1. Clinical Design Program Work Stream 1 PhUSE Semantic Technology WG Mitra Rocca Tarek Elbeik Mary Banach 1

2. Topics  Background  Building Industry  Yosemite Project  Semantic Technology, Industry and Implementation 2

3. Background  Explore how other industries are leveraging semantic technology for decision making  How is semantic technology leveraged for various use cases 3

4. Building Industry  An ontology-based knowledge management system, which is a prototype that aims at enhancing the collaborative activities in the early building design phase.  Building industry produces a mass amount of information that has very high reuse value of explicit knowledge, which is the important strategic resource of an organization  Reuse and share of knowledge plays a vital role for improving collaboration amongst the involved stakeholders throughout the building life cycle 4

5. Other Resources Many companies that you are familiar with have already started using Semantic web technology.  Google  Facebook  Twitter  LinkedIn http://iswc2015.semanticweb.org/calls/industry 5

6. Yosemite Project 6

7. The Yosemite Project for Healthcare Information Interoperability http://YosemiteProject.org/ 7

8. 8 Yosemite Project MISSION: Semantic interoperability of all structured healthcare information

9. Standardize the standards  Use RDF & family as a common, computable definition language  Semantically link standards  Converge on common definitions 9

10. Semantic Technology, Industry and Implementation 10

11. Industrial Implementation of Emerging Semantic Technology Incorporation of Legacy Information Collect Knowledge: Maintain legacy information used for product development and company practices Associate: Identification and mining of key categorical information in order to accurately associate the captured knowledge with associated instances Ease of Adoption Identify Challenges: Possible challenges that may be encountered during implementation New Software: Installation, maintenance training, managing software, training, learning curve, compatibility with existing applications Customization: Customized structure and relationships that relate to the specific practices of the company Robustness of File Type Support Common Storage Framework and Central Document Repository: Ability to support multiple file types allows all product information to be stored in a single database, reducing redundancy throughout the entire database and improving knowledge storage and retrieval Security Tight Control: Outside hackers and concerns from within an organization Access: Controlled, log in names and password, restricted access levels Secret Sauce: Keep competitive advantages but often ensure required confidentiality Addressing the End User Intuitive User Interface: Software introduced to industry will challenge employees who would prefer to stick with existing systems Keep it Simple: Simple navigation / retrieval, seamless employee adoption, Web browser access Adapted from Breindel JT, Grosse IR, Krishnamurty S, Altidor J, Wileden J, Trachtenberg Witherell P. Techniques for Industrial Implementation of Emerging Semantic Technology http://scholarworks.umass.edu/theses/779/ and Towards Industrial Implementation of Emerging Semantic Technologies http://www.nist.gov/manuscript-publication-search.cfm?pub_id=908123 http://scholarworks.umass.edu/theses/779/http://www.nist.gov/manuscript-publication-search.cfm?pub_id=908123 Challenges facing implementation of Semantic Technology in industry rather than academia Using Raytheon Integrated Defense Systems as a model to identify industry issues and solutions Implementing Semantic Technology in Industry: Five issues identified Described the thee solutions Semantic Wiki: SMW+ MemoExtractor Software Tool E-Design Framework 11

