Plausible Reasoning Based on Qualitative Entity Embeddings
Authors: Steven Schockaert, Shoaib Jameel
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we first discuss how an important class of background knowledge can be induced from vector space representations that have been learned from (mostly) unstructured data. Subsequently, we advocate the use of qualitative abstractions of these vector spaces, as they are easier to obtain and manipulate, among others, while still supporting various forms of plausible reasoning. [...] Experiments in [Derrac and Schockaert, 2015] have shown that classifiers using only these rankings can be as accurate as classifiers that directly operate on the vector space representation. |
| Researcher Affiliation | Academia | Steven Schockaert Cardiff University, UK Schockaert S1@Cardiff.ac.uk Shoaib Jameel Cardiff University, UK Jameel S1@Cardiff.ac.uk |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any links or explicit statements about the availability of its own source code. |
| Open Datasets | No | The paper is theoretical and does not present new empirical data or specify datasets used in experiments. |
| Dataset Splits | No | The paper does not present new experimental results, thus no training/validation/test splits are specified. |
| Hardware Specification | No | The paper is theoretical and does not present new experiments, thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper does not describe new experimental implementations and therefore does not specify software dependencies with version numbers. |
| Experiment Setup | No | The paper does not present new experimental results, thus no specific experimental setup details like hyperparameters or training configurations are provided. |