AG 1, AG 2, AG 3, INET, AG 4, AG 5, D6, SWS, RG1, MMCI   Info

Public Audience

English

60 Minutes

Saarbrücken

Commonsense knowledge (CSK) about properties of concepts and human
behaviors (e.g., elephants are big and eat plants, children love
visiting zoos, tipping is not a common practice in Japan) is crucial for
robust human-centric AI. However, this kind of knowledge is covered by a
small number of structured knowledge projects. Most prior CSK resources
are restricted in their expressiveness to subject-predicate-object (SPO)
triples with simple concepts for S and monolithic strings for P and O.
Furthermore, the plausibility of CSK can vary across cul-tures (e.g.,
"one should tip the waiter" is generally true in the US, but not in
Japan), which is overlooked in existing resources. This dissertation
aims to address these limitations by:
(1) introducing advanced commonsense knowledge models with refined
subjects, semantic facets, and culture-specific assertions;
(2) proposing methods for acquiring such knowledge from large-scale web
contents and large language models (LLMs). Our methods strive for both
high precision and wide coverage with salient assertions, which resulted
in CSK resources that outperform existing resources in various intrinsic
and extrinsic evaluations.

Petra Schaaf

+49 681 9325 5000

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