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..
Language Models as Knowledge Embeddings
Authors: Xintao Wang, Qianyu He, Jiaqing Liang, Yanghua Xiao
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that LMKE achieves state-of-the-art performance on KE benchmarks of link prediction and triple classification, especially for long-tail entities. We conduct extensive experiments on widely-used KE benchmarks |
| Researcher Affiliation | Academia | 1Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University 2Fudan-Aishu Cognitive Intelligence Joint Research Center |
| Pseudocode | No | No pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | Yes | Our codes are available at https://github.com/Neph0s/LMKE |
| Open Datasets | Yes | We experiment on four popular benchmark datasets: FB13 [Socher et al., 2013], FB15k237 [Toutanova, 2015], UMLS [Dettmers et al., 2018] and WN18RR [Dettmers et al., 2018], whose statistics are shown in Table 3. |
| Dataset Splits | Yes | We experiment on four popular benchmark datasets: FB13 [Socher et al., 2013], FB15k237 [Toutanova, 2015], UMLS [Dettmers et al., 2018] and WN18RR [Dettmers et al., 2018], whose statistics are shown in Table 3. Table 3: Statistics of the datasets. Dataset #Entity #Relation #Train #Dev #Test Avg DL. We search these hyperparameters with BERT-tiny: learning rate of PLMs among {10 4, 5 10 5, 10 5}, learning rate of other components among {10 3, 5 10 4, 10 4, 10 5}, batch size among {12, 16, 32, 64} based on best Hits@10 on the dev set. |
| Hardware Specification | No | No specific hardware details (e.g., GPU model, CPU type, memory) were found. The paper mentions models are "fine-tuned" but does not specify the computational resources used. |
| Software Dependencies | No | The paper mentions using "BERT-base [Devlin et al., 2018] and BERT-tiny [Turc et al., 2019] as the language model" and "Adam as the optimizer." However, specific version numbers for these or other software dependencies (e.g., Python, PyTorch, TensorFlow, CUDA) are not provided. |
| Experiment Setup | Yes | We search these hyperparameters with BERT-tiny: learning rate of PLMs among {10 4, 5 10 5, 10 5}, learning rate of other components among {10 3, 5 10 4, 10 4, 10 5}, batch size among {12, 16, 32, 64} based on best Hits@10 on the dev set. With BERT-base, we set the batch size as 16 for triple classification and 12 for link prediction, which considers both results of BERT-tiny and limited memory. For triple classification, we sample 1 negative triple for each positive triple. |