12. Industrial Implementation of Emerging Semantic Technology (Additional points) Incorporation of Legacy Information Collect Knowledge: Maintain legacy information used for product development and company practices Associate: Identification and mining of key categorical information in order to accurately associate the captured knowledge with associated instances Ease of Adoption Identify Challenges: Possible challenges that may be encountered during implementation New Software: Installation, maintenance training, managing software, training, learning curve, compatibility with existing applications Customization: Customized structure and relationships that relate to the specific practices of the company Robustness of File Type Support Common Storage Framework and Central Document Repository: Ability to support multiple file types allows all product information to be stored in a single database, reducing redundancy throughout the entire database and improving knowledge storage and retrieval Security Tight Control: Outside hackers and concerns from within an organization Access: Controlled, log in names and password, restricted access levels Secret Sauce: Keep competitive advantages but often ensure required confidentiality Addressing the End User Intuitive User Interface: Software introduced to industry will challenge employees who would prefer to stick with existing systems Keep it Simple: Simple navigation / retrieval, seamless employee adoption, Web browser access Adapted from Breindel JT, Grosse IR, Krishnamurty S, Altidor J, Wileden J, Trachtenberg Witherell P. Techniques for Industrial Implementation of Emerging Semantic Technology http://scholarworks.umass.edu/theses/779/ and Towards Industrial Implementation of Emerging Semantic Technologies http://www.nist.gov/manuscript-publication- search.cfm?pub_id=908123 http://scholarworks.umass.edu/theses/779/http://www.nist.gov/manuscript-publication- search.cfm?pub_id=908123 1.Garbage in- Garbage out 2.Manage 1.Addressing intercompany issues 1.Answering internal 2.Answering to the FDA 1.Accomodating 2.Manage 1.Addressing intercompany issues 12

13. Incorporation of Legacy Information Ease of Adoption Robustness of File Type Support Security Addressing the End User 1.Data must be “governed” to make it useful 2.Data Governance separate from Data Operations 3.Data not easily accessible, within organizations, local businesses, data warehouses, documents 1.Data must be “governed” to make it useful 2.Data Governance separate from Data Operations 3.Collecting and linking information from various sources 4.Data harmonization Semantic Wiki: SMW+ MemoExtractor Software Tool E-Design Framework Examples of different Industries’ Issues and Implementation of Semantic Technology 1.Requires “massive” business and IT transformation 1.Rapidly addresses regulatory requirements Financial Services Viacom 1.60 networks and 500 digital media properties 2.Have 23 different files but actually all about the same topic! 3.Multi-platform is standard for this industry 4.Need to make all “material reusable, findable, searchable and purposeable 1.How to sell semantics up and down the corporate chain: From C level execs to tech staff and developers 2.Identify and allow “Superstars developers” create library and framework, and extraction layer so other developers call into a service 1.Standards need to be followed by all, security of new tools in place, debugging and troubleshooting 1.Helping you deal with a certain amount of uncertainty and chaos 13

14. Clinical Development Plan Set goal Condition Population Survival Function Quality Characterize Intervention Clinical effect Biological effect (if feasible) Correlation between exposure and clinical effect Demonstrate benefit & risk Credible study design and analysis plan Minimize bias and uncertainty Assure participant safety and data integrity Stephen Hirschfeld: Clinical Development Specifications Incorporation of Legacy Information Ease of Adoption Robustness of File Type Support Security Addressing the End User Semantic Wiki: SMW+ MemoExtractor Software Tool E-Design Framework 14

15. Thank You 15

16. Backup Slides 16

17.  Semantic Organizer:  A collaborative knowledge management system for supporting distributed NASA teams.  The teams could be for a variety of domains and tasks ranging from scientific data collection and field activity to accident investigation after mishaps. SO is essentially a customizable, semantically structured repository and provides a common access point for all work products in team tasks.  Members of a team can upload, store, and query a wide variety of information in different formats into the repository.  NASA Taxonomy:  Building an enterprise-wide taxonomy for NASA.  The intended use of the taxonomy is to help NASA personnel – scientists and engineers find information, through the use of intelligent search, browsing, and navigation systems that utilize the taxonomy  SWEET  The Semantic Web for Earth and Environmental Technologies (SWEET) an effort aimed at making the discovery and access of NASA’s Earth Science data products over the Web more effective and intuitive. The focus has primarily been on developing a variety of ontologies for earth science information sources, earth science data and earth science subject domains of interest. NASA 17

18. 5 Ways Semantic Technology is Transforming the Financial Services Industry (2014) http://www.wallstreetandtech.com/data-management/5-ways-semantic-technology- is-transforming-the-financial-services-industry/a/d-id/1279130 Salient points: – Need to reduce costs and increase revenue – Many changes that challenge this approach – “Rapidly evolving regulatory oversight” – Must comply with regulatory agencies and prove quality of data – Customers familiar with apps demanding better service/products – Requires “massive” business and IT transformation – Data not easily accessible within organizations, local businesses, data warehouses, and documents 18

