Adversarial Explanations for Knowledge Graph Embeddings
Authors: Patrick Betz, Christian Meilicke, Heiner Stuckenschmidt
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We report results on par with state-of-the-art white-box attack methods that additionally require full access to the model architecture, the learned embeddings, and the loss functions. This is a surprising result which indicates that knowledge graph embedding models can partly be explained post hoc with the help of symbolic methods. |
| Researcher Affiliation | Academia | Patrick Betz , Christian Meilicke and Heiner Stuckenschmidt University of Mannheim, Research Group Data and Web Science {patrick, christian, heiner}@informatik.uni-mannheim.de |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. It provides definitions but not algorithmic steps. |
| Open Source Code | Yes | Further details about KGE training and the code for running all experiments can be found in the supplementary material. |
| Open Datasets | Yes | We use the KGE models Compl Ex [Trouillon et al., 2016], Dist Mult [Yang et al., 2015] and Conv E [Dettmers et al., 2018] and the same datasets as [Bhardwaj et al., 2021], i.e., we use the common benchmarks WN18RR and FB15k-237. |
| Dataset Splits | Yes | Datasets are usually split into training, validation and test sets where evaluation takes place by forming queries as described above for all the triples in the test set. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | We have set the time available for Any BURL to learn the rule set to 100 seconds. |