SWRL Tutorial 01

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Adapted by Martin O’Connor Stanford Medical Informatics, Stanford University

Transcript of SWRL Tutorial 01

Page 1: SWRL Tutorial 01

Adapted by Martin O’ConnorStanford Medical Informatics, Stanford University

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Outline

Rules and the Semantic Web: OWL + SWRL

SWRLTab: a Protégé-OWL development environment for SWRL

Knowledge-driven Querying Relation-to-OWL mapping

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Semantic Web Stack

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Limitations in OWL

The OWL reasoning tools are mostly related to classes and classification.

OWL reasoning is able to compute all the property values that are implied by the property characteristic.

In OWL it is not possible to establish that a person is the boss of a secretary, only that the person is a boss.

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Rule-based Systems are common in many domains

Engineering: Diagnosis rules Commerce: Business rules Law: Legal reasoning Medicine: Eligibility, Compliance Internet: Access authentication

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Rule Markup (RuleML) Initiative

Effort to standardize inference rules.

RuleML is a markup language for publishing and sharing rule bases on the World Wide Web.

Focus is on rule interoperation between industry standards.

RuleML builds a hierarchy of rule sublanguages upon XML, RDF, and OWL, e.g., SWRL

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What is SWRL?

SWRL is an acronym for Semantic Web Rule Language.

SWRL is intended to be the rule language of the Semantic Web.

SWRL includes a high-level abstract syntax for Horn-like rules.

All rules are expressed in terms of OWL concepts (classes, properties, individuals).

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SWRL: Combining Ontologies and Rules

Semantic Web Rule Language (SWRL)

A proposal to combine ontologies and rules: Ontologies: OWL-DL Rules: RuleML

SWRL = OWL-DL + RuleML OWL-DL: variable free

corresponding to SHOIN(D) RuleML: variables are used.

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Why Do We Need a Rule Language?

A rule language is needed for several reasons: The existing rule sets can be reused. Expressivity can be added to OWL Although expressivity always comes

with a price, i.e.Decidabilit It is easier to read and write rules

with a rule language. Rules are called syntactic sugar; True in some cases but not in all

situations

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SWRL Rule Format (1)

SWRL rules have the form of an implication between an antecedent (body) and consequent (head).

The intended meaning can be read as: whenever the conditions specified in the antecedent hold, then the conditions specified in the consequent must also hold.

Both the antecedent (body) and consequent (head) consist of zero or more atoms.

Head

(Consequent)

Body

(Antecedant)

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SWRL Rule Format (2)

An empty antecedent is treated as trivially true (i.e. satisfied by every interpretation), so the consequent must also be satisfied by every interpretation;

An empty consequent is treated as trivially false (i.e., not satisfied by any interpretation), so the antecedent must also not be satisfied by any interpretation.

Multiple atoms are treated as a conjunction

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SWRL Rules : Summary

Summarizing, SWRL rules can be described as follows: antecedent → consequent

in which the antecedent and consequent consist of one or multiple atoms.

Typical SWRL reasoning occurs on property and instance levels.

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Example SWRL Rule: Has uncle

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hasParent(?x, ?y) ^ hasBrother(?y, ?z)

→ hasUncle(?x, ?z)

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Example SWRL Rule with Named Individuals: Has brother

Person(Fred) ^ hasSibling(Fred, ?s) ^ Man(?s) → hasBrother(Fred, ?s)

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Example SWRL Rule with Literals and Built-ins: is adult?

Person(?p) ^ hasAge(?p,?age) ^ swrlb:greaterThan(?age,17)

→ Adult(?p)

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SWRL Characteristics

W3C Submission in 2004: http://www.w3.org/Submission/SWRL/

Based on OWL-DL Has a formal semantics Rules saved as part of ontology Increasing tool support: Bossam,

R2ML, Hoolet, Pellet, KAON2, RacerPro, SWRLTab

Can work with reasoners

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Combining OWL reasoning and SWRL reasoning

OWL has inference capabilities through the OWL characteristics of properties, like inversion, symmetry and transitivity.

SWRL has inference capabilities through the SWRL rules.

In order to avoid the necessity of iteration between OWL inferences and SWRL inferences, it would be good if rule engines could also apply the OWL characteristics.

This implies that OWL characteristics would be ‘translated’ to a SWRL equivalent.

In SWRL it is perfectly possible to define rules for symmetry, inversion, or transitivity characteristics.

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SWRLTab

A Protégé-OWL development environment for working with SWRL rules

Supports editing and execution of rules

Extension mechanisms to work with third-party rule engines

Mechanisms for users to define built-in method libraries

Supports querying of ontologies

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SWRLTab: http://protege.cim3.net/cgi-bin/wiki.pl?SWRLTab

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What is the SWRL Editor?

The SWRL Editor is an extension to Protégé-OWL that permits the interactive editing of SWRL rules.

The editor can be used to create SWRL rules, edit existing SWRL rules, and read and write SWRL rules.

It is accessible as a tab within Protégé-OWL.

