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  • Cardiovascular diseases kill more than cancer and they generate more direct and indirect costs.
  • This happens mainly because information doesn’t flow as expected during emergency situations.Patient unconscious.Doctors and paramedics act mainly on the basis of statistical and not actual knowledge.Motivated by these facts, a hospital asked us to find a way to overcome the problem of limited information.Hoping that this would have been resolved by straightforward technology transfer and easy money for the researchers involved we accepted the job. Unfortunately the problem wasn’t so easy and we actually had to do research to provide a solution. Problem Solved? No. Now the information flows but we still need to access it effectively.
  • SAFE EDBT 2011

    1. 1. Keyword-basedContext-aware Selection of Natural Language Query-Patterns<br />Giorgio Orsi, LetiziaTanca and Eugenio Zimeo<br />EDBT Conference – Uppsala<br />March 23rd 2011<br />
    2. 2. Background:Cardiovascular diseases<br />2<br />March 16, 2011<br />SAFE – EDBT Conference<br />Courtesy of American Heart Association: Heart Disease & Stroke Statististics (2009)<br />
    3. 3. 3<br />March 16, 2011<br />SAFE – EDBT Conference<br />Background:Emergency rescue of people with CVD<br />(1)<br />emergency<br />(2)<br />rescue<br />(3)<br />on-site assistance<br />missing information<br />time constraints<br />limited technology<br />(5)<br />surgey preparation<br />(4)<br />transport to hospital<br />(6)<br />surgery<br />
    4. 4. Positioning:which information access paradigm?<br />4<br />March 16, 2011<br />SAFE – EDBT Conference<br />Form-based:<br />IR-style:<br />NLP queries:<br />Keyword search<br />Schema-less:<br />graph patterns:<br />too rigid, application flow does not always “covers” the users needs.<br />interpretation of keywords, output are documents and not tuples.<br />non-trivial NL analysis takes time and shallow analysis is too imprecise.<br />good! if keywords are interpreted<br /><ul><li>semantics is not exploited enough
    5. 5. still affected by uncertainty</li></li></ul><li>5<br />March 23, 2011<br />SAFE – EDBT Conference<br />Approach:The SAFE way<br />Desiderata:<br />query-answeringsystem<br />execution<br />formulation<br />DB<br />user<br />relevant results<br />context<br />context<br />SAFE:<br />keywords<br />ranked<br />query patterns<br />instantiation<br />of query<br />patterns<br />queries<br />user<br />user review<br />context<br />
    6. 6. Approach:Query Patterns<br />6<br />March 22, 2011<br />SAFE – EDBT Conference<br /><nlquery id=“Q23"><br /> <sentence><br /> …<br /> </sentence><br /> <variables><br /> …<br /> </variables><br /> <formalQuery><br /> <query><br /> …<br /> </query><br /> <resources><br /> …<br /> </resources><br /> </formalQuery><br /></nlquery><br /><query><br /> select ?name ?formula<br />where {<br /> ?x rdf:typedomain:Substance.<br />?y rdf:typedomain:Substance.<br />?x domain:subName ?n1.<br />?x domain:formula ?formula.<br />{<br /> ?x domain:interacts ?y.<br />}<br />?y domain:subName ?n2<br />FILTER (?n2 = '<fvarref=“v1"/>')<br />}<br /></query><br /><resources><br /> <res modelRef="&domain#Substance" /><br /> <res modelRef=“&domain#Additive" /><br /><res modelRef="&domain#Molecule" /><br /> <res modelRef="&domain#Pharmacon" /> <br /> <res modelRef="&domain#interacts" /><br /><res modelRef="&domain#foodPresence" /><br /></resources><br /><sentence description=“pharmacological interactions"><br /> <fixed><br />show the substances and their formulas <br /> which are known to interact with<br /> </fixed><br /> <varref=“v1"/><br /></sentence><br /><variables><br /> <variable id=“v1" label=“pharmacon name" type=“xsd:string"/><br /></variables><br />
