1. Keyword-basedContext-aware Selection of Natural Language Query-Patterns Giorgio Orsi, LetiziaTanca and Eugenio Zimeo EDBT Conference – Uppsala March 23rd 2011
2. Background:Cardiovascular diseases 2 March 16, 2011 SAFE – EDBT Conference Courtesy of American Heart Association: Heart Disease & Stroke Statististics (2009)
3. 3 March 16, 2011 SAFE – EDBT Conference Background:Emergency rescue of people with CVD (1) emergency (2) rescue (3) on-site assistance missing information time constraints limited technology (5) surgey preparation (4) transport to hospital (6) surgery
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6. Approach:Query Patterns 6 March 22, 2011 SAFE – EDBT Conference <nlquery id=“Q23"> <sentence> … </sentence> <variables> … </variables> <formalQuery> <query> … </query> <resources> … </resources> </formalQuery> </nlquery> <query> select ?name ?formula where { ?x rdf:typedomain:Substance. ?y rdf:typedomain:Substance. ?x domain:subName ?n1. ?x domain:formula ?formula. { ?x domain:interacts ?y. } ?y domain:subName ?n2 FILTER (?n2 = '<fvarref=“v1"/>') } </query> <resources> <res modelRef="&domain#Substance" /> <res modelRef=“&domain#Additive" /> <res modelRef="&domain#Molecule" /> <res modelRef="&domain#Pharmacon" /> <res modelRef="&domain#interacts" /> <res modelRef="&domain#foodPresence" /> </resources> <sentence description=“pharmacological interactions"> <fixed> show the substances and their formulas which are known to interact with </fixed> <varref=“v1"/> </sentence> <variables> <variable id=“v1" label=“pharmacon name" type=“xsd:string"/> </variables>
8. 8 March 22, 2011 SAFE – EDBT Conference Approach:Pertinence Construct S by picking nterms t from LOnt related to the keywords already chosen (those in the set K) S = f( freq( t ), pert( t, K ) ) LR1 = {drug, pharmaceutical, medicinal} K = { , ascriptin} drug LR2 = {disease, condition, illness, sickness} input=Ø LR3 = {treats, cures, heals} R4 R3 R5 R1 R2 LR4 = {name} string string 0.5 0.5 0.25 LR5 = {code} 1.0 0.25 pertinence computation: phase 1: best-match decoration phase 2: neighbors decoration phase 3: pertinence combination assuming n = 6… S = { treats, cures, heals, name, disease, condition }
9. 9 March 22, 2011 SAFE – EDBT Conference Approach:Ranking the Query Patterns naïve approach Rank by average pertinence of the formal resources in the pattern rkgp=𝑟𝑖∈𝑅𝑃(𝑝)𝑝𝑒𝑟𝑡(𝑟𝑖, 𝐾)𝑅𝑃(𝑝) normalized approach use the number of resources directly associated to a keyword and mentioned in the pattern rkgnormp=𝜃×rkgp 𝜃= 1−𝑅𝑃(𝑝)𝑅𝑃(𝑝)×𝑅𝑃(𝑝)K+𝑅𝑃(𝑝)
10. Approach:Focus by Context-Awareness 10 March 22, 2011 SAFE – EDBT Conference all role situation topic anamn treat doctor para-md rescue ER patology pharm relevant areas definition keyword suggestion pattern-ranking query-answering chem natural CeV CaV
11. 10 people without a previous experience of the systems 50 natural-language query patterns metric access time (aT) Break down metrics: aT = thT + kpT + srT + qeT + coT Thinking time (thT) pertinence computation (kpT) Scoring and ranking (srT) query execution time (qeT) communication time (coT) Experimentation:Experimental settings 11 March 22, 2011 SAFE – EDBT Conference
12. Experimentation:Validation (1) 5 query patterns (randomly selected from the pool) to each user. The “right” queries were found: in 65 % of cases on top of the list in 25 % of cases at the second position in 8 % cases of the first result page In some cases, the testers were not able to formulate the right query using the form-based system. 12 March 22, 2011 SAFE – EDBT Conference
14. Summary:Conclusion and future work now… novel paradigm for keyword-based search context-aware and semantic ranking of query patterns fast and precise information access 14 March 22, 2011 SAFE – EDBT Conference future… automatic definition of query patterns automatic definition of natural language descriptions automatic definition of relevant areas
16. Two implementations: Maemo Linux on Nokia Smartphones N810 and N900 Web based on OpenLaszlo and enterprise technologies Experimental testbed in a client/server environment Web-based SAFE vs form-based system provided by a hospital Experimentation:Testbeds 16 March 22, 2011 SAFE – EDBT Conference
Editor's Notes
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.
