Open-World Knowledge Graph Completion
Authors: Baoxu Shi, Tim Weninger
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on large data sets, both old and new, show that Con Mask performs well in the open-world KGC task and even outperforms existing KGC models on the standard closed-world KGC task. |
| Researcher Affiliation | Academia | Baoxu Shi, Tim Weninger University of Notre Dame {bshi,tweninge}@nd.edu |
| Pseudocode | No | The paper describes the model components and their interactions but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | Con Mask is implemented in Tensor Flow. The source code is available at https://github.com/bxshi/Con Mask. |
| Open Datasets | Yes | The Freebase 15K (FB15k) data set is widely used in KGC... we introduce two new data sets DBPedia50k and DBPedia500k for both open-world and closed-world KGC tasks. Statistics of all data sets are shown in Tab. 2. Also, 'we also released two new DBPedia data sets for KGC research and development.' |
| Dataset Splits | Yes | Statistics of all data sets are shown in Tab. 2. The methodology used to evaluate the open-world and closed-world KGC tasks is similar to the related work. Specifically, we randomly selected 90% of the entities in the KG and induced a KG subgraph using the selected entities, and from this reduced KG, we further removed 10% of the relationships, i.e., graph-edges, to create KGtrain. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware used for running its experiments. |
| Software Dependencies | No | The paper states 'Con Mask is implemented in Tensor Flow' but does not provide specific version numbers for TensorFlow or other software dependencies. |
| Experiment Setup | Yes | Training parameters were set empirically but without finetuning. We set the word embedding size k = 200, maximum entity content and name length kc = kn = 512. The word embeddings are from the publicly available pre-trained 200-dimensional Glo Ve embeddings (Pennington, Socher, and Manning 2014). The content masking window size km = 6, number of FCN layers kfcn = 3 where each layer has 2 convolutional layers and a BN layer with a moving average decay of 0.9 followed by a dropout with a keep probability p = 0.5. Max-pooling in each FCN layer has a pool size and stride size of 2. The mini-batch size used by Con Mask is kb = 200. We use Adam as the optimizer with a learning rate of 10-2. The target sampling set sizes for |E+| and |E | are 1 and 4 respectively. All open-world KGC models were run for at most 200 epochs. |