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..
Scalable Maintenance of Knowledge Discovery in an Ontology Stream
Authors: Freddy Lecue
IJCAI 2015 | Venue PDF | LLM Run Details | Input Tokens: 16,509 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,160 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 | Finally, we report experimental results on scalability and accuracy with data from Dublin City and draw some conclusions. |
| Researcher Affiliation | Industry | Freddy L ecu e IBM Research Ireland EMAIL |
| Pseudocode | Yes | Algorithm 1: [A1]Interest Update O, Sn 0 , -, S ... Algorithm 2: [A2]Rule Expansion O, Sn 0 , S, +, E, γmin ... Algorithm 3: [A3]In KD O, Sn 0 , -, S, +, E, ϱmin, γmin, Φmin |
| Open Source Code | No | The paper does not provide a statement about releasing its source code or a link to a code repository for the methodology described. |
| Open Datasets | No | Data streams (Table 2) related to road weather, travel time, incident, event, bus location in Dublin are transformed in EL++ ontology streams using mapping techniques [L ecu e et al., 2014]. |
| Dataset Splits | No | The paper describes windowed stream processing and various experimental settings but does not provide explicit train/validation/test dataset splits (e.g., percentages or counts) or reference predefined splits for reproducibility. |
| Hardware Specification | Yes | The system is tested on: 4 Intel(R) Xeon(R) X5650, 2.67GHz cores, 6GB RAM. |
| Software Dependencies | No | The paper mentions Description Logics (DL) and EL++ as technical foundations but does not specify any software libraries or dependencies with version numbers used for implementation. |
| Experiment Setup | Yes | Settings: The evaluation is achieved using a variable (i) size of stream window (i.e., snapshots) |w| {100, 500, 1000}, (ii) sliding k in {1/3, 2/3, 1} of |w|; a variation of min. thresholds of (iii) knowledge similarity in {1/3, 2/3, 1}, ε-rarity with ε E {1, ..., |w|}, (iv) support, confidence as in Table 3. |