doctoral consortium@ruleml2015 -multidimensional ontologies for contextual quality data...

Multidimensional Ontologies for Contextual Quality DataSpecification and Extraction

Mostafa MilaniSupervisor: Prof. Leopoldo Bertossi

Carleton UniversitySchool of Computer Science

Ottawa, Canada

(Carleton University) Ontology-Based Multidimensional Contexts 1 / 15

Problem Statement Introduction

Multidimensional Contexts and Data Quality

Measurements tablecontains thetemperatures of patientsat a hospital

MeasurementsTime Patient Value

Sep/5-12:10 Tom Waits 38.2Sep/6-11:50 Tom Waits 37.1Sep/7-12:15 Tom Waits 37.7Sep/9-12:00 Tom Waits 37.0Sep/6-11:05 Lou Reed 37.5Sep/5-12:05 Lou Reed 38.0

A doctor suppose/expects the table to contain:

”The body temperatures of Tom Waits for September 5

taken around noon with a thermometer of brand B1”

But Measurements does not contain the information to make thisassessment

An external context can provide that information, making it possibleto assess the given data

Contex is modeled as relational databases (Bertossi et al., BIRTE 2010)

The database under assessment is mapped into the contextualdatabase for further data quality analysis and cleaning

Context is commonly of a multi-dimensional nature

The dimensional aspects of context are not considered in(Bertossi et al., BIRTE 2010)

Multidimensional Context Extended HM Data Model

Extending Context with Multidimensional Data

We can see the context as an ontology, containing:

A MD data model/instance:

PatientWard: A table containing the location of patients

Hospital dimension: Represents the hierarchy of locations

Information such as a hospital guideline:

”Temperature measurement for patients in standard care unithave to be taken with thermometers of Brand B1”

Basis data model: HM model (Hurtado and Mendelzon, 2005)

We extend the HM model (Maleki et al., AMW 2012)

Informally, some of the new ingredients in MD contexts:

Dimensions as in the HM

Categorical relations: Generalize fact tables, not necessarily numericalvalues, linked to different levels of dimensions, possibly incomplete

Dimensional rules: Generate data where missing

Dimensional constraints: Constraints on (combinations of) categoricalrelations, involve values from dimension categories)

Dimensional rules and constraints can support and restrictupward/downard navigation

Example

Ward and Unit:

categories of Hospital

dimension

UnitWard(unit,ward): a

parent/child relation

PatientUnit

id Unit Day Patient

1 Standard Sep/5 Tom Waits

3 Intensive Sep/7 Tom Waits

4 Intensive Sep/6 Lou Reed

5 Standard Sep/5 Lou Reed

PatientWard

id Ward Day Patient

1 W1 Sep/5 Tom Waits

4 W3 Sep/6 Lou Reed

5 W2 Sep/5 Lou Reed

AllHospital

Institution

Standard Intensive Terminal

W1 W2 W3 W4

allHospital

AllTime

PatientWard: categorical relation with Ward and Day categoricalattributes taking values from dimension categories

Example

Ward and Unit:

dimension

PatientUnit

id Unit Day Patient

PatientWard

id Ward Day Patient

4 W3 Sep/6 Lou Reed

5 W2 Sep/5 Lou Reed

AllHospital

Institution

W1 W2 W3 W4

allHospital

AllTime

Example

Ward and Unit:

dimension

PatientUnit

id Unit Day Patient

PatientWard

id Ward Day Patient

4 W3 Sep/6 Lou Reed

5 W2 Sep/5 Lou Reed

AllHospital

Institution

W1 W2 W3 W4

allHospital

AllTime

Example

Ward and Unit:

dimension

PatientUnit

id Unit Day Patient

PatientWard

id Ward Day Patient

4 W3 Sep/6 Lou Reed

5 W2 Sep/5 Lou Reed

AllHospital

Institution

W1 W2 W3 W4

allHospital

AllTime

Dimensional Constraints

Example

Categorical relations are subject to dimensional constraints:

A referential constraint restricting units in PatientUnitto elements in the Unit category, as a negative constraint:

⊥ ← PatientUnit(u,d ; p),¬Unit(u)

“All thermometers used in a unit are of the same type”:

t = t ′ ← Thermometer(w , t; n),Thermometer(w ′, t′; n′),

UnitWard(u,w),UnitWard(u,w ′) An EGD

“No patient in intensive care unit on August /2005”:

⊥ ← PatientWard(w ,d ; p),UnitWard(Intensive,w),

MonthDay(August/2005, d)

Example

Dimensional Rules

Example

Data in PatientWard generate data about patients forhigher-level categorical relation PatientUnit:

