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12 shades of RDF: Impact of Syntaxes on Data Extraction with Language
Published on Jun 17, 202441 Views
The fine-tuning of generative pre-trained language models (PLMs) on a new task can be impacted by the choice made for representing the inputs and outputs. This article focuses on the linearization pro
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Chapter list
12 shades of RDF Impact of Syntaxes on Data Extraction with Language Models00:00
I.RDF-Pattern Based Extraction00:28
RDF-Pattern Based Extraction00:34
12 shades of RDF RQ - 100:55
12 shades of RDF RQ - 201:10
12 shades of RDF RQ - 301:31
II. Graph linearization01:44
Encoder-decoder FT models for relation extraction via linearization01:47
Linearization proposed by the literature02:00
RDF syntaxes proposed by the W3C...02:19
6 or 12 shades of RDF ?02:28
Turtle Light02:38
Variations: Factorisation of triples02:52
Variations: One-line Turtle light ?03:16
III. Experimental Framework03:27
Ground-truth construction03:32
Shape definition03:50
Models : BART-base & T5-base FT - 104:13
Models : BART-base & T5-base FT - 204:25
Model tokenizers04:36
Evaluation - 105:10
Evaluation - 206:15
Experimental overview07:12
IV. And the Best syntax is ?07:23
IV. The best syntaxes - 107:32
IV. The best syntaxes - 207:36
IV. The best syntaxes - 307:56
IV. The best syntaxes - 408:24
IV. The best syntaxes - 508:35
IV. The best syntaxes - 609:51
IV. The best syntaxes - 710:22
V. Conclusions10:32
How does the choice of a syntax impact the generation of RDF triples using datatype properties?10:39
What did you wish you knew before starting this work?11:18
What is a key challenge going forward that your work gives rise to?11:52
Contact12:21
Let’s discuss !12:26