Knowledge Refinement via Rule Selection
Authors: Phokion G. Kolaitis, Lucian Popa, Kun Qian2886-2894
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we carry out a systematic complexity-theoretic investigation of the following rule selection problem: given a set of rules specified by Horn formulas, and a pair of an input database and an output database, find a subset of the rules that minimizes the total error, that is, the number of false positive and false negative errors arising from the selected rules. [...] A natural next step is the implementation and experimental evaluation of the approximation algorithms for MIN RULESELECTFP(a,r) and MIN RULE-SELECTFP+FN(a,r) based on corresponding approximation algorithms for RED-BLUE SET COVER and POSITIVE-NEGATIVE PARTIAL SET COVER. |
| Researcher Affiliation | Collaboration | Phokion G. Kolaitis,1,2 Lucian Popa,2 Kun Qian2 1UC Santa Cruz, 2IBM Research Almaden kolaitis@ucsc.edu, lpopa@us.ibm.com, qian.kun@ibm.com |
| Pseudocode | No | The paper describes theoretical algorithms and reductions using textual descriptions and mathematical notation, but it does not include any clearly labeled pseudocode blocks or algorithm figures. |
| Open Source Code | No | The paper states, A natural next step is the implementation and experimental evaluation of the approximation algorithms for MIN RULESELECTFP(a,r) and MIN RULE-SELECTFP+FN(a,r), indicating that implementation and code release are future work. No current access is provided. |
| Open Datasets | No | The paper refers to available data and ground truth data in the abstract and problem description as part of the conceptual setup for the rule selection problem. However, it does not describe specific datasets used in experiments conducted within this paper, nor does it provide access information for such datasets. |
| Dataset Splits | No | The paper is theoretical and does not describe experimental validation; therefore, it does not specify any training/validation/test dataset splits. |
| Hardware Specification | No | The paper is a theoretical work and does not describe any experimental setup, thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is a theoretical work and does not describe any experimental implementation, thus no software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup or training configurations like hyperparameters. |