Predicting the top-level ontological concepts of domain entities using word embeddings, informal definitions, and deep learning

Published: 6 June 2022| Version 1 | DOI: 10.17632/kphbst6v88.1
Contributors:
Alcides Lopes,
,
,

Description

This dataset comprises the two datasets described in the article "Predicting the top-level ontological concepts of domain entities using word embeddings, informal definitions, and deep learning." Each dataset is organized into 3 columns: Column 1: the Dolce-Lite-Plus class in which the term subsumes Column 2: the term representing an OntoWordNet concept Column 3: the informal description of the respective term From this dataset, as described in the article, it is possible to search for embedding representations of the terms (column 2) in a word embeddings model. Then use a feed-forward neural network and an LSTM neural network to predict the classes of top of domain terms and their informal descriptions. The source files can be found at https://github.com/BDI-UFRGS/ESWA2021

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Institutions

Universidade Federal do Rio Grande do Sul

Categories

Ontology, Machine Learning, Neural Network, Long Short-Term Memory Network

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