Valeria de Paiva discusses the need for knowledge resources like ontologies for the Portuguese language. While Portuguese is the 6th most spoken language, existing lexical and semantic resources are insufficient for natural language understanding in Portuguese. The OpenWordnet-PT project has created a Portuguese wordnet that is used in applications like Google Translate but requires further improvement. Other relevant resources for Portuguese include the UD corpus and SICK-BR corpus. Machine learning presents both opportunities if knowledge can be incorporated explainably, as well as challenges if it is seen as a "black box" solution.

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Ontologies and semantics for
Portuguese
Valeria de Paiva
Visiting Prof DI PUC-RJ &
Topos Institute, Berkeley
Thanks,
Giancarlo!

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Predicting the future…
http://openwordnet-pt.org
 Alan Kay: The best way to predict the future is to invent
it!
 What do I want: Build knowledge resources e.g. a
Knowledge Graph (KG) for Brazilian Culture
 Why? Portuguese is the 6th most spoken language in the
planet. BUT
 We have no lexical/semantic/KR resources enough to deal
with Understanding Portuguese!
A proxima decada de pesquisa em ontologias: desafios e
oportunidades…

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A grand plan (2018 Lux AI Summit)
keynote

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Which Resources?
 OpenWordnet-PT (From 2012)
 Used by Google Translate, BabelNet, OpenWordNet
 Code in https://github.com/own-pt/openWordnet-PT, user interface
http://openwordnet-pt.org
 Sweet spot between coverage and accuracy. Need to make it better.
NomLex-BR only started
 UD (Universal Dependencies) for Portuguese 2017 Bosque
https://github.com/UniversalDependencies/UD_Portuguese-Bosque
 SICK-BR corpus to test inference
 New projects with PUC-Rio
Needed a Portuguese wordnet, but none was
openly available. We built it.

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Challenges & Opportunities
 ML: Biggest opportunity, if we can incorporate it and make it
explainable and generalizable
 ML: Biggest challenge as many believe ‘fairy dust’ will do
the work
 Traditional trade-off: expressivity of logic vs difficulty of
computation
 Representation and implementation are different. Different
use cases support different computational time limits. (Pease)

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Thanks!

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The Future of Ontology
In the future people will use one or a small number of large ontologies defined in
expressive logical languages. A dictionary of one page isn't very useful. To define shared
terms for software integration or discourse, a large corpus is needed. A dictionary that
just allows for stating relationships among terms, but not their definitions also isn't very
useful. We'll use languages that are formal and computable and also don't limit us from
the things we'd want to say in a human language definition. Much current work in
ontology uses languages that are limited in the logical expressiveness of what they can
say, in order to conform to a particular use case in computation, with technology available
at the time the language was created. But the power of logical inference systems has
advanced with every passing decade, and the choices made 20 years ago are not so
relevant today. Definition (or representation) and implementation are different. Different
use cases support different computational time limits. Choosing very limited languages
ensures that use cases that provide more time for execution will not have available the
definitions and constraints needed to capture what concepts mean in the world. In the
future of ontology, logical inference will advance considerably from where it is today, and
ontologies written to take advantage of the logical inference systems of the future will be
the ones that get used.
Adam Pease, Nov 2020