Empirical Analysis of Foundational Distinctions in Linked Open Data
Authors: Luigi Asprino, Valerio Basile, Paolo Ciancarini, Valentina Presutti
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We report on a set of experiments based on machine learning and crowdsourcing that show promising results. In this study, we perform a set of experiments to assess whether machines can learn to perform foundational distinctions, and if they match common sense. |
| Researcher Affiliation | Academia | 1 STLab, ISTC-CNR, Rome, Italy 2 University of Bologna, Bologna, Italy 3 University of Turin, Turin, Italy |
| Pseudocode | No | The paper describes the methods in prose and does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | The resulting datasets include a sample of annotated DBpedia entities and are available online 17. 17https://github.com/fdistinctions/ijcai18 |
| Open Datasets | Yes | The resulting datasets include a sample of annotated DBpedia entities and are available online 17. 17https://github.com/fdistinctions/ijcai18. We use DBpedia (release 2016-10) in our study as most LOD datasets link to it. |
| Dataset Splits | Yes | We used a 10-fold cross validation strategy using the reference datasets (cf. Section 4). Before training the classifiers, the datasets were adjusted in order to balance the samples of the two classes. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU models, or cloud instance specifications used for running the experiments. |
| Software Dependencies | No | We have used WEKA16 for their implementation. (Footnote 16: https://www.cs.waikato.ac.nz/ml/weka/) - The paper mentions WEKA but does not specify a version number for it or any other software libraries used. |
| Experiment Setup | No | The paper describes the features used for the machine learning models and the cross-validation strategy, but it does not provide specific hyperparameters (e.g., learning rate, batch size, epochs) or detailed system-level training settings for the algorithms. |