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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
A Tractable Approach to ABox Abduction over Description Logic Ontologies
Authors: Jianfeng Du, Kewen Wang, Yi-Dong Shen
AAAI 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We develop a tractable method (in data complexity) for computing all representative explanations in a consistent ontology. Experimental results demonstrate that the method is efficient and scalable for ontologies with large ABoxes. |
| Researcher Affiliation | Academia | Jianfeng Du Guangdong University of Foreign Studies, Guangzhou 510006, China EMAIL Kewen Wang Griffith University, Brisbane, QLD 4111, Australia k.wang@griffith.edu.au Yi-Dong Shen State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing 100190, China EMAIL |
| Pseudocode | No | The paper presents theoretical lemmas and theorems, and illustrative examples of the method, but it does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions that the proposed method was 'implemented in Java' and refers to a 'Prolog-based method' with a footnote link to 'http://dataminingcenter.net/abduction/', but it does not explicitly state that the source code for the proposed methodology is open-source or provide a direct link to its repository. |
| Open Datasets | Yes | Seven benchmark ontologies with large ABoxes were used. The first two are Semintec (about financial services) and Vicodi (about European history). The remaining ontologies are LUBMn (n = 1, 5, 10, 50, 100) from the Lehigh University Benchmark (Guo, Pan, and Heflin 2005) |
| Dataset Splits | No | The paper mentions generating 'observations' or 'BCQs' for testing but does not specify any training, validation, or test splits of the datasets themselves. |
| Hardware Specification | Yes | All experiments were conducted on a laptop with Intel Dual-Core 2.20GHz CPU and 4GB RAM, running Windows 7, where the maximum Java heap size was set to 1GB. |
| Software Dependencies | No | The proposed method was implemented in Java, using the Requiem (P erez-Urbina, Motik, and Horrocks 2010) API for query rewriting and the My SQL engine to store and access ABoxes. No specific version numbers for Java, Requiem, or MySQL are provided. |
| Experiment Setup | Yes | We set a one-hour time limit to both methods for handling one observation. |