PatientUnit(u,d ; p) ← PatientWard(w ,d ; p),

UnitWard(u,w)

Since relation schemas ”match”, ∃-variable in the head is not needed

Rule is used to navigate from PatientWard.Ward upwards toPatientUnit.Unit via UnitWard

Once at the level of Unit, it is possible to take advantage of aguideline -in the form of a rule- stating that:

“Temperatures of patients in a standard care unit are taken with oralthermometers”

Dimensional Rules

Example

UnitWard(u,w)

Dimensional Rules

Example

UnitWard(u,w)

Dimensional Rules

Example

UnitWard(u,w)

Dimensional Rules

Example

UnitWard(u,w)

Dimensional Rules

Example

UnitWard(u,w)

Multidimensional Context Ontological Representation of the Extended MD Model

Datalog± as Representation Language

We use Datalog± as our representation language (Cali et al., 2009)

An extension of Datalog for ontology building with efficientaccess to underlying data sources

A family of languages with different syntactic restrictions on rules toguarantee decidability

The chase (that forwards propagates data through rules) may notterminate

Our MD contexts has the general forms of dimensional rules andconstraints captured by Datalog± TGDs, EGDs, and NegativeConstraints

Properties of MD Ontologies and Query Answering

Our Datalog± MD ontologies become weakly-sticky Datalog±programs (Cali et al., 2012)

It is crucial that repeated variables in TGDs are for categoricalattributes (a finite number of values can be taken by them)

Weak-stickiness guarantees tractability of conjunctive queryanswering (QA): only an initial portion of the chase has to beinspected

A non-deterministic algorithm WeaklySticky-QAns for weakly-stickyDatalog± (Cali et al., 2012)

We proposed a deterministic version of the algorithm forweakly-sticky programs and studied optimization technique (Milani and

Bertossi, AMW 2015)

Multidimensional Context MD Context for Quality Data Assessment

MD Contexts and Quality Query Answering: The Gist

The MD ontology M becomes part of the context for data qualityassessment

The original instance D of schema S is to be assessed or cleanedthrough the context

By mapping D into the contextual schema/instance C

Example

A dimensional rule in M:

PatientUnit(u, t; p)← PatientWard(w , d ; p),DayTime(d , t),

UnitWard(u,w)A quality predicate:

TakenWithTherm(t, p, b)← PatientUnit(u, t; p), u = Standard, b = B1

Example

UnitWard(u,w)

A quality predicate:

Example

Quality version Measurementsq:

Measurementsq(t, p, v)← Measurements ′(t, p, v),

TakenWithTherm(t, p, b), b = B1, y = certified

A doctor asks the body temperatures of Tom Waits for September 5taken around noon:

Q(t, v) : Measurements(t, Tom Waits, v) ∧ Sep5-11:45 ≤ t ≤ Sep5-12:15

He expects that the measurements are taken with a thermometer ofbrand B1

Example

Replacing predicates of S in Q with their quality versions in Sq:

Qq(t, v) :Measurementsq(t, Tom Waits, v)∧Sep5-11:45 ≤ t ≤ Sep5-12:15

Applying the definition of quality versions:

QC(t, v) : Measurements ′(t, p, v) ∧ TakenWithTherm(t, p, B1) ∧p = Tom Waits ∧ Sep/5-11:45 ≤ t ≤ Sep/5-12:15

Unfolding the definition of quality predicates in P:

QM(t, v) :Measurements ′(t, p, v) ∧ PatientUnit(u, t; p) ∧ u=Standard ∧p = Tom Waits ∧ Sep/5-11:45 ≤ t ≤ Sep/5-12:15

Example

Measurements ′ has the same extension of Measurements

PatientUnit is computed by QA on M

The first second and lastmeasurements have theexpected quality

The first measurement is aclean answer to Q:t = Sep/5-12:10 and v=38.2

Example

Conclusions

Multidimensional contexts are represented as Datalog± ontologies

They allow us to specify data quality conditions, and to retrievequality data

Development, implementation of the query answering algorithms isongoing work

Several extensions:

Uncertain downward-navigation in dimensional rules

Checking dimensional constraints not only on the result of the chasebut while data generation

Relaxing the assumption of complete categorical data, and studying itseffect on dimensions

Conclusions

Several extensions:

Conclusions

Several extensions:

Conclusions

Several extensions:

Conclusions

Several extensions:

Conclusions

Several extensions:

Conclusions

Several extensions:

doctoral consortium@ruleml2015 -multidimensional ontologies for contextual quality data...

Science

body temperatures of

multidimensional ontologies

lou reed

data quality analysis

cleaning carleton university

given data contex

temperatures of patients

external context