Pith. sign in

REVIEW 1 cited by

PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2007.14175 v2 pith:BFCH7N2B submitted 2020-07-28 cs.LG cs.AIstat.ML

PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings

Mehdi Ali , Max Berrendorf , Charles Tapley Hoyt , Laurent Vermue , Sahand Sharifzadeh , Volker Tresp , Jens Lehmann This is my paper
classification cs.LG cs.AIstat.ML
keywords graphknowledgepykeentrainingbeenembeddingsevaluatingkges
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original abstract

Recently, knowledge graph embeddings (KGEs) received significant attention, and several software libraries have been developed for training and evaluating KGEs. While each of them addresses specific needs, we re-designed and re-implemented PyKEEN, one of the first KGE libraries, in a community effort. PyKEEN 1.0 enables users to compose knowledge graph embedding models (KGEMs) based on a wide range of interaction models, training approaches, loss functions, and permits the explicit modeling of inverse relations. Besides, an automatic memory optimization has been realized in order to exploit the provided hardware optimally, and through the integration of Optuna extensive hyper-parameter optimization (HPO) functionalities are provided.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. ChronoMedKG: A Temporally-Grounded Biomedical Knowledge Graph and Benchmark for Clinical Reasoning

    cs.CL 2026-05 unverdicted novelty 6.0

    ChronoMedKG builds a temporal biomedical KG with 460k evidence-linked triples across 13k diseases using LLM consensus and introduces the ChronoTQA benchmark showing RAG gains on time-sensitive questions.