EL Embeddings: Geometric Construction of Models for the Description Logic EL++
Authors: Maxat Kulmanov, Wang Liu-Wei, Yuan Yan, Robert Hoehndorf
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate that our method can be used for improved prediction of protein protein interactions when compared to semantic similarity measures or knowledge graph embeddings. We evaluate the predictive performance based on recall at rank 10, rank 100, mean rank and area under the ROC curve using our validation set. We report results on our testing set in Table 2 for the yeast PPI dataset and in Table 3 for the human PPI dataset. |
| Researcher Affiliation | Academia | King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia 2Department of Mathematics & Statistics, Dalhousie University, Halifax, Nova Scotia, Canada |
| Pseudocode | Yes | Algorithm 1: Algorithm used for training EL Embeddings |
| Open Source Code | Yes | 1All code is freely available on https://github.com/bio-ontology-research-group/el-embeddings/ |
| Open Datasets | Yes | We use the PPI dataset provided by the STRING database [Roth et al., 2016] to construct a knowledge graph of proteins and their interactions. We further add the associations of proteins with functions from the GO, provided by STRING, together with all classes and relations from GO. GO can be formalized in OWL 2 EL and therefore falls in the EL++formalism [Golbreich and Horrocks, 2007]. |
| Dataset Splits | Yes | We generate a training, testing, and validation split (80%/10%/10%) from interaction pairs of proteins. |
| Hardware Specification | No | The paper mentions running experiments, but does not specify any hardware details such as GPU/CPU models or specific machine configurations. |
| Software Dependencies | No | Training of embeddings and optimization is done using Python and the TensorFlow library, and we use the Adam optimizer [Kingma and Ba, 2014] for updating embeddings. The processing of ontologies in OWL format and normalization into the EL++normal forms are performed using the OWL API and the APIs provided by the j Cel reasoner which implements the EL++normalization rules [Mendez, 2012]. No specific version numbers for these software components are provided. |
| Experiment Setup | Yes | In our experiments, we perform an extensive search for optimal parameters for Trans E and our EL Embeddings, testing embeddings sizes of 50, 100, 200, and 400. We also evaluate the performance with different margin parameters γ, using 0.1, 0.01, 0, 0.01, and 0.1. The optimal set of parameters for EL Embeddings are embedding size = 50 and γ = 0.1. |