Distributing Knowledge into Simple Bases
Authors: Adrian Haret, Jean-Guy Mailly, Stefan Woltran
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We formally introduce the concept of knowledge distributability, as well as a restricted version of it where the profile is limited to a single knowledge base (simplifiability). We show that for drastic distance arbitrary knowledge can be distributed into bases restricted to mostly any kind of fragment, while simplifiability is limited to trivial cases. On the other hand, for Hamming-distance based merging the picture is more opaque. We show that for 1CNF, distributability w.r.t. H, is limited to trivial cases, while slightly more can be done with H,GMin and H,GMax. For 2CNF we show that arbitrary knowledge can be distributed and even be simplified. Finally, we discuss the Horn fragment for which the results for H, , H,GMin and H,GMax are situated in between the two former fragments. |
| Researcher Affiliation | Academia | Adrian Haret and Jean-Guy Mailly and Stefan Woltran Institute of Information Systems TU Wien, Austria {haret,jmailly,woltran}@dbai.tuwien.ac.at |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., repository link, explicit statement of code release) for the source code of the methodology described. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments involving datasets, training, or public data access. It uses abstract examples for theoretical demonstrations. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments involving dataset splits (training, validation, testing). |
| Hardware Specification | No | The paper is theoretical and does not describe any computational experiments that would require specific hardware. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe computational experiments. No specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details such as hyperparameters or training configurations. |