Context-aware Path Ranking for Knowledge Base Completion
Authors: Sahisnu Mazumder, Bing Liu
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on three large KBs show that the path features (fewer in number) discovered by C-PR not only improve predictive performance but also are more interpretable than existing baselines. |
| Researcher Affiliation | Academia | Sahisnu Mazumder, Bing Liu Department of Computer Science, University of Illinois at Chicago, USA sahisnumazumder@gmail.com, liub@cs.uic.edu |
| Pseudocode | No | The paper describes the algorithm steps in text and provides an example trace in a table, but it does not include a formal pseudocode block or algorithm listing. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for their method is openly available. |
| Open Datasets | Yes | We used three datasets for our experiments: two standard ones, viz (1) FB15k 4, (2) Word Net 4 and (3) one dataset created by us from Concept Net 5 triples. FBK15k is a relatively dense subgraph of Freebase. The Word Net dataset is comparatively small and Concept Net dataset is the largest one among the three. |
| Dataset Splits | Yes | For each of the 25 relations, we randomly shuffle the list of distinct triples, choose 1000 triples and split them into 80% training, 20% test. |
| Hardware Specification | No | We performed experiments on Amazon EC2 linux server having Intel Xeon processor, 264GB RAM and 64 CPU cores in order to support the baselines that use exhaustive path search. |
| Software Dependencies | No | We use sklearn python ML library for training the LR model with L2-regularization. |
| Experiment Setup | Yes | We use sklearn python ML library for training the LR model with L2-regularization. Other parameters of the LR model are: tolerance set to 0.0001, maximum iterations for convergence to 200, and class weight set as balanced . We choose LR because LR not only has been used in existing PR-based approaches [Lao and Cohen, 2010; Gardner et al., 2014; Gardner and Mitchell, 2015], but also has been shown to give better performances compared to SVM [Wang et al., 2016]. |