# Veni, Vidi, Vici: Solving the Myriad of Challenges before Knowledge Graph Learning

arXiv: https://arxiv.org/abs/2402.06098 · HTML: https://mlsanity.com/paper/2402.06098/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 40 references that are in the map.
- First posted on arXiv 8 Feb 2024.

## Builds on (most cited first, 21 of 21)

- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [Variational Graph Auto-Encoders](https://mlsanity.com/paper/1611.07308/) (2016)
- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [The Mythos of Model Interpretability](https://mlsanity.com/paper/1606.03490/) (2016)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [Explainability in Graph Neural Networks: A Taxonomic Survey](https://mlsanity.com/paper/2012.15445/) (2020)
- [A Review of Relational Machine Learning for Knowledge Graphs](https://mlsanity.com/paper/1503.00759/) (2015)
- [Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings](https://mlsanity.com/paper/2002.05969/) (2020)
- [Complex Query Answering with Neural Link Predictors](https://mlsanity.com/paper/2011.03459/) (2020)
- [Bringing Light Into the Dark: A Large-scale Evaluation of Knowledge Graph Embedding Models Under a Unified Framework](https://mlsanity.com/paper/2006.13365/) (2020)
- [Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning](https://mlsanity.com/paper/1903.08948/) (2019)
- [EL Embeddings: Geometric construction of models for the Description Logic EL ++](https://mlsanity.com/paper/1902.10499/) (2019)
- [Co-embedding of Nodes and Edges with Graph Neural Networks](https://mlsanity.com/paper/2010.13242/) (2020)
- [Neuro-symbolic representation learning on biological knowledge graphs](https://mlsanity.com/paper/1612.04256/) (2016)
- [Investigating Robustness and Interpretability of Link Prediction via Adversarial Modifications](https://mlsanity.com/paper/1905.00563/) (2019)
- [Neural Multi-Hop Reasoning With Logical Rules on Biomedical Knowledge Graphs](https://mlsanity.com/paper/2103.10367/) (2021)
- [Adversarial Attacks on Knowledge Graph Embeddings via Instance Attribution Methods](https://mlsanity.com/paper/2111.03120/) (2021)
- [Explainability in Graph Neural Networks: An Experimental Survey](https://mlsanity.com/paper/2203.09258/) (2022)
- [Implications of Topological Imbalance for Representation Learning on Biomedical Knowledge Graphs](https://mlsanity.com/paper/2112.06567/) (2021)
- [Learning Embeddings from Knowledge Graphs With Numeric Edge Attributes](https://mlsanity.com/paper/2105.08683/) (2021)

## Abstract (arXiv)

Knowledge Graphs (KGs) have become increasingly common for representing large-scale linked data. However, their immense size has required graph learning systems to assist humans in analysis, interpretation, and pattern detection. While there have been promising results for researcher- and clinician- empowerment through a variety of KG learning systems, we identify four key deficiencies in state-of-the-art graph learning that simultaneously limit KG learning performance and diminish the ability of humans to interface optimally with these learning systems. These deficiencies are: 1) lack of expert knowledge integration, 2) instability to node degree extremity in the KG, 3) lack of consideration for uncertainty and relevance while learning, and 4) lack of explainability. Furthermore, we characterise state-of-the-art attempts to solve each of these problems and note that each attempt has largely been isolated from attempts to solve the other problems. Through a formalisation of these problems and a review of the literature that addresses them, we adopt the position that not only are deficiencies in these four key areas holding back human-KG empowerment, but that the divide-and-conquer approach to solving these problems as individual units rather than a whole is a significant barrier to the interface between humans and KG learning systems. We propose that it is only through integrated, holistic solutions to the limitations of KG learning systems that human and KG learning co-empowerment will be efficiently affected. We finally present our "Veni, Vidi, Vici" framework that sets a roadmap for effectively and efficiently shifting to a holistic co-empowerment model in both the KG learning and the broader machine learning domain.