Mining Definitions from RDF Annotations Using Formal Concept Analysis
Authors: Mehwish Alam, Aleksey Buzmakov, Victor Codocedo, Amedeo Napoli
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To evaluate our model, four datasets were created from DBpedia, namely Cars , Videogames , Smartphones and Countries (see characteristics of the datasets in Table 5). Each dataset was created using a single SPARQL query with a unique restriction (either a fixed subject or a fixed type). A dataset consists of a set of triples whose predicate is given by the properties in Table 5. The heterogeneous aspect of data is illustrated by the fact that in two of the four datasets there are properties with numerical ranges. For each dataset we calculated the set of all implications derived from the heterogeneous pattern concept lattice. Each rule of the form X ùñ Y was ranked according to the confidence of the rule Y ùñ X (the latter is referred as the inverted rule of the former). |
| Researcher Affiliation | Academia | Mehwish Alam, Aleksey Buzmakov, Victor Codocedo, Amedeo Napoli LORIA (CNRS Inria Nancy Grand Est Université de Lorraine) BP 239, Vandoeuvre-lès-Nancy, F-54506, France {firstname.lastname@loria.fr} |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. It includes a SPARQL query in Listing 1, but no general algorithm. |
| Open Source Code | No | The paper does not provide concrete access to source code, such as a specific repository link, explicit code release statement, or mention of code in supplementary materials. |
| Open Datasets | Yes | To evaluate our model, four datasets were created from DBpedia, namely Cars , Videogames , Smartphones and Countries (see characteristics of the datasets in Table 5). Each dataset was created using a single SPARQL query with a unique restriction (either a fixed subject or a fixed type). |
| Dataset Splits | No | The paper uses datasets but does not explicitly provide specific training/validation/test dataset splits, such as percentages, sample counts, or citations to predefined splits. It describes a human evaluation process for the extracted rules. |
| Hardware Specification | No | The paper mentions execution times for the experiments in Table 5 but does not provide specific hardware details (e.g., GPU/CPU models, memory amounts, or detailed computer specifications) used for running the experiments. |
| Software Dependencies | No | The paper mentions general technologies like RDF and SPARQL, but it does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | The paper describes the methodology of using Formal Concept Analysis and pattern structures, and how implications are extracted and evaluated. However, it does not provide specific experimental setup details such as hyperparameters, training configurations, or system-level settings, as might be found in deep learning or optimization papers. The details are more about the theoretical application of the method. |