# Knowledge Graph Embedding for Ecotoxicological Effect Prediction

arXiv: https://arxiv.org/abs/1907.01328 · HTML: https://mlsanity.com/paper/1907.01328/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2019.

## Built on by (most cited first, 4 of 4)

- [Knowledge Graphs for the Life Sciences: Recent Developments, Challenges and Opportunities](https://mlsanity.com/paper/2309.17255/) (2023)
- [Prediction of Adverse Biological Effects of Chemicals Using Knowledge Graph Embeddings](https://mlsanity.com/paper/2112.04605/) (2021)
- [Literal-Aware Knowledge Graph Embedding for Welding Quality Monitoring: A Bosch Case](https://mlsanity.com/paper/2308.01105/) (2023)
- [Understanding Adverse Biological Effect Predictions Using Knowledge Graphs](https://mlsanity.com/paper/2210.15985/) (2022)

## Abstract (arXiv)

Exploring the effects a chemical compound has on a species takes a considerable experimental effort. Appropriate methods for estimating and suggesting new effects can dramatically reduce the work needed to be done by a laboratory. In this paper we explore the suitability of using a knowledge graph embedding approach for ecotoxicological effect prediction. A knowledge graph has been constructed from publicly available data sets, including a species taxonomy and chemical classification and similarity. The publicly available effect data is integrated to the knowledge graph using ontology alignment techniques. Our experimental results show that the knowledge graph based approach improves the selected baselines.