Abducing Relations in Continuous Spaces
Authors: Taisuke Sato, Katsumi Inoue, Chiaki Sakama
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We next test our linear algebraic approach using artificial data and real data. ... We conduct an experiment for non-recursive abduction with artificial data. ... We use FB15k, a standard knowledge graph comprised of triples ... We conduct an experiment with artificial data to test the above approach. ... We then conduct a similar experiment with real data. We choose five network graphs from the Koblenz Network Collection |
| Researcher Affiliation | Academia | Taisuke Sato1, Katsumi Inoue2, Chiaki Sakama3 1 AI research center AIST, Japan 2 National Institute of Informatics, Japan 3 Wakayama University, Japan |
| Pseudocode | No | The paper describes its methods using mathematical equations and text but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of open-source code for the methodology described. |
| Open Datasets | Yes | We use FB15k, a standard knowledge graph comprised of triples (subject, relation, object) in RDF format for 1,345 binary relations and 14,951 entities [Bordes et al., 2013]. ... We choose five network graphs from the Koblenz Network Collection [Kunegis, 2013] |
| Dataset Splits | Yes | The triples comprising a relation in FB15k are divided into training, validation and test sets [Bordes et al., 2013]. We use training sets for rule and relation discovery. |
| Hardware Specification | Yes | All experiments in this paper are carried out by GNU Octave 4.0.0 on a PC with Intel(R) Core(TM) i7-3770@3.40GHz CPU, 28GB memory. |
| Software Dependencies | Yes | All experiments in this paper are carried out by GNU Octave 4.0.0 on a PC with Intel(R) Core(TM) i7-3770@3.40GHz CPU, 28GB memory. |
| Experiment Setup | Yes | In the experiment, we set n = 10^4, create two n n random adjacency matrices R1 and R2 ... We repeat this process for λ = 1 and pe {10^-2,10^-3,10^-4,10^-5}. ... We set n = 10^4 and θ = 10^-4. Given pe, we generate an n n random matrix R1 encoding D(n,pe) and compute its transitive closure R2. |