Visualizing*Logical*Dependencies*in* SWRL*Rule*Bases*Visualizing*Logical*Dependencies*in*...

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Visualizing Logical Dependencies in SWRL Rule Bases Saeed Hassanpour, Mar:n J. O’Connor and Amar K. Das Stanford Center for Biomedical Informa:cs Research MSOB X215, 251 Campus Drive, Stanford, California, USA. {saeedhp, mar:n.oconnor, amar.das}@stanford.edu

Transcript of Visualizing*Logical*Dependencies*in* SWRL*Rule*Bases*Visualizing*Logical*Dependencies*in*...

Page 1: Visualizing*Logical*Dependencies*in* SWRL*Rule*Bases*Visualizing*Logical*Dependencies*in* SWRL*Rule*Bases* Saeed*Hassanpour,*Mar:n*J.*O’Connor*and*Amar*K.*Das* Stanford*Center*for*Biomedical*Informacs

Visualizing  Logical  Dependencies  in  SWRL  Rule  Bases  

Saeed  Hassanpour,  Mar:n  J.  O’Connor  and  Amar  K.  Das  

Stanford  Center  for  Biomedical  Informa:cs  Research    MSOB  X215,  251  Campus  Drive,  Stanford,  California,  USA.    

{saeedhp,  mar:n.oconnor,  amar.das}@stanford.edu    

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Overview  

•  Mo:va:on  •  Related  work  •  Methods  

•  Results  •  Summary  

•  Future  work  

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Overview  

•  Mo:va:on  •  Related  work  •  Methods  

•  Results  •  Summary  

•  Future  work  

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Why  Do  We  Need  Methods  for  Rule  Explora:on?  

•  Increasing  use  of  rules  in  ontologies  •  Increasing  size  of  rule  bases  •  Inter-­‐rela:onships  between  rules  can  be  complex  

•  Lack  of  tools  for  rule  explora:on  

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Why  is  a  Rule  Base  Complex?  

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What  is  our  Goal?  

Finding  the  underlying  logical  structures  of  rule  bases  and  visualizing  these  structures  to  help  users  to  explore  the  rule  bases  

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Example SWRL Rule

Five  SWRL  rules  rela/ng  to  drug  recommenda/ons  for  hypertensive  and  diabe/c  adult  pa/ents    

Rule A: Person(?p) ^ hasSystolicBloodPressure(?p, ?sbp) ^ hasDiastolicBloodPressure(?p, ?dbp) ^ swrlb:greaterThan(?sbp, 140) ̂swrlb:greaterThan(?dbp, 90) → hasDiagnosis(?p, Hypertension)

Rule B: Person(?p) ^ hasBloodSugarLevelBeforeMeal(?p, ?bsl) ^ swrlb:greaterThan(?bsl, 126) → hasDiagnosis(?p, Diabetes)

Rule C: hasCondition(?p, Hypertension) ^ hasCondition(?p, Diabetes) ^ → prescribedDrug(?p, ACEInhibitor)

Rule D: Person(?p) ^ hasAge(?p,?age) ^ swrlb:greaterThan(?age,17) ^ hasInsurance(?p, ?i) → InsuredAdult(?p)

Rule E: InsuredPerson(?p) ^ prescribedDrug(?p, ?d) → CoPayEligible(?p)

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Example SWRL Rule

Five  SWRL  rules  rela/ng  to  drug  recommenda/ons  for  hypertensive  and  diabe/c  adult  pa/ents    

Rule A: Person(?p) ^ hasSystolicBloodPressure(?p, ?sbp) ^ hasDiastolicBloodPressure(?p, ?dbp) ^ swrlb:greaterThan(?sbp, 140) ̂swrlb:greaterThan(?dbp, 90) → hasDiagnosis(?p, Hypertension)

Rule B: Person(?p) ^ hasBloodSugarLevelBeforeMeal(?p, ?bsl) ^ swrlb:greaterThan(?bsl, 126) → hasDiagnosis(?p, Diabetes)

Rule C: hasCondition(?p, Hypertension) ^ hasCondition(?p, Diabetes) ^ → prescribedDrug(?p, ACEInhibitor)

Rule D: Person(?p) ^ hasAge(?p,?age) ^ swrlb:greaterThan(?age,17) ^ hasInsurance(?p, ?i) → InsuredAdult(?p)

Rule E: InsuredPerson(?p) ^ prescribedDrug(?p, ?d) → CoPayEligible(?p)

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How  Do  We  Do  That?  

•  Finding  rule  dependencies  •  Finding  logical  layers  in  rules  •  Finding  rule  clusters  in  logical  layers    

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What  Do  We  Do?  

A

B C

D

E

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What  is  OWL?  

•  Ontology  Web  Language  (OWL):  goal  is  to  be  the  language  underpinning  the  Seman:c  Web  

•  Building  blocks:  classes,  proper:es,  individuals  •  Formal  descrip:on  logic-­‐based  seman:cs  

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What  is  SWRL?  