19. Why Two Industry Giants Have Semantic Technology in Their Sites (2013): Telecom. – http://www.dataversity.net/why-two-industry-giants-walmart-and-viacom- have-semantic-technology-in-their-sites/ http://www.dataversity.net/why-two-industry-giants-walmart-and-viacom- have-semantic-technology-in-their-sites/ – Viacom: Salient points: Largest pure-play media company with 60 networks and 500 digital media properties (including Paramount Pictures) Weak point: US. vs. non US digital assets, MPEG-2 for here, MPEG- 4 for there – Moving Pictures Experts Group: Body responsible for standards used for video encoding 1.MPEG-2 (Moving Pictures Experts Group): standard to encode high quality videos for the, then emerging, DVD media 2.MPEG-4: Developed later; an encoding method for devices with limited resources including portable devices (media players, mobile phones) and online hiring of video and audio files. 19

20.  Have 23 different files but actually all about the same topic!  Multi-platform is standard for this industry  Need to make all “material reusable, findable, searchable and purposeful”  Semantic technology is “helping you deal with a certain amount of uncertainty and chaos”  How to sell semantics up and down the corporate chain: From C level execs to tech staff and developers  Identify and allow “Superstars developers” create library and framework, and extraction layer so other developers call into a service Why Two Industry Giants Have Semantic Technology in Their Sites (2013): Telecom. 20

21. Why Two Industry Giants Have Semantic Technology in Their Sites (2013): Telecom.  Moving along in the project:  Measure progress to build traction  Measure effectiveness of your ontologies, the utility of utilities created and changing world views by semantic stores.  Example:  Facilities group semantics-driven app to track people’s office moves, bring together data from various silos including HR, phone, and facilities to know who, where and when a user logs into a new VoIP phone  Managing Media: Metadata tracking and different internal communities’ ideas about what media is (one man’s episode is another man’s installment)  Critical: Standards need to be followed by all, security of new tools in place, debugging and troubleshooting 21

22. Why Two Industry Giants Have Semantic Technology in Their Sites (2013): Retail Walmart: Salient points:  Application of semantic technology for:  200 million walk in customers per week  43 million unique visitors to Walmart.com per month.  Example: – Developed semantic algorithms for color detection to understand that red is a color: 1.Rank shirts that come in red first 2.What colors are close to red 3.Show “red” customer if red is not available (other options) 4.Pick the right accompanying image 22

23. Practical Application of Semantic Technology and Personalization in Travel (2012) http://www.tnooz.com/article/practical-application-of-semantic-technology-and- personalization-in-travel/ http://www.tnooz.com/article/practical-application-of-semantic-technology-and- personalization-in-travel/ Salient points: Making products more accessible to customers, making more “customized” offerings – Planning for semantic search technology: adapting your inventory to appeal to your customers – Understand what you sell and how you sell it – Determine what you know about your customers – Rethink and classify your inventory and services – Make the necessary changes to the descriptive information presented to your customers 23

24. Semantic Technology May Help NIH in its HealthCare Advancement Mission (2014) http://www.dataversity.net/42549/ Salient points: Looking at clinical trials, what’s missing, and where opportunities lie NIH: Progress of funding recipients, missing opportunities Need to assess its portfolio and any research gaps One option is to access more datasets, but requires investment and time to build With semantic technology, we can leave data where it is and link together to address and correlate questions 24

25. Semantic Technology May Help NIH in its HealthCare Advancement Mission (2014) 2013 Prototype: – Looked at cancer drug discovery clinical trials from 5 different datasets by comparing data warehouses vs. semantic web technologies with linked data 25

26. Semantic Technology May Help NIH in its HealthCare Advancement Mission (2014) Schematic 26