Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Efficient Pruning of Large Knowledge Graphs
Authors: Stefano Faralli, Irene Finocchi, Simone Paolo Ponzetto, Paola Velardi
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply our algorithm to the tasks of pruning automatically acquired taxonomies using benchmarking data from a Sem Eval evaluation exercise, as well as the extraction of a domain-adapted taxonomy from the Wikipedia category hierarchy. The results show the superiority of our approach over state-of-art algorithms in terms of both output quality and computational efficiency. |
| Researcher Affiliation | Academia | 1 University of Rome Unitelma Sapienza 2 University of Rome Sapienza 3 University of Mannheim |
| Pseudocode | Yes | Algorithm 1: CRUMBTRAIL |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. |
| Open Datasets | Yes | We use the gold-standard taxonomies and the taxonomy learning systems from Task 17 of the Sem Eval 2015 challenge2. 2http://alt.qcri.org/semeval2015/task17/ ... We apply the four pruning algorithms to the entire Wikipedia category graph... |
| Dataset Splits | No | The paper evaluates performance against gold-standard and reference graphs but does not specify explicit training/validation/test dataset splits for reproducibility in the context of model training or hyperparameter tuning. |
| Hardware Specification | No | The paper mentions issues with CLE running out of memory 'on a multi-core machine' but does not provide specific hardware details like CPU/GPU models, processor types, or memory amounts used for the experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers, such as library or solver names and their versions. |
| Experiment Setup | No | The paper describes the algorithms and evaluation methods but does not provide specific experimental setup details such as hyperparameter values or training configurations for the algorithms themselves. |