1. Diversity and the Semantic Web
Elena Simperl and Denny Vrandečić
Institut AIFB – Angewandte Informatik und Formale Beschreibungsverfahren
KIT – University of the State of Baden-Württemberg and
National Large-scale Research Center of the Helmholtz Association www.kit.edu

2. The loss of serendipity
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3. The myth of numerical methods
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4. Fragmentation, ghettoization, polarization
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5. Policy and decision-making
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6. Challenges
Understand the emergence and impact of biases in
(collaborative) content prosumption.
Different parties are likely to have different points of view and
should be able to express and talk about them.
There is no way to ‚agree to disagree‘ on the Semantic Web!
Core activities and underlying techniques are (implicitly) biased.
Modeling, choice of ontology to be used, definition of mappings,
lifting to RDF, selection of data sets, aggregation,
visualization…
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7. Challenges (2)
Identify, represent and predict biases.
Models to represent provenance, trust, quality…for different types
of content, and as part of the activities listed above.
Models to predict the socio-technical mechanisms leading to
biases.
Opinion mining and sentiment analysis.
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8. Challenges (3)
Design diversity-minded data and information management
algorithms
…taking into account both producer and consumer biases.
Make biases explicit.
Augment ranking, filtering, recommendation, visualization…
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9. Institut AIFB – Angewandte Informatik und Formale Beschreibungsverfahren
KIT – University of the State of Baden-Württemberg and
National Large-scale Research Center of the Helmholtz Association www.kit.edu