on-demand rdf graph databases in the cloud

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 #1 Jun 2015

• The Self-Service Semantic Suite (S4)

• RDF graph databases

• On-demand RDF databases in the Cloud

• Demo

• Roadmap

• Q&A session

Today’s topics

#2 On-Demand RDF Graph Databases in the Cloud Jun 2015

About Ontotext

• Provides products & solutions for content enrichment, metadata management & information discovery

– 70 employees, headquarters in Sofia (Bulgaria)

– Sales presence in London & New York

• Major clients and industries

– Media & Publishing

– Health Care & Life Sciences

– Cultural Heritage & Digital Libraries

– Government

– Education


Some of our clients


Our vision for Smart Data management

Graph Database • Flexible RDF graph

data model • Ontology based

metadata layer

Semantic Search • Semantic,

exploratory search • Metadata driven

content

Text Mining & Interlinking • Interlink people,

locations, organisations, topics

• Discover implicit relations • Reuse open knowledge

graphs


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


Ontotext and the Financial Times

Profile • Top 3 business media • Focused both on B2C publishing and B2B

services

Goals • Create a horizontal platform for content

enrichment and recommendation based on semantics

Challenges • Critical part of the entire workflow • Move fast from inception to production

deployment • GraphDB used not only for data, but for

content storage as well

• Horizontal platform with focus on organizations, people and relations between them

• Automatic extraction of all these concepts and relationships

• Personalised recommendations of relevant content across the entire media


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


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?


Today’s webinar focus

• 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 by Ontotext

Benefits


Getting started in minutes

#13

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.com

4. Contact us with questions!

On-Demand RDF Graph Databases in the Cloud Jun 2015

• 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

#15

S4 result


• 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


Knowledge graph query example

#17

SPARQL query using DBpedia

data


RDF Graph Data Management


• Schema-less data integration, easy querying of diverse data

• Standards compliance

– Based on a mature set of W3C standards: RDF/S, OWL, SPARQL

– Portability & interoperability across vendors

• Complex & exploratory queries

• Infer implicit relations in the graph

• Reuse open knowledge graphs (Linked Open Data)

RDF for smart data management


A visual view of RDF data

#20

Sub-properties Sub-classes

Transitive relations Inference


• High performance RDF database, 10s of billions of triples

• Full SPARQL 1.1 support

• Various reasoning profiles, including custom rules

• Efficient data integration (“sameAs” optimisations) and deletion of statements & their inferences

• Geo-spatial indexing & querying with SPARQL

• RDF Rank, full-text search, 3rd party plugins

• Connectors to Solr, ElasticSearch, NoSQL DBs

• GraphDB Workbench

GraphDB by Ontotext


“Despite all of this attention the market is dominated by Neo4J and Ontotext (GraphDB), which are graph and RDF database providers respectively. These are the longest established vendors in this space (both founded in 2000) so they have a longevity and experience that other suppliers cannot yet match. How long this will remain the case remains to be seen.”

Graph databases report by Bloor

Bloor Group whitepaper Graph Databases, April 2015 http://www.bloorresearch.com/technology/graph-databases/


On-demand RDF Databases in the Cloud


• 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 fully managed options

RDF database in the Cloud with S4


• Available from AWS Marketplace, “1-Click” purchasing

• 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

• Users take care of operations

– Backups, restores

Self-managed RDF DB in the Cloud


Self-managed RDF DB in the Cloud


• Low-cost graph DBaaS available 24/7

• Ideal for small & moderate data & query volumes

– database options: 1M, 10M, 50M, 250M & 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



#28

Database type Max triples

micro 1 million

XS 10 million

S 50 million

M 250 million

L 1 billion


FREE!

• Evaluate the technology

• Instant deployment, faster experimentation

• Faster application development

• Data services / Open Data publishing

• Reducing TCO & risk

Use cases for an RDF DBaaS


• 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



OpenRDF REST API

#32

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 DBaaS*


Uploading data (OpenRDF Workbench)


Uploading data (curl)

#35

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


Uploading data (Java / OpenRDF SDK)

#36

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 repository Repository repository = manager.getRepository(repositoryId); RepositoryConnection repositoryConnection = repository.getConnection(); // upload RDF data File fileToUpload=new File(pathToTheFile); repositoryConnection.add(fileToUpload, baseURI, RDFFormat.RDFXML); // close the connection repositoryConnection.close();


Querying data (OpenRDF Workbench)


Querying data (curl)

#39

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


Demo


• (Create a database)

• Create a repository

• Upload sample data

• Query the data

• Explore data with a 3rd party tool

Demo scenario


Create a database


Micro, XS, S, M, or L

R/O access to Open Data services or open knowledge

graphs

Create a repository


Inference ruleset

Cache distribution

Sample data (European country populations)


Exploring data (Metreeca Graph Rover)


Roadmap


• 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


GraphDB Workbench


Key Takeaways


• S4 provides an enterprise RDF DBaaS

• Free graph databases 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


• Online documentation

– http://docs.s4.ontotext.com/

• Helpdesk

– http://support.s4.ontotext.com/

• Sample code & demos on GitHub

– https://github.com/Ontotext-AD/S4

• Twitter

– @Ontotext_S4

Additional S4 resources


Thank you!

On-Demand RDF Graph Databases in the Cloud

A link to the recording will be sent out shortly

Jun 11th, 2015


DBaaS architecture on AWS


on-demand rdf graph databases in the cloud

Technology

demand rdf graph databases

demand rdf databases

search documents

content enrichment

exploratory search metadata

content storage

cto of ontotext

entire media