# Knowledge Graph Embedding for Ecotoxicological Effect Prediction

## TL;DR

This paper investigates using knowledge graph embeddings to predict ecotoxicological effects of chemicals, aiming to reduce experimental efforts by leveraging integrated biological and chemical data.

## Contribution

It introduces a novel application of knowledge graph embeddings for ecotoxicological effect prediction, integrating diverse data sources with ontology alignment techniques.

## Key findings

- Knowledge graph approach outperforms baseline methods.
- Integration of species taxonomy and chemical data enhances prediction accuracy.
- Ontology alignment improves data consistency and model performance.

## Abstract

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.