on-demand rdf graph databases in the cloud
TRANSCRIPT
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On-Demand RDF Graph Databases in the Cloud
A webinar with Marin Dimitrov, CTO of Ontotext
Jun 11th, 2015
On-Demand RDF Graph Databases in the Cloud #1Jun 2015
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• The Self-Service Semantic Suite (S4)• RDF graph databases• On-demand RDF databases in the Cloud• Technical details• Demo• Roadmap• Q&A session
Today’s Topics
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About Ontotext
• Provides products & solutions for content enrichment and metadata management– 70 employees, headquarters in Sofia (Bulgaria)– Sales presence in London, NYC, Washington & Boston
• Major clients and industries– Media & Publishing– Health Care & Life Sciences– Cultural Heritage & Digital Libraries– Government– Education
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Some of our clients
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Ontotext’s vision for smart data management
Graph Database• Flexible RDF graph
data model• Ontology metadata
layer
Semantic Search• Semantic,
exploratory search• Metadata driven
content
Text Mining & Interlinking• People, locations,
organisations, topics• Discover implicit
relations• Reuse open knowledge
graphs
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Ontotext and AstraZeneca
Profile• Global, Bio-pharma company• $28 billion in sales in 2012• $4 billion in R&D across three continents
Goals• Efficient design of new clinical studies• Quick access to all of the data• Improved evidence based decision-making• Strengthen the knowledge feedback loop• Enable predictive science
Challenges• Over 7,000 studies and 23,000 documents
are difficult to obtain• Searches returning 1,000 – 10,000 results• Document repositories not designed for
reuse• Tedious process to arrive at evidence
based decisions
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Ontotext and LMI
Profile• Established in 1961 to enable federal
agencies • Specializes in logistics, financial,
infrastructure & information management
Goals• Unlock large collections of complex
documents• Improve analyst productivity• Create an application they can sell to US
Federal agencies
Challenges• Analysts taking hours to find, download
and search documents, using inaccurate keyword searches
• Needed a knowledge base to search quickly and guide the analysts – highly relevant searches
• Extracts knowledge from collection of documents
• Uses GraphDB to intuitively search and filter• More than 90% savings in analyst time• Accurate results
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The Self-Service Semantic Suite (S4)
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• 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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What is S4?
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Today’s webinar
focus
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• 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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Getting started in minutes
#12
1. Register a personal account at
s4.ontotext.com
2. Generate an API key
pair
3. Check out the docs, demos & code
at docs.s4.ontotext.co
m
4. Contact us with
questions!
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• 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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News analytics example
#14
S4 result
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• 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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Knowledge graph query example
#16
SPARQL query using
DBpedia data
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RDF Data Management
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• Standards compliance– Based on a mature set of W3C standards: RDF/S, OWL,
SPARQL– Portability & interoperability
• Schema-less data integration, easy querying of diverse data
• Complex & exploratory queries• Infer implicit relations in the graph• Reuse open knowledge graphs (Linked Open Data)
RDF for smart data management
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A visual view of RDF data
#19
Sub-propertiesSub-classesTransitive relations
Inference
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• High performance RDF database• Full SPARQL 1.1 support• Various reasoning profiles, including custom rules• Efficient data integration (“sameAs” optimisations)• Efficient deletion of statements & their inferences• Geo-spatial indexing & querying with SPARQL• RDF Rank, full-text search, 3rd party plugins
GraphDB by Ontotext
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On-demand RDF Databases in the Cloud
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• Ideal for customers who are… – still evaluating and testing RDF technology– In the early phase of adoption / POC
• Enterprise grade RDF database in the Cloud– No need for upfront payments for licenses & hardware– Pay only for what you use, when you use it– Instantly operational within minutes– No need for complex planning - use as many DB
instances for as long as needed– Timely upgrades to the latest version
• Self-managed and full-managed options
RDF database in the Cloud with S4
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• Available from AWS Marketplace• Variety of hardware configurations
– 2 to 8 CPU cores / 8 to 61 GB RAM– IOPS performance & encryption (EBS)
• Manage large data volumes• Pay-per-hour pricing
Self-managed RDF DB in the Cloud
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• Low-cost graph DBaaS available 24/7• Ideal for small & moderate data & query 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• Standard OpenRDF REST API
Fully managed RDF DB in the Cloud
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Fully managed RDF DB in the Cloud
#25
Database type Max triples
micro 1 million
