A Soft Version of Predicate Invention Based on Structured Sparsity
Authors: William Yang Wang, Kathryn Mazaitis, William W. Cohen
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we evaluate the effectiveness of the proposed approach on two datasets: a new, large family relation dataset, which features the Kings and Queens of Europe, including Great Britain s royal family up to 1992; as well as the NELL subsets that include up to 100K grounded facts extracted from the Web. In particular, we focus on the task of structure learning for knowledge base completion [Wang et al., 2014a; Cropper and Muggleton, 2014], where the goal is to learn first-order logic program to reconstruct the KB, given only partially complete background database. |
| Researcher Affiliation | Academia | William Yang Wang, Kathryn Mazaitis, William W. Cohen School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 U.S.A. {yww,krivard,wcohen}@cs.cmu.edu |
| Pseudocode | No | The paper describes algorithms in text, but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper provides a link to a dataset, but does not state that the source code for their methodology is open-source or provide a link for it. |
| Open Datasets | Yes | We introduce a new dataset for research in SRL: the original dataset was created in 1992 by Denis R. Reid, including 3010 individuals and 1422 families of European royalty. [...] 5http://www.cs.cmu.edu/ yww/data/family_data.zip |
| Dataset Splits | No | We use a temporal split to separate the train and test subsets. The training set includes 21,430 facts, while the test set contains 8,899 facts. The paper mentions train and test splits but no explicit validation split. |
| Hardware Specification | No | The paper mentions that operations can be 'easily parallelized on a multi-core machine' but provides no specific hardware details (e.g., CPU/GPU models, memory). |
| Software Dependencies | No | The paper mentions various methods and tools (e.g., Pro PPR, SGD, Lasso, Group Lasso) but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | Throughout the experiments, the regularization coefficient µ for the L2 penalty was set to 0.00001, and µ for the L1 penalty was set to 0.00002. We repeat each experiment 3 times and report the averaged score and the standard deviation of the results. |