Learning First-Order Logic Embeddings via Matrix Factorization
Authors: William Yang Wang, William W. Cohen
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | 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. {yww,wcohen}@cs.cmu.edu |
| 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. |