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see: http://ontolog.cim3.net/file/work/OntologySummit2011/2011-04-18_19_OntologySummit2011_Symposium/Track-5_Grand-Challenges-summary--RamSriram-ErnieLucier-AldenDima_20110418.pdf {nid 2SK0} |
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Co-champions: '''RamSriram, ErnieLucier, AldenDima''' {nid 2SL8} == Overview {nid 2SL9} == The Grand Challenges Track had the following goals: {nid 2SLA} * Invite speakers from a variety of domains {nid 2SLB} * For each domain presented: {nid 2SLC} ** Identify a grand challenge problem {nid 2SLD} ** Summarize current state of the art on ontology use {nid 2SLE} ** Determine gaps that hinder real-world ontology applications {nid 2SLF} ** Enumerate the actions that need to be taken to overcome these gaps {nid 2SLG} ** Brainstorm ideas for a grand challenge problem {nid 2SLH} == Speakers and Domains Represented {nid 2SLI} == Our invited speakers represented three domains (Health Care, Social networks and Homeland Security): {nid 2SLJ} * '''ChristopherWelty''' (IBM) - Grand challenge for Watson-like Systems {nid 2SLL} * '''RameshJain''' (UC, Irvine) - Social Life Networks – Ontology-based Recognition {nid 2SLN} * '''EliotSiegel''' (UMD, School of Medicine) - The Dr. Watson Project: Clinical Perspective {nid 2SLP} * '''ChristopherChute''' (Mayo Clinic) - Relationships among Biomedical Ontologies and Classifications {nid 2SLR} * '''NabilAdam''' ([[DHS]]) - Ontology Applications in Homeland Security {nid 2SLT} * '''ChristopherFrangione''' (X Prize) - Revolution through Competition: Designing Effective Incentive Prizes {nid 2SLV} === Health Care {nid 2SLW} === ''Grand Challenge:'' {nid 2SLX} Much of the Health Care domain presentations focused on IBM'S Watson which has captured much attention. It was built by IBM to advance QA (question answering) techniques. In a heavily publicized competition it won against two previous Jeopardy champions eventually but lost to Congressman Rush Holt. Its <nowiki>DeepQA</nowiki> components use the "Hypothesize-Score-Rank" strategy where the scoring (Evaluating) employs non-linguistic, ontology and background algorithms. {nid 2SLY} Challenges were identified in two settings: {nid 2SLZ} * 1) during medical school; and {nid 2SM0} * 2) after medical school. {nid 2SM1} ''Helping the medical student'' {nid 2SM2} * Medical knowledge from various sources, such as Harrison's Principles of Internal Medicine, Merck Manual Medical Library, Washington Manual of Medical Therapeutics, NLM's Clinical Question Repository, NEJM's (New England Journal of Medicine) CPC (Clinicopathological Conferences) cases and quiz material, can be utilized in their digital form. {nid 2SM3} * The above can be augmented with the computer-based simulations of human physiology and disease processes. {nid 2SM4} * Machine learning techniques can mine knowledge from Electronic Medical Records (EMRs)(secondary use of data) and provide "experiential learning." {nid 2SM5} ''Helping the physician'' {nid 2SM6} * Continue providing access to medical knowledge {nid 2SM7} * Automated chart review of EMRs {nid 2SM8} * Access multiple databases in a unified manner {nid 2SM9} * Aid in diagnosis and treatment, though encoding expert knowledge bases, including drug-drug interactions. {nid 2SMA} * Address questions of teaching Dr. Watson bedside manners. {nid 2SMB} The above challenges need advances in the following areas. {nid 2SMC} * Mining and molding knowledge {nid 2SMD} * Machine Learning {nid 2SME} * Systems Medicine (integrating genomics and clinical systems) {nid 2SMF} * Image analysis and interpretation {nid 2SMG} * Management of medical vocabularies {nid 2SMH} === Social Networking {nid 2SMI} === Social Life Networks (SLNs), which are instances of Net-centric societies, combine two evolving paradigms: computer-mediated social networks and Internet of things. Jain calls this "connecting people with resources," since an SLN can be viewed as a network of people and sensor objects, such as mobile phones and associated senors. In SLNs a considerable amount of data – image, text, other sensors -- passes through the network and should be converted into higher abstractions that can be used in appropriate reasoning. {nid 2SMJ} The discussion of Social Networking focused on three themes: {nid 2SMK} * The emerging Internet of things {nid 2SML} * Systems for Situational Analysis and Recommendation {nid 2SMM} * The need for context-based interpretation of images and text {nid 2SMN} Several trends were identified as driving Social Networking: {nid 2SMO} * Their expanding role in communication {nid 2SMP} * Micro-blogs are becoming major source of News {nid 2SMQ} * The emergence of the Internet of Things {nid 2SMR} * More than 75% of the world population owns mobile phones. {nid 2SMS} Challenges to be addressed in SLNs: {nid 2SMT} * Security of information {nid 2SMU} * Making sense of data/information {nid 2SMV} * Multimodal sensor integration {nid 2SMW} * Dealing with system complexity {nid 2SMX} Ontologies could help in developing strategies to address above challenges. {nid 2SMY} === Homeland Security {nid 2SMZ} === Homeland Security was the final domain to be presented. The DHS infrastructure and the needs for modeling, simulation and analysis were discussed. In this domain, the development, validation, acceptance, update, and integration of ontologies were seen as the key challenges. Other potential domains were identified: Sustainability, Emergency Response Management, and Financial Services. These domains are important but we were not able to cover them in our teleconference. {nid 2SN0} === Other Challenges {nid 2SN1} === Several other domains were identified during the summit. These include the following domains: {nid 2SN2} * Sustainability {nid 2SN3} * Natural disasters {nid 2SN4} * Financial (including help with taxes) {nid 2SN5} == Incentive for Progress {nid 2SN6} == The final portion of our discussion focused on developing incentive systems for moving forward in grand challenge areas. The X Prize was presented as an example of using prizes to motivate progress in key areas. The various attributes, models and development phases of prizes were presented. The $10M Ansari X Prize was offered as a successful example of using prizes. In the resulting competition, 26 teams from 7 nations spent over $100M to attempt to be the first privately-financed team to fly a spaceship capable of carrying three people to 100 kilometers altitude twice in two weeks. The next step for the community is to pick one domain and develop the appropriate X prize. {nid 2SN7} == Reference: {nid 2SN8} == * OntologySummit2011: "Grand Challenges - I" Panel Session - ConferenceCall_2011_03_24 {nid 2SN9} * [http://ontolog.cim3.net/cgi-bin/wiki.pl?OntologySummit2011_Symposium#nid2QX4 Track-5 Summary Report presentation] at the OntologySummit2011_Symposium {nid 2SK0} |