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    

Overview  

•  Mo:va:on  •  Related  work  •  Methods  

•  Results  •  Summary  

•  Future  work  

Overview  

•  Mo:va:on  •  Related  work  •  Methods  

•  Results  •  Summary  

•  Future  work  

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  

Why  is  a  Rule  Base  Complex?  

What  is  our  Goal?  

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

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)

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)

How  Do  We  Do  That?  

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

What  Do  We  Do?  

A

B C

D

E

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  

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.  

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  

Overview  

•  Mo:va:on  •  Related  work  •  Methods  

•  Results  •  Summary  

•  Future  work  

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  

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    

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  

Overview  

•  Mo:va:on  •  Related  work  •  Methods  

•  Results  •  Summary  

•  Future  work  

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  

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    

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)

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)

2.  Rule  Dependency  Graph  Genera:on  

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

A

B C

D

E

3.  Topological  Sort  

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

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  

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)

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.    

Rule  Hierarchical  Clustering  

Clustering  stopping  threshold    

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  

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  

Overview  

•  Mo:va:on  •  Related  work  •  Methods  

•  Results  •  Summary  

•  Future  work  

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/

Hypertension  Rule  Base  

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

Family  Rule  Base  

•  Family  rela:onships  encoding  

Overview  

•  Mo:va:on  •  Related  work  •  Methods  

•  Results  •  Summary  

•  Future  work  

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  

Overview  

•  Mo:va:on  •  Related  work  •  Methods  

•  Results  •  Summary  

•  Future  work  

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

Thank  You!  

Ques:ons?  

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