order](https://mlsanity.com/paper/2108.05280/) (2021)
- [KGvec2go -- Knowledge Graph Embeddings as a Service](https://mlsanity.com/paper/2003.05809/) (2020)
- [Walk this Way! Entity Walks and Property Walks for RDF2vec](https://mlsanity.com/paper/2204.02777/) (2022)

## Built on by (most cited first, 3 of 3)

- [Schema First! Learn Versatile Knowledge Graph Embeddings by Capturing Semantics with MASCHInE](https://mlsanity.com/paper/2306.03659/) (2023)
- [PyGraft: Configurable Generation of Synthetic Schemas and Knowledge Graphs at Your Fingertips](https://mlsanity.com/paper/2309.03685/) (2023)
- [gpuRDF2vec -- Scalable GPU-based RDF2vec](https://mlsanity.com/paper/2508.01073/) (2025)

## Abstract (arXiv)

Knowledge graph embedding is a representation learning technique that projects entities and relations in a knowledge graph to continuous vector spaces. Embeddings have gained a lot of uptake and have been heavily used in link prediction and other downstream prediction tasks. Most approaches are evaluated on a single task or a single group of tasks to determine their overall performance. The evaluation is then assessed in terms of how well the embedding approach performs on the task at hand. Still, it is hardly evaluated (and often not even deeply understood) what information the embedding approaches are actually learning to represent. To fill this gap, we present the DLCC (Description Logic Class Constructors) benchmark, a resource to analyze embedding approaches in terms of which kinds of classes they can represent. Two gold standards are presented, one based on the real-world knowledge graph DBpedia and one synthetic gold standard. In addition, an evaluation framework is provided that implements an experiment protocol so that researchers can directly use the gold standard. To demonstrate the use of DLCC, we compare multiple embedding approaches using the gold standards. We find that many DL constructors on DBpedia are actually learned by recognizing different correlated patterns than those defined in the gold standard and that specific DL constructors, such as cardinality constraints, are particularly hard to be learned for most embedding approaches.