Visualizing Logical Dependencies in SWRL Rule Bases

Transcription

1 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

2 Overview Mo:va:on Related work Methods Results Summary Future work

3 Overview Mo:va:on Related work Methods Results Summary Future work

4 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

5 Why is a Rule Base Complex?

6 What is our Goal? Finding the underlying logical structures of rule bases and visualizing these structures to help users to explore the rule bases

7 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)

8 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)

9 How Do We Do That? Finding rule dependencies Finding logical layers in rules Finding rule clusters in logical layers

10 What Do We Do? A B C E D

11 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

12 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.

13 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

14 Overview Mo:va:on Related work Methods Results Summary Future work

15 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

16 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

17 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

18 Overview Mo:va:on Related work Methods Results Summary Future work

19 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

20 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

21 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)

22 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)

23 2. Rule Dependency Graph Genera:on Rules are presented as a nodes. Edges represent dependencies between them. A B C E D

24 3. Topological Sort The rules are ordered into a sequence where each rule is before all of its dependent rules

25 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

26 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)

27 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.

28 Rule Hierarchical Clustering Clustering stopping threshold

29 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

30 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

31 Overview Mo:va:on Related work Methods Results Summary Future work

32 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 online 1 1

33 Hypertension Rule Base

34 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 online 1 1 National Center for Biomedical Ontology BioPortal:

35 Family Rule Base Family rela:onships encoding

36 Overview Mo:va:on Related work Methods Results Summary Future work

37 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

38 Overview Mo:va:on Related work Methods Results Summary Future work

39 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

40 Thank You! Ques:ons? This project was supported in part by funds from NIH grants 1R01LM A2 and 1R01MH