The document discusses Ontotext's text analytics and linked data management services, highlighting the Self-Service Semantic Suite (S4) which offers capabilities for content enrichment and smart data management with a focus on reducing time-to-market and risks associated with semantic technology adoption. Key features include on-demand access, a pay-per-use pricing model, and various text analytics services like news annotation and entity linking. It emphasizes the importance of a cost-aware cloud platform and the benefits of cloud-native architectures.

1. Text Analytics & Linked Data
Management As-a-Service
Marin Dimitrov, Alex Simov, Yavor Petkov
May 31st, 2015
Text Analytics & Linked Data Management -aaS / Wasabi’2015 #1May 2015

2. About Ontotext
• Provides products & solutions for content
enrichment and metadata management
– 70 employees, headquarters in Sofia (Bulgaria)
– Sales presence in London, NYC & Boston
• Major clients and industries
– Media & Publishing
– Health Care & Life Sciences
– Cultural Heritage & Digital Libraries
– Government
– Education
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3. • Semantic Technology adoption challenges
• The Self-Service Semantic Suite (S4)
• Lessons learned
Contents
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4. Semantic Technology Adoption
Challenges
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5. Time-to-value gap (Gartner)
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From Wasabi @
ESWC’2014
Performance,
Integration,
Penetration,
Payback & ROI

6. • Limiting factors
– Complexity & cost of existing solutions
– Limited resources to evaluate novel technologies
(startups)
– Slow procurement processes, risk aversion (enterprises)
• How can we…
– Reduce time-to-market
– Reduce adoption risks
– Optimise costs
Semantic Technology adoption
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7. The Self-Service Semantic Suite
(S4)
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8. • Capabilities for text analytics, content enrichment
and smart data management
– Text analytics for news, life sciences and social media
– RDF graph database as-a-service
– Access to large open knowledge graphs
• Available on-demand, anytime, anywhere
– Simple RESTful services
• Simple pay-per-use pricing
– No upfront commitments
What is S4?
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9. What is S4?
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10. • Enables quick prototyping
– Instantly available, no provisioning & operations
required
– Focus on building applications, don’t worry about
infrastructure
• Free tier!
• Easy to start, shorter learning curve
– Various add-ons, SDKs and demo code
• Based on enterprise semantic technology
Benefits
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11. • Text analytics services
– News annotation
– News categorisation
– Biomedical
– Twitter
• Entity linking & disambiguation
– Mappings to DBpedia & GeoNames instances
– Mappings to biomedical data sources (LinkedLifeData)
• HTML, MS Word, XML, plain text input
• Simple JSON output
Text analytics with S4
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12. News analytics example
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S4 result
Text Analytics & Linked Data Management -aaS / Wasabi’2015 May 2015

13. • Low-cost graph DBaaS available 24/7
• Ideal for small & moderate data volumes
– database options: 1M, 10M, 50M, 250M and 1B triples
• Instantly deploy new databases when needed
• Zero administration: automated operations,
maintenance & upgrades
• Users pay only for the actual database utilisation
– Number of triples stored + number of queries per month
• OpenRDF REST API
Fully managed RDF DB in the Cloud
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14. Fully managed RDF DB in the Cloud
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15. • SPARQL query endpoint to the FactForge semantic
data warehouse
– 500 million entities / 5 billion triples
• Key LOD datasets integrated
– DBpedia, Freebase/WikiData, GeoNames, WordNet
– Dublin Core, SKOS, PROTON ontologies and
vocabularies
Knowledge graphs with S4
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16. Cloud native architecture of S4
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Elasticity vs
High Availability vs
Cost Efficiency

17. Lessons Learned
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18. • You must build a “cost aware” cloud platform
• Cloud-native architectures are more efficient, but
more difficult to build
• A microservices architecture improve system
resilience & agility, but difficult to design right
• Extensive and continuous benchmarking &
monitoring
– Some problems emerge only at large scale
• Assume failures will happen & design for resilience
Lessons learned
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19. Thank you!
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