Knowledge Representation Learning with Entities, Attributes and Relations
Authors: Yankai Lin, Zhiyuan Liu, Maosong Sun
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experiment results show that, by special modeling of attribute, KR-EAR can significantly outperform state-of-the-art KR models in prediction of entities, attributes and relations. The source code of this paper can be obtained from https://github.com/thunlp/KR-EAR. |
| Researcher Affiliation | Academia | 1 Department of Computer Science and Technology, State Key Lab on Intelligent Technology and Systems, National Lab for Information Science and Technology, Tsinghua University, Beijing, China 2 Jiangsu Collaborative Innovation Center for Language Competence, Jiangsu, China |
| Pseudocode | No | The paper describes the model components and their functions but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source code of this paper can be obtained from https://github.com/thunlp/KR-EAR. |
| Open Datasets | Yes | We evaluate our model on a typical large-scale KG Freebase. Freebase [Bollacker et al., 2008] is a large-scale and growing collaborative KG consisting of data composed mainly by its community members, which provides general facts of the real world. Finally, we build a dataset named as FB24k, and we randomly separate datas into training and testing sets. |
| Dataset Splits | Yes | We tune our models using five-fold validation on the training set. |
| Hardware Specification | No | The paper mentions 'The running time of per iteration is 14s for Trans E and 297s for Trans R in single thread' but provides no specific details about the CPU, GPU, or other hardware used for the experiments. |
| Software Dependencies | No | The paper does not mention any specific software dependencies or their version numbers (e.g., Python version, library versions, frameworks). |
| Experiment Setup | Yes | The best configurations are λ = 0.001, γ = 0.1, k = 100, b1 = 7, b2 = 2, c1 = 10, c2 = 1 and taking L1 as dissimilarity metric. For training, we set the iteration number over all the training triples as 1000. |