Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Signature-Based Abduction with Fresh Individuals and Complex Concepts for Description Logics
Authors: Patrick Koopmann
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we investigate the computational complexity of this form of abduction allowing either fresh individuals, complex concepts, or both for various description logics, and give size bounds on the hypotheses if they exist. To summarize, our contributions are the following. 1. we investigate signature-based ABox abduction for DLs ranging from EL to ALCQI where hypotheses may use fresh individuals, complex concepts or both, 2. we give tight bounds on the size of hypotheses if they exist, 3. we analyse the computational complexity of deciding whether a hypothesis exists, and 4. we analyse the complexity of deciding whether a hypothesis of bounded size exists. |
| Researcher Affiliation | Academia | Patrick Koopmann Institute for Theoretical Computer Science, Technische Universit at Dresden EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. Procedures and methods are described in narrative text. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. It is a theoretical paper focusing on complexity analysis. |
| Open Datasets | No | The paper is theoretical and focuses on computational complexity and logical properties using abstract problem instances, not real-world datasets. Therefore, no information about training data, its availability, or access is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with data, so there are no training/validation/test dataset splits to describe or make reproducible. |
| Hardware Specification | No | The paper is theoretical and focuses on computational complexity, not empirical experiments. Therefore, no hardware specifications used for running experiments are provided. |
| Software Dependencies | No | The paper is theoretical and does not describe any specific software implementations or their dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any empirical experimental setup, specific hyperparameters, or system-level training settings. |