3 research outputs found
How Contentious Terms About People and Cultures are Used in Linked Open Data
Web resources in linked open data (LOD) are comprehensible to humans through
literal textual values attached to them, such as labels, notes, or comments.
Word choices in literals may not always be neutral. When outdated and
culturally stereotyping terminology is used in literals, they may appear as
offensive to users in interfaces and propagate stereotypes to algorithms
trained on them. We study how frequently and in which literals contentious
terms about people and cultures occur in LOD and whether there are attempts to
mark the usage of such terms. For our analysis, we reuse English and Dutch
terms from a knowledge graph that provides opinions of experts from the
cultural heritage domain about terms' contentiousness. We inspect occurrences
of these terms in four widely used datasets: Wikidata, The Getty Art &
Architecture Thesaurus, Princeton WordNet, and Open Dutch WordNet. Some terms
are ambiguous and contentious only in particular senses. Applying word sense
disambiguation, we generate a set of literals relevant to our analysis. We
found that outdated, derogatory, stereotyping terms frequently appear in
descriptive and labelling literals, such as preferred labels that are usually
displayed in interfaces and used for indexing. In some cases, LOD contributors
mark contentious terms with words and phrases in literals (implicit markers) or
properties linked to resources (explicit markers). However, such marking is
rare and non-consistent in all datasets. Our quantitative and qualitative
insights could be helpful in developing more systematic approaches to address
the propagation of stereotypes via LOD
Current and Future Challenges in Knowledge Representation and Reasoning
Knowledge Representation and Reasoning is a central, longstanding, and active
area of Artificial Intelligence. Over the years it has evolved significantly;
more recently it has been challenged and complemented by research in areas such
as machine learning and reasoning under uncertainty. In July 2022 a Dagstuhl
Perspectives workshop was held on Knowledge Representation and Reasoning. The
goal of the workshop was to describe the state of the art in the field,
including its relation with other areas, its shortcomings and strengths,
together with recommendations for future progress. We developed this manifesto
based on the presentations, panels, working groups, and discussions that took
place at the Dagstuhl Workshop. It is a declaration of our views on Knowledge
Representation: its origins, goals, milestones, and current foci; its relation
to other disciplines, especially to Artificial Intelligence; and on its
challenges, along with key priorities for the next decade