Published April 9, 2021
| Version v1
Conference paper
Open
Towards Unsupervised Knowledge Extraction
Creators
- 1. Centre for Research and Technology Hellas (CERTH), University Carlos III of Madrid
- 2. Centre for Research and Technology Hellas (CERTH)
- 3. University Carlos III of Madrid
Description
Integration of symbolic and sub-symbolic approaches is rapidly emerging as an Artificial Intelligence (AI) paradigm. This paper presents a proof-of-concept approach towards an unsupervised learning method, based on Restricted Boltzmann Machines (RBMs), for extracting semantic associations among prominent entities within data. Validation of the approach is performed in two datasets that connect language and vision, namely Visual Genome and GQA. A methodology to formally structure the extracted knowledge for subsequent use through reasoning engines is also offered.
Notes
CEUR-WS.org/Vol-2846
Files
paper21.pdf
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