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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Compiling Model Representations for Querying Large ABoxes in Expressive DLs
Authors: Labinot Bajraktari, Magdalena Ortiz, Mantas Simkus
IJCAI 2018 | Venue PDF | LLM Run Details | Input Tokens: 20,361 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 7,336 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To illustrate the potential of our approach for OMQ answering, we implement a rewriting into an extension of Datalog for OMQs comprising reachability queries, and provide some promising evaluation results. |
| Researcher Affiliation | Academia | Labinot Bajraktari, Magdalena Ortiz and Mantas ˇSimkus Faculty of Informatics, TU Wien, Austria EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Retrieve types that reach C through r |
| Open Source Code | Yes | We have implemented our approach in a prototype reasoner Mod4Q3 for the DL ALCH. 3http://www.kr.tuwien.ac.at/research/systems/Mod4Q/ |
| Open Datasets | Yes | We considered some large realworld data sets, including NPD, a petroleum ontology, IMDb, a film ontology, and My ITS, a transport ontology. The first two were obtained from [Glimm et al., 2017] while the latter from [Bajraktari et al., 2017]. ... We took the 87 ontologies from the Oxford ontology library4 that have both ABox assertions... 4http://www.cs.ox.ac.uk/isg/ontologies/ |
| Dataset Splits | No | The paper mentions evaluating on large real-world datasets but does not specify any training, validation, or test splits for data partitioning. |
| Hardware Specification | Yes | Experiments were run on a PC with an i7 2.4 GHz CPU running 64bit Linux Mint 17, with a JAVA heap space of 12GB. |
| Software Dependencies | No | The paper mentions 'Java', 'Postgre SQL database', and 'Clingo [Gebser et al., 2011]', but does not provide specific version numbers for Java or PostgreSQL, nor a precise version for Clingo itself. |
| Experiment Setup | No | The paper describes the general experimental design, including types of queries and data sources, but does not provide specific hyperparameter values, training configurations, or detailed system-level settings for the experiments. |