WaSABi’2013

Adaptive Semantic Publishing

Georgi Georgiev, Borislav Popov, Petya Osenova & Marin Dimitrov

Contents

• Adaptive Semantic Publishing

• Software and Information Architecture

• Semantic Annotation Process

• Personalised Content Delivery

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Adaptive Semantic Publishing

• High-quality content

– Recommened related materials

– Improved reuse

• Dynamic products

– Semantic enrichment

– Metadata-driven web content

– Dynamic re-purposing

• Personalised content delivery

– Adaptive content streams

– Highly relevant information

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Success Stories

• BBC Sports (UK)

– 2010 World Cup & 2012 Olympics

• Press Association (UK)

– Semantic enrichment of assets

• EuroMoney (UK)

– Finance & economics

• NDP (NL)

– Semantic publishing, 3rd party apps

• Publicis (DE)

– Content recommendation

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BBC’s Dynamic Semantic Publishing

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Adaptive Semantic Publishing for NDP

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XML Content Store

Generalised Semantic Publishing Architecture

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Information Architecture

• Subset of LOD

– Dbpedia & Freebase – persons & organisations

– Geonames – important locations

– Tailored for the specific use case needs

– 2.5M+ entities

• LOD ontologies mapped through PROTON

– Upper-level ontology; 250 classes & 100 properties

– Unified ontology interface

• Domain specific ontologies

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Annotation Guidelines & Corpus Creation

• Functional requirements

– Informal discussion with customer’s domain experts

– Sample documents <> sample (manual) annotations

– Agreement on annotation types

• Annotation guidelines

– Rules, examples, ambiguous cases, quality checks for annotators

• Manual annotation

– Based on the annotation guidelines

– Multiple annotators + super-annotator

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Iterative Improvement of the Semantic Annotation

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Curation

• Instance disambiguation

– “Washington” >> Politician, US State, US City, …

• Tagging

– Verify automated annotations

– Fix mis-tagged entities if necesary

• Topic / keyword correction

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Curation

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Sample Evaluation Corpus

• 250 documents, 4.5K average document size

• 6,000+ manually annotated entities

• 2.35 candidates for disambiguation per entity

– … but up to 100 candidates in some cases!

• F1 (Org) = 0.82; F1 (Per) = 0.90; F1 (Loc) = 0.92

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Personalised Content Delivery

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Personalised Content Delivery

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Personalised Content Delivery

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Q & A

Thank you!

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