Keyword-basedContext-aware Selection

of Natural Language Query-Patterns

Giorgio Orsi, Letizia Tanca and Eugenio Zimeo

EDBT Conference – Uppsala

March 23rd 2011

2SAFE – EDBT Conference

Background:Cardiovascular diseases

March 16, 2011

Courtesy of American Heart Association: Heart Disease & Stroke Statististics (2009)

3SAFE – EDBT ConferenceMarch 16, 2011

Background:Emergency rescue of people with CVD

(1)emergency

(2)rescue

(3)on-site assistance

(4)transport to hospital

(5)surgey preparation

(6)surgery

missing informationtime constraintslimited technology

4SAFE – EDBT Conference

Positioning:which information access paradigm?

March 16, 2011

Form-based:

IR-style:

NLP queries:

Keyword search

Schema-less:

graph patterns:

too rigid, application flow does not always “covers” the users needs.interpretation of keywords, output are documents and not tuples.non-trivial NL analysis takes time and shallow analysis is too imprecise.good! if keywords are interpreted

• semantics is not exploited enough

• still affected by uncertainty

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user

Approach:The SAFE way

query-answeringsystem

DB relevant results

Desiderata:

SAFE:

user

keywords

rankedquery

patterns

instantiation

of querypatterns

queries

user review

formulation execution

context context

context

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Approach:Query Patterns

<nlquery id=“Q23"> <sentence> … </sentence> <variables> … </variables> <formalQuery> <query> … </query> <resources>

… </resources>

</formalQuery></nlquery>

<sentence description=“pharmacological interactions"> <fixed>

show the substances and their formulas which are known to interact with </fixed> <var ref=“v1"/> </sentence>

<variables> <variable id=“v1" label=“pharmacon name" type=“xsd:string"/></variables>

<query> select ?name ?formula where { ?x rdf:type domain:Substance. ?y rdf:type domain:Substance. ?x domain:subName ?n1. ?x domain:formula ?formula. { ?x domain:interacts ?y. }

?y domain:subName ?n2 FILTER (?n2 = '<fvar ref=“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>

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S = {CPR, heart massage, …}K = {heart stroke, CPR}

S = {heart stroke, heart failure, …}K = {heart stroke}

ontology controlled vocabulary

keywords:search terms (e.g., patient, drug)

parameters (e.g., “John Doe”, “49.5 Kg”)

online keyword suggestionauto-completion

semantically-related terms

frequently-used terms

Approach:Keyword to Ontology Matching

input = <he…

Intended input: <heart stroke>

LOnt = {…, heart stroke, heart failure, CPR, resuscitation, …}

K = ØS = Ø

Input = <c…

S = {resuscitation, …}

LOnt : ontological terms (labels)

S : suggested keywords

K : input keywords

Intended input: <CPR>

input = “”

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pertinence computation:

phase 1: best-match decoration

phase 2: neighbors decoration

phase 3: pertinence combination

Approach:PertinenceConstruct S by picking n terms t from LOnt related to the keywords already chosen (those in the set K)

S = f( freq( t ), pert( t, K ) )

K = { , ascriptin} LR1 = {drug, pharmaceutical, medicinal}

LR2 = {disease, condition, illness, sickness}

LR3 = {treats, cures, heals}

LR4 = {name}

LR5 = {code}

input = Ø

R1

R2

string stringR4 R3 R5

drug

1.00.50.5

0.250.25

assuming n = 6…

S = { treats, cures, heals, name, disease, condition }

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Approach:Ranking the Query Patterns

naïve approach

Rank by average pertinence of the formal resources in the pattern

normalized approach

use the number of resources directly associated to a keyword and mentioned in the pattern

rkg (p )= ∑𝑟 𝑖∈𝑅𝑃 (𝑝)

𝑝𝑒𝑟𝑡 (𝑟𝑖 ,𝐾 )|𝑅𝑃 (𝑝)|

rkg norm (p )=𝜃×rkg (p )

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relevant areas definition

keyword suggestion

pattern-ranking

query-answering

Approach:Focus by Context-Awareness

all

roletopicsituation

rescue ER

anamn treat

para-md doctor

pharm patology

naturalchem CeV CaV

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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

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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.

Experimentation:Validation (1)

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Experimentation:Validation (2)

SAFE – EDBT Conference

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now…

novel paradigm for keyword-based search

context-aware and semantic ranking of query patterns

fast and precise information access

Summary:Conclusion and future work

future…

automatic definition of query patterns

automatic definition of natural language descriptions

automatic definition of relevant areas

Q & A

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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