•  Seman:c  Web  Rule  Language  (SWRL)  is  intended  the  de  facto  standard  rule  language  of  the  Seman:c  Web.  

•  All  rules  are  expressed  in  terms  of  OWL  concepts  (classes,  proper:es,  individuals).  

•  SWRL  is  based  on  a  high-­‐level  abstract  syntax  for  Horn-­‐like  rules.  

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Challenges:  Sources  of  Poten:al  Inter-­‐rule  Dependencies  

•  OWL  classes:  capture  classifica:on  informa:on  about  individuals  

•  OWL  object  proper:es:  relate  individuals  to  each  other.  

•  Inference  run-­‐:me  sources:  – data  value  asser:ons  – built-­‐in  atoms  – data  range  atoms  

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Overview  

•  Mo:va:on  •  Related  work  •  Methods  

•  Results  •  Summary  

•  Future  work  

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Related  Work  

•  SAMOS    –  An  object-­‐oriented  database  management  system,  provides  a  graphical  

rule  editor  and  browser  for  managing  Event-­‐Condi:on-­‐Ac:on-­‐rules  in  databases  

–  No  mechanisms  for  showing  the  rela:onships  or  dependencies  between  rules.    

•  UML  –  Visualize  some  types  of  dependencies  between  rules    

–   Not  a  full  rule  representa:on  language:  incompa:bili:es  between  rule  modeling  and  the  object-­‐oriented  paradigm  of  UML  

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Related  Work  

•  URML  –  Based  Rule  Modeling  Language  (URML)  addresses  some  of  UML’s  

limita:ons    

–  The  overall  approach  is  focused  on  represen:ng  event  triggering  and  event  produc:on  rather  than  displaying  the  rela:onships  between  rules  themselves.    

•  Rule  Dependency  Analysis  –  These  techniques  detect  anomalies  in  rule  bases  

–   These  approaches  have  not  concentrated  on  exploring  these  dependencies  to  produce  visualiza:ons  of  overall  rule  base  structure    

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Related  Work •  Axiomé  

–  A  rule  management  tool  to    categorize, visualize, and paraphrase SWRL rules    

–  Based  on  the  syntac:c  structure  of  the  rules  and  it does not incorporate the semantics of the underlying relationships  

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Overview  

•  Mo:va:on  •  Related  work  •  Methods  

•  Results  •  Summary  

•  Future  work  

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Methods  

1.  Analyzing  dependencies  among  rules  2.  Rule  dependency  graph  genera:on  3.  Topological  sort  4.  Building  layers  of  dependencies  5.  Rule  clustering  6.  Evalua:on  

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1.  Analyzing  Dependencies  Among  Rules    

•  An  analysis  of  references  to  the  same  OWL  classes  and  object  proper:es  in  different  rules  

•  An  analysis  of  the  domain  and  range  of  object  property  atoms  to  determine  if  any  resul:ng  object  property  asser:ons  about  OWL  individuals  can  produce  dependencies.  

•  Object  property  atoms  matches  when  their  individuals  are  from:  –  The  same  classes  

–  Sub/Super  classes  –  Equivalent  classes    

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Example SWRL Rule

Five  SWRL  rules  rela/ng  to  drug  recommenda/ons  for  hypertensive  and  diabe/c  adult  pa/ents    

Rule A: Person(?p) ^ hasSystolicBloodPressure(?p, ?sbp) ^ hasDiastolicBloodPressure(?p, ?dbp) ^ swrlb:greaterThan(?sbp, 140) ̂swrlb:greaterThan(?dbp, 90) → hasDiagnosis(?p, Hypertension)

Rule B: Person(?p) ^ hasBloodSugarLevelBeforeMeal(?p, ?bsl) ^ swrlb:greaterThan(?bsl, 126) → hasDiagnosis(?p, Diabetes)

Rule C: hasCondition(?p, Hypertension) ^ hasCondition(?p, Diabetes) ^ → prescribedDrug(?p, ACEInhibitor)

Rule D: Person(?p) ^ hasAge(?p,?age) ^ swrlb:greaterThan(?age,17) ^ hasInsurance(?p, ?i) → InsuredAdult(?p)

Rule E: InsuredPerson(?p) ^ prescribedDrug(?p, ?d) → CoPayEligible(?p)

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Example SWRL Rule

Five  SWRL  rules  rela/ng  to  drug  recommenda/ons  for  hypertensive  and  diabe/c  adult  pa/ents    

Rule A: Person(?p) ^ hasSystolicBloodPressure(?p, ?sbp) ^ hasDiastolicBloodPressure(?p, ?dbp) ^ swrlb:greaterThan(?sbp, 140) ̂swrlb:greaterThan(?dbp, 90) → hasDiagnosis(?p, Hypertension)

Rule B: Person(?p) ^ hasBloodSugarLevelBeforeMeal(?p, ?bsl) ^ swrlb:greaterThan(?bsl, 126) → hasDiagnosis(?p, Diabetes)

Rule C: hasCondition(?p, Hypertension) ^ hasCondition(?p, Diabetes) ^ → prescribedDrug(?p, ACEInhibitor)

Rule D: Person(?p) ^ hasAge(?p,?age) ^ swrlb:greaterThan(?age,17) ^ hasInsurance(?p, ?i) → InsuredAdult(?p)

Rule E: InsuredPerson(?p) ^ prescribedDrug(?p, ?d) → CoPayEligible(?p)

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2.  Rule  Dependency  Graph  Genera:on  

       Rules  are  presented  as  a  nodes.  Edges  represent  dependencies  between  them.  

