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AI & Digital Culture
Vassilis Tzouvaras , NTUA | IASI Romania 2019
Metadata Enrichment
• Quality of Metadata
• Data models, EDM, XML, RDF, LoD, URIs, SKOS,
WikiData, Geonames
• Manual proce...
Machine Learning
CC BY-SA
Machine learning algorithms build a
mathematical model of sample data, known as
"training data",...
Deep Neural Networks
CC BY-SA
Supervised Learning
CC BY-SA
Supervised learning algorithms
build a mathematical model of a set
of data that contains both...
Datasets???
CC BY-SA
Machine learning requires high volumes of data for training, validation, and
testing.
it’s important ...
Crowd & Machine
Intelligence
CC BY-SA
Machine intelligence and human intelligence can cooperate and improve each
other in ...
CrowdHeritage
Title here
CC BY-SA
Color tagging
Find dominant
colors in fashion
artifacts!
8
Title here
CC BY-SA
9
Geo tagging
Drop pins to
countries or
locations in the
map that
represents the
picture
9
Greek Aggregator
Title here
CC BY-SA
10
EKT has developed an aggregation infrastructure
that consists of various platforms...
semantic enrichment
Title here
CC BY-SA
11
● Semantics.gr: a platform developed by EKT where institutions can create,
esta...
Title here
CC BY-SA
12
21 October 2016
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The Europeana meeting under the Romanian Presidency, Exposing Online the European Cultural Heritage: The impact of Cultural Heritage on the Digital Transformation of The Society, Iasi, Romania - 17 & 18 April 2019

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AI & Digital Culture by Vassilis Tzouvaras

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The Europeana meeting under the Romanian Presidency, Exposing Online the European Cultural Heritage: The impact of Cultural Heritage on the Digital Transformation of The Society, Iasi, Romania - 17 & 18 April 2019

  1. 1. AI & Digital Culture Vassilis Tzouvaras , NTUA | IASI Romania 2019
  2. 2. Metadata Enrichment • Quality of Metadata • Data models, EDM, XML, RDF, LoD, URIs, SKOS, WikiData, Geonames • Manual process • Automatic enrichment, machine learning • Crowdsourcing, Human in the loop, Why is needed? CC BY-SA Letter carrier from "Letters from the Land of the Rising Sun”.1886 - 1892, British Library United Kingdom, Public Domain
  3. 3. Machine Learning CC BY-SA Machine learning algorithms build a mathematical model of sample data, known as "training data", in order to make predictions or decisions Technologies: NLP, object detection, machine translation, historical event detection, visual similarity,
  4. 4. Deep Neural Networks CC BY-SA
  5. 5. Supervised Learning CC BY-SA Supervised learning algorithms build a mathematical model of a set of data that contains both the inputs and the desired outputs. The data is known as training data, and consists of a set of training examples.
  6. 6. Datasets??? CC BY-SA Machine learning requires high volumes of data for training, validation, and testing. it’s important to have the right data, structured in the right format, covering all the variation of your solution. So how do you get the large volume of structured data you need? Human- annotated data is the key to successful machine learning.
  7. 7. Crowd & Machine Intelligence CC BY-SA Machine intelligence and human intelligence can cooperate and improve each other in a mutually rewarding way. ● Exploit the user obtained annotations for training/improving machine learning algorithms ● Use machine learning methods to validate user acquired labels ● Crowdsourcing campaign with specifically selected content which will improve performance of automated machine learning system
  8. 8. CrowdHeritage Title here CC BY-SA Color tagging Find dominant colors in fashion artifacts! 8
  9. 9. Title here CC BY-SA 9 Geo tagging Drop pins to countries or locations in the map that represents the picture 9
  10. 10. Greek Aggregator Title here CC BY-SA 10 EKT has developed an aggregation infrastructure that consists of various platforms and systems that cover the lifecycle of the digital content aggregation, from harvesting and validation, to cleansing, transformation, semantic enrichment and secured preservation.
  11. 11. semantic enrichment Title here CC BY-SA 11 ● Semantics.gr: a platform developed by EKT where institutions can create, establish and publish LOD vocabularies, taxonomies, thesauri and authority files (SKOS but also other schemas as well) ○ Vocabulary of 139 item types ○ Vocabulary of 94 Greek historical periods ● Enrichment tool of Semantics.gr: a tool for setting enrichment mapping rules from distinct metadata values to vocabulary terms ○ Mappings per collection ○ Semi-automatic (automatic suggestions and curation) ○ Mappings can be based on one or more metadata fields ● Time normalization tool of the aggregator platform: a tool for setting parametric normalization patterns of time values
  12. 12. Title here CC BY-SA 12
  13. 13. 21 October 2016

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