Towards Unsupervised Knowledge Extraction

descriptionPublicationkeyboard_double_arrow_right Conference object , Article 01 Jan 2020 English Publisher:ZenodoJournal:AAAI Spring Symposium: Combining Machine Learning with Knowledge EngineeringFunded by:EC | ANITA

Authors: Tsatsou, Dorothea; Karageorgos, Konstantinos; Dimou, Anastasios; Carbo, Javier; Molina, Jose M.; Daras, Petros;

doi: 10.5281/zenodo.4686855 , 10.5281/zenodo.4686854

handle: 10016/39768

Towards Unsupervised Knowledge Extraction

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Abstract

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.

CEUR-WS.org/Vol-2846

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Keywords

Informática, neuro-symbolic integration, knowledge representation, neurosymbolic integration, knowledge extraction, formal knowledge representation, unsupervised learning, spectral analysis, symbolic AI, sub-symbolic AI, neural-symbolic integration

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