AllegroGraph - Cognitive Probability Graph webcast
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The document discusses the concept of cognitive computing and its application in various fields such as healthcare, ecommerce, logistics, and fraud detection through cognitive probability graphs. It highlights the integration of structured and unstructured data, as well as the importance of unified terminology systems and probabilistic inferences for analytics. The aim is to create a comprehensive platform for data analysis that enhances queryability and research reproducibility while reducing redundancy.
AllegroGraph - Cognitive Probability Graph webcast
- 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
- 14. Structured patient data combined with complex integrated terminology
- 15. Provenance for every value
- 16. 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
- 17. 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
- 21. 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
- 22. Odds ratio
- 31. 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
- 34. And then just a few other examples
- 35. In the ecommerce world: find similar objects based on > 10 criteria, including description, product codes, pictures, etc
- 37. Returns a Graph in a Table (ughh )
- 38. But powerful when visualized
- 39. Or like this
- 40. And linking with the panama papers
- 41. And now the researchers can start investigating
- 42. 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