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SCHEDULE ,[object Object]
9:00am-10:15am  INAUGURATION CEREMONY(Endeavor Room, Student Union)- Chancellor of University System of Ohio, Eric Fingerhut ,[object Object]
Techincal Introduction of Knoesis (Dr.AmitSheth– Director, and the team)
10:15am-11:15am  CAREER PANEL (Endeavor Room, Student Union)
 Professor Ahmed Elmagarmid, Purdue University
Dr. Daniel Gruhl, IBM, Research
Dr. PankajMehra, HP Labs
Dr. Daniel Serfati, Aptima
Dr. Harry Silver, LexisNexis
 11:15am-12:00pm  POSTERS/DEMOS
Twitris, Cuebee, Scooner, Sensor Web etc
2:00pm-3:00pm  Technical Talk I- Semantic Supercomputing ,[object Object],[object Object]
Ohio Center of Excellence on  Knowledge-Enabled Computing (Kno.e.sis) scientiapotentiaest Knowledge is Power  Francis Bacon, 1597 …established and popularized deductive methodologies for scientific inquiry 3
Ohio Center of Excellence Knowledge-Enabled Computing (Kno.e.sis) Ohio Center of Excellence on  Knowledge-Enabled Computing (Kno.e.sis) Our society has progressed from  Agriculture  Industrial Service Knowledge    … we live in a Knowledge Society 4
Knowledge Economy As the economies have transformed …. … new era … where the principal component of  value creation, productivity and economic growth is  knowledge.    Florida & Kenny 91 5
Knowledge Economy Thousands knowledge-intensive services are leading the all sector in job creation,  R&D spending, average wages and growth. 6
Knowledge Economy Exploitation of Knowledge for improving human experience/effectiveness, social and economic gains Computer + Human Knowledge (knowledge discovery, insight) Computer + Collective Action Collective Intelligence (Knowledge) Computer (IR,ML,NLP) + Knowledge = (better extraction, next gen search) learning, insight Humanknowledge + Computer Representation = Situated Cognition Computer + Human + Knowledge(better) Interaction, (improved) Human Experience, (higher) Productivity 7
Knowledge Services Creation and exploitation of Knowledge for improving human experience/effectiveness, social and economic gains Computer + Human Knowledge (knowledge discovery, insight) Computer + Collective Action  Collective Intelligence Computer (IR,ML,NLP) + Knowledge = (better) learning (extraction, search…) Humanknowledge+ Computer Representation = Situated Cognition Computer + Human + Knowledge  (better) Interaction, (improved) Human Experience, (higher) Productivity 8
affects Migraine Magnesium Stress isa inhibit Knowledge discovery Patient Calcium Channel  Blockers 2D-3D & Immersive Visualization, Human Computer Interfaces Impacting bottom line Domain Models/Knowledge Structured text (Scientific publications / white papers) Biomedical Knowledge Discovery, Knowledge  Management & Visualization SEMANTICS, MEANING PROCESSING Patterns / Inference / Reasoning Meta data / Semantic Annotations Search and browsing Metadata Extraction/Semantic Annotations Massive amounts of data Clinical Trial Data Experimental  Results Public domain knowledge (PubMed) 9
Kno.e.sis’ leadership in semantic processing will contribute to basic theory about  computation and cognitive systems, and address pressing  practical problems associated with productive thinking in the face of an explosion of data. Kno.e.sis intends to lead a march from information age to meaning age. Kno.e.sis Vision 10
Why Kno.e.sis? ,[object Object]
We incorporate synergy to carry out exceptional vision
We are world class – and recognized as such
Our track record shows we can succeed
We target the growth aspect of economy and regional/state needs11
Globally Competitive Careers and Economic Development Dayton Region Companies WPAFB Directorates Human Effectiveness Sensor Woolpert REI Tech,  Aptima Tech^Edge SAIC LexisNexis Knowledge Workers, Products, Services and Applications Human Sciences  & Health Care Advanced Data Management Defense/Aerospace R & D Application to Regional Industry Cluster daytaOhio – a WCI Kno.e.sis+Faculty Strengths ,[object Object]
Consulting and Technology  Transfer
 Cognitive Science & Human Factors
 Data Analysis/Mining/Visualization
 Info. & Knowledge Mgmt
 Web 3.0 (Semantics, Services, Sensors)
 Virtual Worlds, Social Computing
 High Performance/Cloud Computing
 Bioinformatics/Biomedicine, HealthcareAcademic Research and  Infrastructure 12
13
Significant Infrastructure Whole-Body Laser  Range Scanner VERITAS stereoscopic 3D  visualization NMR AVL 14
Exceptional Regional Collaboration ,[object Object]
Human Effectiveness Directorate
Sensors Directorate 15
Exceptional National Collaboration ,[object Object]
Microsoft, IBM, HP, Google16
Exceptional International Collaboration ,[object Object],17