XS 10 million
S 50 million
M 250 million
L 1 billion
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Fully managed RDF DB in the Cloud
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• …
Self vs fully
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• …
Use cases
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Technical Details
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• Cloud native architecture, running on AWS• Designed for elasticity & high availability
– More resources added whenever needed– Failed nodes replaced immediately
• GraphDB is the RDF DB engine– OpenRDF REST API
• Isolation of the multi-tenant databases– Docker containers– Private NAS volumes (EBS) for data storage
Fully managed RDF DB in the Cloud
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OpenRDF REST API
#31
resource operations comments
/repositories GET Get info on DB repos
/repositories/<REPOSITORY> GET, POST, PUT, DELETE
Create*, delete, query a repository
/repositories/<REPOSITORY>/size GET Gets the number of triples in a repository
/repositories/<REPOSITORY>/statements GET, POST, PUT, DELETE
Add, read, update, delete statements
repositories/<REPOSITORY>/rdf-graphs/<GRAPH>
GET, POST, PUT, DELETE
Same as above
/settings GET, PUT Configure the DB*
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Uploading data (OpenRDF Workbench)
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Uploading data (OpenRDF Workbench)
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Uploading data (curl)
#34
API_KEY=… KEY_SECRET=… USER=… DATABASE=… REPOSITORY=…
SERVICE_ENDPOINT="https://$API_KEY:[email protected]/$USER/$DATABASE"
curl -X POST -H "Content-Type:application/rdf+xml;charset=UTF-8" -T example.rdf $SERVICE_ENDPOINT/repositories/$REPOSITORY/statements
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Uploading data (Java / OpenRDF SDK)
#35
String dbaasURL = "<dbaas URL>"; String repositoryId="<repository ID>"; String pathToTheFile="<pathToTheFile>"; String ApiKey = "<api-key>"; String ApiPass = "<api-pass>";
//The base URI to resolve any relative URIs that are in the data against. String baseURI="http://www.example.org";
// Create a RemoteRepositoryManager RemoteRepositoryManager manager = RemoteRepositoryManager.getInstance(dbaasURL, ApiKey, ApiPass);
// Open a connection to the repositoryRepository repository = manager.getRepository(repositoryId); RepositoryConnection repositoryConnection = repository.getConnection();
// upload RDF dataFile fileToUpload=new File(pathToTheFile); repositoryConnection.add(fileToUpload, baseURI, RDFFormat.RDFXML);
// close the connectionrepositoryConnection.close();
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Querying data (OpenRDF Workbench)
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Querying data (OpenRDF Workbench)
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Querying data (curl)
#38
API_KEY=… KEY_SECRET=… USER=… DATABASE=… REPOSITORY=… SERVICE_ENDPOINT="https://$API_KEY:[email protected]/$USER/$DATABASE"
SPARQL_QUERY="…"
curl -X POST -H "Accept:application/sparql-results+xml" -d "query=$SPARQL_QUERY" $SERVICE_ENDPOINT/repositories/$REPOSITORY
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Demo
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• (Create database)• Create a repository• Upload sample data• Query the data• Explore data with a 3rd party tool
Demo scenario
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Create a database
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Micro, XS, S, M, or L
R/O access to Open Data
services or open knowledge
graphs
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Create a repository
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Inference ruleset
Cache distribution
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Uploading data (OpenRDF Workbench)
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Sample data (European country populations)
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Uploading data (OpenRDF Workbench)
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Uploading data (OpenRDF Workbench)
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Querying data (OpenRDF Workbench)
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Querying data (OpenRDF Workbench)
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Exploring data (GraphRover)
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Exploring data (GraphRover)
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Exploring data (GraphRover)
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Exploring data (GraphRover)
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Roadmap
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• Various improvements (backup & export)• Gradually introduce XS, S, M and L databases• Increased availability
– Cross-datacenter replication
• Integration with the GraphDB Workbench
Work in progress
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GraphDB Workbench
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Key Takeaways
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• S4 provides an enterprise RDF DBaaS• Free DBs up to 1M triples• Instantly available whenever needed• Easy to use: OpenRDF REST services• Zero administration: automated operations,
maintenance & upgrades• Resilient design, high availability• Check out http://s4.ontotext.com
Key Takeaways
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• Online documentation– http://docs.s4.ontotext.com/
• Sample code & demos on GitHub– https://github.com/Ontotext-AD/S4
• Helpdesk– http://support.s4.ontotext.com/
• Twitter– @Ontotext_S4
Additional S4 resources
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Thank you!
On-Demand RDF Graph Databases in the Cloud
A link to the recording will be sent out shortly
Jun 11th, 2015
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DBaaS architecture on AWS
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