Modeling Heterogeneous Hierarchies with Relation-specific Hyperbolic Cones
Authors: Yushi Bai, Zhitao Ying, Hongyu Ren, Jure Leskovec
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on standard knowledge graph benchmarks show that Con E obtains state-of-the-art performance on hierarchical reasoning tasks as well as knowledge graph completion task on hierarchical graphs. (Abstract) and Given a KG containing many hierarchical and non-hierarchical relations, our experiments evaluate: (A) Performance of Con E on hierarchical reasoning task of predicting if entity h1 is an ancestor of entity h2. (B) Performance of Con E on generic KG completion tasks. (Section 4) |
| Researcher Affiliation | Academia | Yushi Bai bys18@mails.tsinghua.edu.cn Tsinghua University; Rex Ying rexying@stanford.edu Stanford University; Hongyu Ren hyren@cs.stanford.edu Stanford University; Jure Leskovec jure@cs.stanford.edu Stanford University |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code of our paper is available at http://snap.stanford.edu/cone. (Section 4) |
| Open Datasets | Yes | We use four knowledge graph benchmarks (Table 1): Word Net lexical knowledge graph (WN18RR [5, 17]), drug knowledge graph (DDB14 [18]), and a KG capturing common knowledge (FB15k-237 [31]). Furthermore, we also curated a new biomedical knowledge graph GO21, which models genes and the hierarchy of biological processes they participate in. (Section 4) and Table 1 includes a 'training' column for dataset statistics. |
| Dataset Splits | Yes | Table 1: Datasets statistics. #training #validation #test (Table 1 clearly provides a 'validation' column with sample counts for each dataset). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'Adam [32] as the optimizer' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | During training, we use Adam [32] as the optimizer and search hyperparameters including batch size, embedding dimension, learning rate, angle loss weight and dimension of subspace for each hierarchical relation. (Section 4) |