Identifying Useful Inference Paths in Large Commonsense Knowledge Bases by Retrograde Analysis
Authors: Abhishek Sharma, Keith Goolsbey
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results show that this approach leads to significant reduction in inference time. |
| Researcher Affiliation | Industry | Cycorp, Inc.,7718 Wood Hollow Drive, Suite 250, Austin TX 78731 abhishek@cyc.com, goolsbey@cyc.com |
| Pseudocode | Yes | Figure 2: A high-level description of our algorithm |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | No | The paper states it uses 'hundreds of queries from the Cyc KB'. While it describes the characteristics of the Cyc KB (e.g., '21.7 million assertions'), it does not provide any link, DOI, or specific citation with author/year for public access to this dataset. |
| Dataset Splits | No | The paper states: 'we divided the queries into four groups based on their time requirements'. However, it does not specify explicit training, validation, or test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | Yes | The experimental data was collected on a 4-core 3.40 GHz Intel processor with 32 GB of RAM. |
| Software Dependencies | No | The paper mentions the Cyc inference engine but does not provide specific software names with version numbers for reproducibility (e.g., libraries, frameworks, or operating systems). |
| Experiment Setup | Yes | In this paper, the user-specified threshold, , was set to zero. |