Ohio Center of Excellence on  Knowledge-Enabled Computing (Kno.e.sis) Knowledge & Next Generation of the Web Web has become the core infrastructure for the knowledge economy Web 1.0: Web of Documents and Media Web 2.0: Web of People Web 3.0: Web of Meaning MeenaNagarajan Advanced Data Management
KNOWLEDGE EXTRACTION “Human Cognition” AND Psychology AND Neuroscience? 19
Harvesting Community Knowledge & Scientific Corpus “Human Cognition” AND Psychology AND Neuroscience 20
Human Performance & Cognition Ontology “Human Cognition” AND Psychology AND Neuroscience 21
INSIGHTS &KNOWLEDGE DISCOVERY
DataExploration on the Web Utilizes semantic information from domain models to guide user interaction  ,[object Object]
“EGF and hypoxia induce CXCR4 in non-small cell lung cancer...” [PMID:15802268]Record navigation trail:  ,[object Object],23
The Memex Vision  [Vannevar Bush] Of now the human brain navigates an information space … “It operates by association. With one item in its grasp, it snaps instantly to the next that is suggested by the association of thoughts, in accordance with some intricate web of trails carried by the cells of the brain.” 24
Cuebee and Scooner Shared as open source, with demos online. 25
Ohio Center of Excellence  Knowledge-Enabled Computing  SEMANTIC SENSOR WEB 26
br />Semantic Sensor Web Utilizes semantic technologies for situational awareness Data Storage (Raw Data, XML, RDF) Semantic Analysis and Query Feature Extraction and Entity Detection Semantic Annotation Sensor Data Collection Ontologies ,[object Object]
 Time Ontology
 Situation Theory Ontology
 Domain Ontology 27
Ohio Center of Excellence  Knowledge-Enabled Computing (Kno.e.sis) SEMANTIC SOCIAL WEB
Everyone Wants to talk …and be heard! Hundreds and thousands of tweets, facebook posts, blogs  about a single event, multiple narratives, strong opinions, breaking news.. 29
TWITRIS : Twitter+Tetris Our attempt to help you keep up with citizen observations on Twitter WHAT are people saying, WHEN, from WHERE Puts citizen reports in context for you by overlaying it with news, wikipedia articles! 30
31
Ohio Center of Excellence on  Knowledge-Enabled Computing (Kno.e.sis) Biomedical & Health Sciences From data to understanding Mike Raymer Many biomedical collaborations: Nick Reo, Toxicology;  Tim Cope, Neuroscience; Jerry Alter, Protein Science; Oleg Paliey, Microbiolgy and health Many health care collaborations: Kate Cauley, Center for Healthy Communities; UC-Irvine – Emergency health, Sonia Michail, obesity & intestinal health; Bradley Jacobs, human health Human Sciences  & Health Care

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Inauguration Function - Ohio Center of Excellence in Knowledge-Enabled Computing at Wright State (Kno.e.sis)

Notes de l'éditeur

  1. Let me start by offering my appreciation for our Chancellor Dr. Fingerhut’s visionary leadership in establishing the Ohio Center of Excellence program that identifies Centers and program that generate world-class research and help draw talent and investment to the state. I would be remiss if I did not call out tremendous leadership that our President Dr. Hopkins and his entire leadership team has shown in regards to identifying and promoting these centers– my tanks to Dr. Angle, Dr. Bantle. Dr. Jang, and Dr. Sudkamp– thanks for your early and steadfast support for Kno.e.sis.
  2. In the 16th Century, Francis Bacon—an Englishphilosopher, statesman, scientist, lawyer, jurist and author established deductive methodologies for scientific enquiry… and he said something that is more true than ever– Knowledge is Power
  3. A study of OECD countries over a decade showed that as much as 75% of the economic growth and most new jobs was due to knowledge-intensive services- and the importance of these services was not only limited only high-tech or IT areas, but pervaded all sectors of economy
  4. Let me give a technological introduction to what our center is about: we all face a fire hose of data-- Pubmed adds 2000 to 4000 citations per day, it is usual to add about 5 gig from a single run of a scientific experiment -- and just imagine how much data created by all the cameras and 40 billion mobile sensors in the world! But even with all the search and browsing tools we have, we face huge information glut. How do we make sense from the data? Just as humans apply their knowledge and experience to understand what they see– we apply domain model or knowledge to attach meaningful labels to these data. Then we can apply computational techniques to visualize, provide situational awareness, discovery nuggets of knowledge of information and insight. For example, from all that biomedical data, what a scientist may be looking for is– how can we treat Migraine? What has Magnesium to do with Migraine? Why does Magnesium deficiency cause Migraine? What is the process by which Magnesium affects Migraine?
