Ontology-Mediated Queries for Probabilistic Databases
Authors: Stefan Borgwardt, Ismail Ceylan, Thomas Lukasiewicz
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we extend Open PDBs by Datalog ontologies, under which both upper and lower probabilities of queries become even more informative, enabling us to distinguish queries that were indistinguishable before. We show that the dichotomy between P and PP in (Open)PDBs can be lifted to the case of first-order rewritable positive programs (without negative constraints); and that the problem can become NPPP-complete, once negative constraints are allowed. We also propose an approximating semantics that circumvents the increase in complexity caused by negative constraints. |
| Researcher Affiliation | Academia | Stefan Borgwardt, Ismail Ilkan Ceylan Faculty of Computer Science Technische Universit at Dresden, Germany firstname.lastname@tu-dresden.de Thomas Lukasiewicz Department of Computer Science University of Oxford, UK thomas.lukasiewicz@cs.ox.ac.uk |
| Pseudocode | No | The paper contains formal definitions and logical expressions but no pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not state that open-source code for the described methodology is provided or link to a code repository. The link 'https://lat.inf.tu-dresden.de/research/papers.html' in the introduction is for an extended version of the paper/proofs, not code. |
| Open Datasets | No | The paper is theoretical and does not use datasets for training or evaluation. The examples provided (e.g., Example 1, 2, 3, 4, 6) are illustrative conceptual examples, not actual datasets used in experiments. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with data splits. There is no mention of training, validation, or test sets. |
| Hardware Specification | No | The paper is theoretical and does not describe any computational experiments or the hardware used to run them. |
| Software Dependencies | No | The paper is theoretical and does not describe any computational implementation. Therefore, no software dependencies or versions are mentioned. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup, hyperparameters, or training configurations. |