AllegroGraph - Cognitive Probability Graph webcast

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Information about AllegroGraph - Cognitive Probability Graph webcast

Published on June 27, 2016

Author: FranzInc

Source: slideshare.net

1. The power of the Cognitive Probability Graph (aka Cognitive Computing) June 2016 Jans Aasman ja@franz.com

2. 10 years ago Structured Data

3. 7 years ago Structured Data Unstructured Data

4. 4 to 5 years ago Structured Data Unstructured Data Knowledge Domain knowledge Linked Open Data Vocabularies Taxonomies/Ontologies

5. New #1: Learning. Feed output of data science back into data infrastructure Structured Data Unstructured Data Knowledge Domain knowledge Linked Open Data Vocabularies Taxonomies/Ontolo gies Probabilistic Inferences.

6. New # 2: everything in one (distributed) semantic graph Structured Data Unstructured Data Knowledge Domain knowledge Linked Open Data Vocabularies Taxonomies/Ontol ogies Probabilistic Inferences. Unstructured Data Knowledge Domain knowledge Linked Open Data Vocabularies Taxonomies/Ontol ogies

7. AKA: Cognitive Computing Structured Data Unstructured Data Knowledge Domain knowledge Linked Open Data Vocabularies Taxonomies/Ontol ogies Probabilistic Inferences. Unstructured Data Knowledge Domain knowledge Linked Open Data Vocabularies Taxonomies/Ontol ogies

8. Examples Examples • Healthcare: If I have this class of diagnostics and I get this procedure what are some of the new symptoms I might get in the next two years. • eCommerce and brand protection: find all my products based on product similarity • Logistics: what can I statistically predict about part P breaking down and what other parts do I usually buy after that part breaks down. • Police Intelligence: find the most plausible story of a temporally orderend shortest path between two criminal through observed (hard) facts and inferred (soft facts) • Fraud detection: find links between your local chamber of commerce and the panama papers through similar names and addresses.

9. Example healthcare • Franz and Montefiore are partners in the Semantic Data Lake project.

10. One cognitive computing platform for all healthcare analytics

11. Example healthcare • We created a single data centric platform that can serve any type of analytic without building a new data mart for every new question. • Currently 2.7 million patients with 10 years of data • All data captured in a Unified Clinical Event model with 350 classes of events.

12. Healthcare: structured and unstructured data

13. Structured patient data combined with complex integrated terminology

14. Provenance for every value

15. Healthcare: the knowledge bases • More > 180 vocabularies and terminology systems integrated in on unified terminology system (Mesh, Snomed, UMLS, RxNorm, LOINC etc, etc) • External databases and • Linked Open Data

16. OMOP 11089001 6600349 11894800 5 7534205 16790501 14667809 35896705 9209005 1732609 9908905 1469609 329005 LOINC 113345001 140460009 skos:semanticRelation skos:narrower 118948005 skos:broader “9209005” SNOMEDCT M0024135 M0008124 M0004742 skos:semanticRelation skos:narrower M0015742 skos:broader“Abdominal Pain” “M0024135” skos:exactMatch A0549302 A0978543 skos-xl:prefLabel 9209005 “Abdominal Pain” SAB AUI SUI MeSH SNOMEDCT MedDRA rdfs:subClassOf rdf:type C0172359 C0232487 C0238551 “Abdominal Pain” “C0000737” skos:semanticRelation skos:broader UMLS - MTH skos:notation S035799 skos-xl:label MTH STR C000737 Everything linked through SKOS SKOS/SKOS-XL ConceptScheme Concept UMLS - Semantic Net Entity Event Label

17. Population, Community Time Pt.Pt.Pt.Pt. SDL Paradigm: Pt.Pt.Pt.Pt. Diagnosis Codes Disease Classification OMIM, GONG Genetic Profile Procedure CodeHCPC Manufacturers PharmKGB Drug Classification Drug Codes DrugBank ClinicalTrials CER PubMed Analytic Tapestry (closed loop analytics)

18. Healthcare: probabilistic inferences Why is this so important? • Usually the output of data science results in reports and publications but • No formal trace where the data came from • No formal link to the actual methods you used, or who did it, or when you did it • Cannot be compared to earlier results • Cannot be used as building blocks for further research • In general : the output is not queryable • This is not good for delivery of care, reproducibility of research findings, security and compliance, and results in loss of value-added information, and enterprise intellectual property and assets, and unnecessary duplication of efforts

19. Odds ratio

20. Association rules

21. K-means clustering

22. And then a query you could do never before • Using the Knowledge Base, the Structured Data and the Probabilistic inferences all at the same time. • To find the statistical links between Diabetes and Vision problems in our Semantic Data Lake • Find the set of ICD9s that are connected via one or more steps to concepts in the KB that mention Diabetes • Find the set of ICD9s that are connected via one or more steps to vision* or eye* or retinal* • An show how those two sets are related in the space of odds ratios

23. And then just a few other examples

24. In the ecommerce world: find similar objects based on > 10 criteria, including description, product codes, pictures, etc

25. Returns a Graph in a Table (ughh )

26. But powerful when visualized

27. Or like this

28. And linking with the panama papers

29. And now the researchers can start investigating

30. Summary: this is the new paradigm of computing Structured Data Unstructured Data Knowledge Domain knowledge Linked Open Data Vocabularies Taxonomies/Ontol ogies Probabilistic Inferences. Unstructured Data Knowledge Domain knowledge Linked Open Data Vocabularies Taxonomies/Ontol ogies

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