    7. 7. Approach:Keyword to Ontology Matching<br />LOnt: ontological terms (labels)<br />ontology  controlled vocabulary<br />keywords:<br />search terms (e.g., patient, drug)<br />parameters (e.g., “John Doe”, “49.5 Kg”)<br />online keyword suggestion<br />auto-completion<br />semantically-related terms<br />frequently-used terms<br />7<br />March 22, 2011<br />SAFE – EDBT Conference<br />S: suggested keywords<br />K: input keywords<br />LOnt = {…, heart stroke, heart failure, CPR, resuscitation, …}<br />Intended input: <heart stroke><br />Intended input: <CPR><br />S = Ø<br />S = {CPR, heart massage, …}<br />S = {heart stroke, heart failure, …}<br />S = {resuscitation, …}<br />K = Ø<br />K = {heart stroke, CPR}<br />K = {heart stroke}<br />input = <he… <br />Input = <c… <br />input = “”<br />
    8. 8. 8<br />March 22, 2011<br />SAFE – EDBT Conference<br />Approach:Pertinence<br />Construct S by picking nterms t from LOnt related to the keywords already chosen (those in the set K)<br />S = f( freq( t ), pert( t, K ) ) <br />LR1 = {drug, pharmaceutical, medicinal}<br />K = { , ascriptin}<br />drug<br />LR2 = {disease, condition, illness, sickness}<br />input=Ø<br />LR3 = {treats, cures, heals}<br />R4<br />R3<br />R5<br />R1<br />R2<br />LR4 = {name}<br />string<br />string<br />0.5<br />0.5<br />0.25<br />LR5 = {code}<br />1.0<br />0.25<br />pertinence computation:<br />phase 1: best-match decoration<br />phase 2: neighbors decoration<br />phase 3: pertinence combination<br />assuming n = 6…<br />S = { <br /> treats, cures, heals, <br /> name, disease, <br /> condition<br /> }<br />
    9. 9. 9<br />March 22, 2011<br />SAFE – EDBT Conference<br />Approach:Ranking the Query Patterns<br />naïve approach<br />Rank by average pertinence of the formal resources in the pattern<br />rkgp=𝑟𝑖∈𝑅𝑃(𝑝)𝑝𝑒𝑟𝑡(𝑟𝑖, 𝐾)𝑅𝑃(𝑝)<br /> <br />normalized approach<br />use the number of resources directly associated to a keyword and mentioned in the pattern<br />rkgnormp=𝜃×rkgp<br /> <br />𝜃= 1−𝑅𝑃(𝑝)𝑅𝑃(𝑝)×𝑅𝑃(𝑝)RK+𝑅𝑃(𝑝)<br /> <br />
    10. 10. Approach:Focus by Context-Awareness<br />10<br />March 22, 2011<br />SAFE – EDBT Conference<br />all<br />role<br />situation<br />topic<br />anamn<br />treat<br />doctor<br />para-md<br />rescue<br />ER<br />patology<br />pharm<br />relevant areas definition<br />keyword suggestion<br />pattern-ranking<br />query-answering<br />chem<br />natural<br />CeV<br />CaV<br />
    11. 11. 10 people without a previous experience of the systems<br />50 natural-language query patterns<br />metric  access time (aT)<br />Break down metrics: aT = thT + kpT + srT + qeT + coT<br />Thinking time (thT)<br />pertinence computation (kpT)<br />Scoring and ranking (srT)<br />query execution time (qeT)<br />communication time (coT)<br />Experimentation:Experimental settings<br />11<br />March 22, 2011<br />SAFE – EDBT Conference<br />
    12. 12. Experimentation:Validation (1)<br />5 query patterns (randomly selected from the pool) to each user.<br />The “right” queries were found: <br />in 65 % of cases on top of the list<br />in 25 % of cases at the second position<br />in 8 % cases of the first result page<br />In some cases, the testers were not able to formulate the right query using the form-based system.<br />12<br />March 22, 2011<br />SAFE – EDBT Conference<br />
    13. 13. Experimentation:Validation (2)<br />13<br />March 22, 2011<br />SAFE – EDBT Conference<br />1<br />2<br />3<br />4<br />5<br />6<br />7<br />8<br />9<br />10<br />11<br />12<br />13<br />14<br />15<br />16<br />17<br />18<br />19<br />
    14. 14. Summary:Conclusion and future work<br />now…<br />novel paradigm for keyword-based search<br />context-aware and semantic ranking of query patterns<br />fast and precise information access<br />14<br />March 22, 2011<br />SAFE – EDBT Conference<br />future…<br />automatic definition of query patterns<br />automatic definition of natural language descriptions<br />automatic definition of relevant areas<br />
    15. 15. Q & A<br />
    16. 16. Two implementations:<br />Maemo Linux on Nokia Smartphones N810 and N900<br />Web based on OpenLaszlo and enterprise technologies<br />Experimental testbed in a client/server environment<br />Web-based SAFE vs form-based system provided by a hospital<br />Experimentation:Testbeds<br />16<br />March 22, 2011<br />SAFE – EDBT Conference<br />