Swift Logic for Big Data and Knowledge Graphs
Authors: Luigi Bellomarini, Georg Gottlob, Andreas Pieris, Emanuel Sallinger
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We are in the process of conducting a full-scale evaluation. However, we want to give a glimpse on the results so far. In particular, for the company control scenario from our running example, Figure 2 reports promising results. We considered 7 purely randomly generated company ownership graphs (following the Erd os-Rényi model, relatively dense) from 10 to 1M companies and 5 real-world-like graphs (density and topology resembling the real-world setting), from 10 to 50K companies. For each random graph we performed two kinds of evaluations: all-rand, where we compute the control relationship between all companies and report the reasoning time in seconds; query-rand, where we make 50 separate queries for specific pairs of companies and report the average reasoning time in seconds. For each real-world-like graph, we make the same evaluations, all-real and query-real, respectively. |
| Researcher Affiliation | Academia | Luigi Bellomarini1, Georg Gottlob1,2, Andreas Pieris3, and Emanuel Sallinger1 1Department of Computer Science, University of Oxford, UK 2Institute of Information Systems, TU Wien, Austria 3School of Informatics, University of Edinburgh, UK |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper mentions '7 purely randomly generated company ownership graphs' and '5 real-world-like graphs' but does not provide any specific access information (link, DOI, repository name, or formal citation with authors/year) for these datasets. |
| Dataset Splits | No | The paper does not provide specific dataset split information (percentages, sample counts, citations to predefined splits, or detailed splitting methodology) for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, processor types, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as hyperparameter values, training configurations, or system-level settings. |