Relatedness and TBox-Driven Rule Learning in Large Knowledge Bases
Authors: Giuseppe Pirrò2975-2982
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We considered four-real world datasets, that is, WN-18RR and FB15-237 used in (Meilicke et al. 2019), and excerpts of Yago3-10 (Yago) (Gal arraga et al. 2015) and DBpedia (Shiralkar et al. 2017). For all these datasets we used the portion of the TBox schema including subclass information and domain and range of properties. Details about the datasets are available in Table 1. Following (Meilicke et al. 2019), we computed the filtered hits@1, filtered hits@10, and the mean reciprocal rank (MRR); we did not compute the filtered MRR as we are only interested in computing top-k ranks only. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Sapienza University of Rome Via Salaria 113, 00198, Rome, Italy pirro@di.uniroma1.it |
| Pseudocode | Yes | Algorithm 1: RARL (p, k, d, G, GS, MR, V +, V , rel R, α, β) ... Algorithm 2: generate Candidate Bodies(p, k, d, GS, MR) ... Algorithm 3: get Reduced ABox Graph(G, Πi, V +, V ) |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository for RARL. |
| Open Datasets | Yes | We considered four-real world datasets, that is, WN-18RR and FB15-237 used in (Meilicke et al. 2019), and excerpts of Yago3-10 (Yago) (Gal arraga et al. 2015) and DBpedia (Shiralkar et al. 2017). |
| Dataset Splits | No | Table 1: Datasets characteristics. ... Testset 3K 20K 5K |
| Hardware Specification | Yes | We implemented RARL in Java and ran experiments on a laptop with 4 cores (each with 2,7 GHz) and 16GB RAM. |
| Software Dependencies | No | We implemented RARL in Java and ran experiments on a laptop with 4 cores (each with 2,7 GHz) and 16GB RAM. |
| Experiment Setup | Yes | We considered the following default parameter values: d=3 (max. body length), top Ps=10 (top-10 related predicates), top C=80% (percentage of candidate bodies for which we want to compute confidence), α=β=0.5 (weights for the confidence score in equation (2)), n Exs=80% (number examples used as a percentage of all available positive facts for a predicate). |