Enhancing Existential Rules by Closed-World Variables

Authors: Giovanni Amendola, Nicola Leone, Marco Manna, Pierfrancesco Veltri

IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical In this paper, we enhance existential rules by closed-world variables to consciously reason on the properties of known (non-anonymous) and arbitrary individuals in different ways. Accordingly, we uniformly generalize the basic classes of existential rules that ensure decidability of ontology-based query answering. For them, after observing that decidability is preserved, we prove that a strict increase in expressiveness is gained, and in most cases the computational complexity is not altered. The paper includes proofs, theorems, and algorithms (Algorithm 1, 2, 3, 4) for theoretical reductions and procedures related to decidability and complexity, but no empirical evaluation or dataset analysis.
Researcher Affiliation Academia Giovanni Amendola, Nicola Leone, Marco Manna and Pierfrancesco Veltri University of Calabria, Italy amendola@mat.unical.it, leone@mat.unical.it, manna@mat.unical.it, veltri@mat.unical.it
Pseudocode Yes Algorithm 1. Reduction A1 from a hybrid triple D, Σ, q; Algorithm 2. Reduction A2 from a hybrid triple D, Σ, q; Algorithm 3. Alternating decision procedure A3; Algorithm 4. Reduction A4 from a standard triple D, Σ, q
Open Source Code No The paper does not provide any links to open-source code or explicitly state that code for the described methodology is publicly available.
Open Datasets No The paper is theoretical and does not conduct experiments on datasets, thus it does not mention public datasets or their availability.
Dataset Splits No The paper is theoretical and does not conduct experiments on datasets, thus it does not mention training/test/validation splits.
Hardware Specification No The paper is theoretical and does not conduct experiments, thus it does not provide any hardware specifications.
Software Dependencies No The paper is theoretical and describes algorithms and properties without mentioning specific software or library dependencies with version numbers.
Experiment Setup No The paper is theoretical and does not conduct empirical experiments, thus it does not provide details about an experimental setup or hyperparameters.