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..
Context-aware Path Ranking for Knowledge Base Completion
Authors: Sahisnu Mazumder, Bing Liu
IJCAI 2017 | Venue PDF | 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 EMAIL, EMAIL |
| 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]. |