  5. So what is Kno.e.sis about– it is about stepping away from the concerns of storing and searching data, to that of improving human experience, human effectiveness, human performance, human productivity.
  6. Our 15 faculty from 4 colleges are already engaged in multiple jointly funded grants, pending proposals, serving on interdisciplinary programs like Biomedical Sciences PhD program and on committees of students of colleagues.
  7. One of the hallmarks of the knowledge enabled computinginfrasturucture is Enabling machines to understand the data that they are processing. At the core of this vision also shared by the Semantic Web is having a formal representation of a domain or what we call ‘domain knowledge’ –the entities involved in a domain and how are they related to each other..Where do we get some domain knowledge from – one could look to domain experts to categorize world knowledge in that domain, borrow from legacy systems or in many cases tap into the knowledge that is already present in documents ..
  8. One of our projects in this space looks into scalable harvesting of community knowledge that exists about a domain in collaborative sources such as Wikipedia and scientific corpus to construct formal models of a domain
  9. Here is an example of a Human performance n cognition Ontology developed by us using this technique and which is being used by human effectiveness directorate at the AFRLSeveral part of this work were conducted in collaboration with Hewlett Packard research labs who currently conduct beta testing of a commercial version called Taxonom.com
  10. One application of building domain models is knowledge discovery or deriving informational nuggets present in data sources by using facts that you already know about a domain
  11. One of our efforts in this direction uses existing domain models such as the UMLS or those built from wikipedia to guide browsing of concepts in documents and use the browse trails to formulate hypotheses to discover knowledge that we did not know previously existed in the data.
  12. This work that envisions our information space as a web of relationships to connect diferent document or web resources represents in many ways the beginnings of the realization of the expansive memex vision and trailblazing that Dr Vanevar Bush outlined in 1945… As he was speaking to how our human brain navigates the information space… using an association of thoughts that link one item to the next
  13. Several tools that we built in this space are available online and also shared as an open source project with the community
  14. A second area where we extensively employ domain knowledge is in interpreting various forms of sensor data in order to achieve meaningful situational awareness..Today there are more than 40 billion mobile sensors on the groundAnd nearly 4 million people carrying mobile phones taking pictruesWith so many sensors around there is a lot of information available at hand surrounding any eventHow do u know that they all relate to the same event and lend an understanding for how event evolves?
  15. The knowledge enabled techniques we develop enable a fusion of Sensor data – lending understanding from the stages of identifying sensors, to gathering processing and using this data for situational awareness and sense makingSome of our work in this space is being supported by the sensor directorate at the AFRL next door.
  16. The last representative work we’d like to share with you is our work on making sense of social data, like those from Twitter and facebookaround news worthy events that are of interest to a populace.The goal is to offer an understanding of what people are talking about and paying attention to
  17. What the social perceptions behind the data might be, the multiple narratives
  18. Twitris is our effort in this direction to help users keep up with observations made around news-worthy events.. Before I hand over the microphone to Dr. Mike Raymer, I’d like to leave you with a short demo of the deployed web application.
  19. No where is it more important to enable the path from data to understanding than in the medical and life sciences!The barriers to interdisciplinary collaboration are high – natural scientists and computer scientists speak different languages.More new words in their first biology class than an entire year of a foreign language.We have demonstrated success in breaking down those barriers.Cross-disciplinary scientists (we make it our job) & cross disciplinary students.
  20. No where is it more important to enable the path from data to understanding than in the medical and life sciences!The barriers to interdisciplinary collaboration are high – natural scientists and computer scientists speak different languages.More new words in their first biology class than an entire year of a foreign language.We have demonstrated success in breaking down those barriers.Cross-disciplinary scientists (we make it our job) & cross disciplinary students.
  21. No where is it more important to enable the path from data to understanding than in the medical and life sciences!The barriers to interdisciplinary collaboration are high – natural scientists and computer scientists speak different languages.More new words in their first biology class than an entire year of a foreign language.We have demonstrated success in breaking down those barriers.Cross-disciplinary scientists (we make it our job) & cross disciplinary students.
  22. Example – AFRL problem – collaboration with Dr. Nick Reo, School of MedicineDr. Reo – Nuclear Magnetic ResonanceProblem – highly complex data sets – many noisy results from each experiment
  23. Example – AFRL problem – collaboration with Dr. Nick Reo, School of MedicineDr. Reo – Nuclear Magnetic ResonanceProblem – highly complex data sets – many noisy results from each experiment
  24. No where is it more important to enable the path from data to understanding than in the medical and life sciences!The barriers to interdisciplinary collaboration are high – natural scientists and computer scientists speak different languages.More new words in their first biology class than an entire year of a foreign language.We have demonstrated success in breaking down those barriers.Cross-disciplinary scientists (we make it our job) & cross disciplinary students.