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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Learning First-Order Logic Embeddings via Matrix Factorization
Authors: William Yang Wang, William W. Cohen
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In experiments, we demonstrate the effectiveness of reasoning with first-order logic embeddings by comparing with several state-of-the-art baselines on two datasets in the task of knowledge base completion. |
| Researcher Affiliation | Academia | William Yang Wang and William W. Cohen School of Computer Science Carnegie Mellon University, Pittsburgh, PA 15213, U.S.A. EMAIL |
| Pseudocode | Yes | Algorithm 1 A Matrix Factorization Based Algorithm for Learning First-Order Logic Embeddings |
| Open Source Code | No | The paper does not provide an explicit statement about the release of its source code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | The statistics of the datasets are shown in Table 2. ... Table 2: Statistics of the two publicly available datasets used in the knowledge base completion experiments. Rel.: relations. En.: entities. Word Net 18 40,943 141,442 5,000 5,000 FB15K 1,345 14,951 483,142 50,000 59,071 |
| Dataset Splits | Yes | Table 2: Statistics of the two publicly available datasets used in the knowledge base completion experiments. ... Word Net 18 40,943 141,442 5,000 5,000 FB15K 1,345 14,951 483,142 50,000 59,071 |
| Hardware Specification | No | The paper does not specify the hardware (e.g., CPU, GPU models, memory) used for the experiments. |
| Software Dependencies | No | The paper mentions using 'stochastic gradient descent (SGD)' and 'fast parallel stochastic gradient descent (FPSG) [Chin et al., 2015]' as optimization approaches, but does not specify particular software libraries or their version numbers. |
| Experiment Setup | Yes | Pro PPR s reset parameter is set to 0.1, and the approximation error parameter is set to 1 10 3. ... the latent dimension of first-order logic embeddings is set to 8. ... we vary the latent dimension k for learning first-order logic embeddings. ... We investigate the effects of choosing various loss functions for learning first-order logic embeddings. |