Scalable Maintenance of Knowledge Discovery in an Ontology Stream
Authors: Freddy Lecue
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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 freddy.lecue@ie.ibm.com |
| 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. |