text analytics & linked data management as-a-service

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 #1 May 2015

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

#2 Text Analytics & Linked Data Management -aaS / Wasabi’2015 May 2015

• Semantic Technology adoption challenges

• The Self-Service Semantic Suite (S4)

• Lessons learned

Contents


Semantic Technology Adoption Challenges


Time-to-value gap (Gartner)


From Wasabi @ ESWC’2014

Performance, Integration, Penetration,

Payback & ROI

• 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


The Self-Service Semantic Suite (S4)


• 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?


What is S4?


• 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


• 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


News analytics example

#12

S4 result

Text Analytics & Linked Data Management -aaS / Wasabi’2015 May 2015

• 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


Fully managed RDF DB in the Cloud


• 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


Cloud native architecture of S4


Elasticity vs High Availability vs

Cost Efficiency

Lessons Learned


• 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


Thank you!
