Sorted Neighborhood for the Semantic Web
Authors: Mayank Kejriwal
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Real-world evaluations demonstrate the workflow s utility against a popular baseline. In Figure 3, experimental results from the Video Game benchmark are provided. This benchmark contains over 230,000 triples, and is real-world. The proposed method is shown to outperform an established clustering baseline, Canopies (Mc Callum, Nigam, and Ungar 2000). Reduction Ratio and Pairs Completeness are standard metrics that re-spectively measure efficiency and effectiveness of blocking (Christen 2012). In the expanded work, we also show results from two other real-world test cases. |
| Researcher Affiliation | Academia | Mayank Kejriwal, Daniel P. Miranker University of Texas at Austin {kejriwal,miranker}@cs.utexas.edu |
| Pseudocode | No | The paper describes the Sorted Neighborhood workflow conceptually but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions "The full technical details are in an expanded work4. https://sites.google.com/a/utexas.edu/mayank-kejriwal/projects/sorted-neighborhood" which points to a personal project page, not a direct source-code repository (e.g., GitHub, GitLab) for the methodology. |
| Open Datasets | No | The paper mentions the "Video Game benchmark" and "Linked Open Data (LOD) linkeddata.org" but does not provide a specific link, DOI, or formal citation for directly accessing these datasets for research reproduction. |
| Dataset Splits | No | The paper does not provide specific details about dataset splits (e.g., percentages, counts) for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x). |
| Experiment Setup | No | The paper describes the workflow and results but does not provide specific experimental setup details such as hyperparameters, training configurations, or system-level settings. |