A

B C

D

E

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3.  Topological  Sort  

 The  rules  are  ordered  into  a  sequence  where  each  rule  is  before  all  of  its  dependent  rules    

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4.  Building  Layers  of  Dependencies  

•  Aher  sor:ng  the  rules  topologically,  the  method  then  aiempts  to  group  the  rules  into  layers  based  on  their  dependencies  

•  To  form  these  layers  we  use  a  greedy  algorithm  that  guarantees  the  minimum  number  of  layers  

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Building  Layers  of  Dependencies  -­‐  Algorithm  

L ← List of topologically sorted nodes Layers ← Empty list of nodes in each layer

for each node n in L do P is the list of n’s parents if P is empty then add n to Layers(0) else maxLayer ← The largest layer number of nodes in P

add n to Layers(maxLayer+1)

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5.  Clustering  Rules  with  Similar  Dependencies    

     As  a  final  step  aher  breaking  the  rules  into  dependency  layers,  our  method  further  clusters  the  rules  within  each  layer  into  subgroups  of  similar  rules  based  on  the  strength  of  their  dependencies.    

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Rule  Hierarchical  Clustering  

Clustering  stopping  threshold    

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Number  of  Clusters  

•  The  number  of  rule  clusters  is  decided  by  the  user    

•  We  provide  two  heuris:c  criteria  as  sugges:ons  to  automa:cally  decide  when  to  terminate  the  clustering  process:  – Find  the  most  stable  clustering  – Median  distance  for  rules  in  a  layer  

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6.  Evalua:on  

   To  evaluate  the  usefulness  and  efficacy  of  our  techniques,   we   applied   our   method   on   two  publicly   available   OWL   ontologies   containing  SWRL  rules  bases:  – Hypertension  rule  base  – Family  rela:onship  rule  base  

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Overview  

•  Mo:va:on  •  Related  work  •  Methods  

•  Results  •  Summary  

•  Future  work  

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Hypertension  Rule  Base  •  Medical  treatment  rules  for  pa:ents  with  hypertension  or  

elevated  blood  pressure  •  There  are  19  SWRL  rules  in  the  rule  base  

•  There  are  145  OWL  classes  and  proper:es  in  pa:ent  management  ontology  

•  The  ontology  and  rule  base  are  developed  by  a  separate  group  and  available  online1  

1 http://www.cs.auckland.ac.nz/~thusitha/aiim09/

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Hypertension  Rule  Base  

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Family  Rule  Base  

•  Encodes  family  rela:onships  •  There  are  146  rules  and  defines  a  set  of  rela:onships  

between  people  in  a  family  •  There  are  578  OWL  classes  and  proper:es  in  the  family  

history  ontology  •  The  ontology  and  rule  base  are  developed  by  a  separate  

group  and  available  online1  

1 National Center for Biomedical Ontology BioPortal: www.bioontology.org

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Family  Rule  Base  

•  Family  rela:onships  encoding  

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Overview  

•  Mo:va:on  •  Related  work  •  Methods  

•  Results  •  Summary  

•  Future  work  

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Summary  

•  The  increasing  size  and  complexity  of  rule  bases  makes  tools  for  rule  base  explora:on  a  necessity  

•  Our  methods  of  seman:c  rule  analysis  and  visualiza:on  enables  summarizing  and  explora:on  large  and  complex  rule  bases  

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Overview  

•  Mo:va:on  •  Related  work  •  Methods  

•  Results  •  Summary  

•  Future  work  

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Future  Work  

•  Inves:gate  addi:onal  graphical  techniques  that  will  enhance  the  display  of  the  logical  dependencies  between  layers  and  clusters  of  rules  

•  Support  explana:on  and  visualiza:on  of  inference  results

Page 40: Visualizing*Logical*Dependencies*in* SWRL*Rule*Bases*Visualizing*Logical*Dependencies*in* SWRL*Rule*Bases* Saeed*Hassanpour,*Mar:n*J.*O’Connor*and*Amar*K.*Das* Stanford*Center*for*Biomedical*Informacs

Thank  You!  

Ques:ons?  

This project was supported in part by funds from NIH grants 1R01LM009607-01A2 and 1R01MH087756-0109.