Alitora Innovation Networks

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Alitora Systems presents Innovation Networks for Life Sciences at Semantic Technology Conference 2009.

Technologie

[object Object],[object Object],[object Object],[object Object],Building Innovation Networks in the Life Sciences http://en.wikipedia.org/wiki/Collaborative_Innovation_Networks

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How can we: Surface & Distribute Insights from Unstructured Data? Or: How do we deliver to you the interesting bits of knowledge out of a lot of text?

Problem: Insights from Data Silos Case Study: Eli Lilly / Life Science

Solution: Natural Language Processing? Transform a pile of Documents into A pile of Facts . We still have a pile.

Solution: Natural Language Processing + Linked Relationships via Ontologies? What in the network is important ?

Solution: Natural Language Processing + Linked Relationships + Relevancy? Relevant to whom?

Solution: Natural Language Processing + Linked Relationships + Relevancy + Collaboration. Ah Ha! Relevant information can find me via my context. My colleagues can help.

Then we can Surface & Distribute Insights Deploy solution as a service so Insights can be embedded anywhere.

People Databases, Ontologies News, PR, Documents, Reports Innovation Network

Innovation Network - DEMO Web Application Application Plug-In

Alitora Systems Architecture ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Collaboration, Entitlement ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Innovation Network kHarmony™ Data Representation: Each Object has Properties “ chunks” of RDF Unique URI “ Core” RDFs Model

Innovation Network kHarmony™ HyperEdges: Statements about Statements Track Provenance Difficult in RDF alone (everything “reified”)

kHarmony™ Relevancy ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Confidential

Semantic Knowledge Statements Domain Ontology + Instance Statements

Semantic Knowledge Statements Direct or Inferred

Text Mine Unstructured Data: Semantic Parse Graph Representation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Entity and Relationship Normalization ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Confidential Copyright Alitora Systems, Inc. 2009

Innovation Network ,[object Object],[object Object],[object Object],[object Object],[object Object],“ Semantics-as-a-Service” “ Platform-as-a-Service”

Innovation Network - DEMO ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Innovation Network - DEMO Web Application Application Plug-In … including OpenCalais

Contact Information ,[object Object],[object Object],[object Object],[object Object],[object Object]

Alitora Foundry Toolkit ,[object Object]

Alitora Foundry Toolkit ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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Linklaters is one of the world’s leading global law firms. The firm has a wealth of high value information held within our systems however due to the nature of these systems it is not always easy to leverage this value. Our goal was to improve decision making across the firm by transforming access to and ability to query data. To do this we wanted a solution that would combine our information, was easy to extend in an iterative fashion and would leverage our existing investment in business intelligence. To achieve this we chose to create a graph based warehouse using Linked Data. Data from our SAP Business Warehouse was combined with flat file and XML feeds from our systems of record and transformed into RDF via ETL services that loaded it into a triple store. To provide simple integration with our existing environment a SPARQL to OData service was deployed creating an OData compliant endpoint. Finally a model driven, mobile friendly, user interface was created allowing users to query, review results and explore the underlying graph. This talk will describe the approach we took and the lessons learnt.

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Watch Alberto's session from Fast Data Strategy on-demand here: https://buff.ly/2wByS41 Gartner’s recently published report “Data Catalogs Are the New Black in Data Management Analytics” emphasizes the importance of data catalogs. Watch this session to learn more about: • The vision behind the Denodo Data Catalog • How to maximize information value with the Denodo Data Catalog • Why it is essential to combine data delivery with a data catalog

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GDPR = General Data Protection Regulations or GDPR = Get Demand Payment Ready when your hacked or audited. A Realistic project plan for GDPR Compliance. Another reality is the 95% not ready and even the 5% that say they are, will not like what they see in this plan in the hopes of becoming GDPR compliant. There is just not enough time or people to get it done in the next 8 months and even if you had 2 years. This is a harsh reality and without the use of software technology and strict yet flexible, repeatable methodologies, it just won’t happen. Look at this Project plan of what needs to be done, do the math, see the complexity of data movement and code and programs needed then give us a call.

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3. How can we: Surface & Distribute Insights from Unstructured Data? Or: How do we deliver to you the interesting bits of knowledge out of a lot of text?

4. Problem: Insights from Data Silos Case Study: Eli Lilly / Life Science

5. Solution: Natural Language Processing? Transform a pile of Documents into A pile of Facts . We still have a pile.

6. Solution: Natural Language Processing + Linked Relationships via Ontologies? What in the network is important ?

7. Solution: Natural Language Processing + Linked Relationships + Relevancy? Relevant to whom?

8. Solution: Natural Language Processing + Linked Relationships + Relevancy + Collaboration. Ah Ha! Relevant information can find me via my context. My colleagues can help.

9. Then we can Surface & Distribute Insights Deploy solution as a service so Insights can be embedded anywhere.

10. People Databases, Ontologies News, PR, Documents, Reports Innovation Network

11. Innovation Network - DEMO Web Application Application Plug-In

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13.

14. Innovation Network kHarmony™ Data Representation: Each Object has Properties “ chunks” of RDF Unique URI “ Core” RDFs Model

15. Innovation Network kHarmony™ HyperEdges: Statements about Statements Track Provenance Difficult in RDF alone (everything “reified”)

16.

17. Semantic Collaborative Statements

18. Semantic Knowledge Statements Domain Ontology + Instance Statements

19. Semantic Knowledge Statements Direct or Inferred

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24. Innovation Network - DEMO Web Application Application Plug-In … including OpenCalais

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26. Innovation Network Interface

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42. Innovation Network

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Alitora Innovation Networks

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