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SWRL Java API

The SWRL API provides a mechanism to create and manipulate SWRL rules in an OWL knowledge base.

This API is used by the SWRL Editor. However, it is accessible to all OWL Plugin developers.

Third party software can use this API to work directly with SWRL rules and integrate rules into their applications

Fully documented in SWRLTab Wiki.

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Limitations of SWRLTab/Protégé

SWRLTab is a very convenient tool for editing SWRL rules since it supports automatic completion of the properties and class names and checks the syntax of the entered rules.

Rules are considered as instance data in Protégé.

Protégé, even in the combination with SWRLTab, does not support SWRL rule execution.

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Need of a Rule Engine

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Including SWRL Data in Protege

INSTANCES RULES

CLASSES

AP

PL

ICA

TIO

N

SW

RL

Ontology

Knowledge

Base

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Need for Rule Engine

The execution of SWRL rules requires the availability of a rule engine.

The most general picture of a rule engine : The rule engine can perform reasoning using a set

of rules and a set of facts as input. Any new facts that are inferred are used as input

to potentially fire more rules (in forward chaining).

Rules and facts should be available in a format that is accessible to the rule engine.

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Rule Engine Execution

RULES

FACTS

RULE ENGINE

New FACTS

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Provision of Rules to Rule Engines

Translations that are necessary in the current state-of-the-art to be able to run SWRL rules on a Protégé data set. The rules have to be translated and

introduced in the rule engine (1). Afterwards, the ontology and the

knowledge base have to be translated and introduced into the rule engine (2).

After reasoning (3), the results of the reasoning should be

translated back into the Protégé format (4).

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Actions for Execution of SWRL Rules based on Protégé Input

INSTANCES RULES

CLASSES

AP

PL

ICA

TIO

N

SW

RL

Ontology

Knowledge

Base

RULES

FACTS

RULE

ENGINE

New FACTS

(1)

(2)

(4)

(3)

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Reasoning Methods

the two reasoning methods are forward chaining and backward chaining.

Forward Chaining In forward chaining, the input and input

changes are used to select the rules that need to be fired,

and the inferred changes are treated as input changes (so they can lead to the firing of rules, too).

Backward Chaining In backward chaining an assertion is put or a

query is set and the rule engine reasons back to the conditions, implied by the assertion or the query, that need to be applied to the data.

The rule engine returns an answer to the assertion or to the query based on that data.

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SWRL JESS INTEGRATION

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Executing SWRL Rules

SWRL is a language specification Well-defined semantics Developers must implement

engine Or map to existing rule engines Hence, a bridge…

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OWL

KB

+

SWRL

SWRL Rule

Engine Bridge

Data

Knowledge

Rule Engine

GUI

SWRL Rule Engine Bridge

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SWRL Rule Engine Bridge

Given an OWL knowledge base it will extract SWRL rules and relevant OWL knowledge.

Also provides an API to assert inferred knowledge.

Knowledge (and rules) are described in non Protégé-OWL API-specific way.

These can then be mapped to a rule-engine specific rule and knowledge format.

This mapping is developer’s responsibility.

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Example: SWRL Bridge to Integrate Jess Rule Engine with Protégé-OWL

Jess is a Java-based rule engine. Jess system consists of a rule

base, fact base, and an execution engine.

Available free to academic users, for a small fee to non-academic users

Has been used in Protégé-based tools, e.g., JessTab.

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Outstanding Issues

SWRL Bridge does not know about all OWL constraints: Contradictions with rules possible! Consistency must be assured by the

user incrementally running a reasoner.

Hard problem to solve in general. Integrated reasoner and rule

engine would be ideal. Possible solution with KAON2.

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SWRL Built-in Bridge

SWRL provides mechanisms to add user-defined predicates, e.g., hasDOB(?x, ?y) ^ temporal:before(?y, ‘1997’)… hasDOB(?x, ?y) ^ temporal:equals(?y, ‘2000’)…

These built-ins could be implemented by each rule engine.

However, the SWRL Bridge provides a dynamic loading mechanism for Java-defined built-ins.

Can be used by any rule engine implementation.

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Defining a Built-in in Protégé-OWL

Describe library of built-ins in OWL using definition of swrl:Builtin provided by SWRL ontology.

Provide Java implementation of built-ins and wrap in JAR file.

Load built-in definition ontology in Protégé-OWL. Put JAR in plugins directory.

Built-in bridge will make run-time links.

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Example: defining stringEqualIgnoreCase from Core SWRL Built-ins Library

Core SWRL built-ins defined by: http://www.w3.org/2003/11/swrlb

Provides commonly needed built-ins, e.g., add, subtract, string manipulation, etc.

Normally aliased as ‘swrlb’. Contains definition for

stringEqualIgnoreCase

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Example Implementation Class for Core SWRL Built-in Methods

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package edu.stanford.smi.protegex.owl.swrl.bridge.builtins.swrlb;

import edu.stanford.smi.protegex.owl.swrl.bridge.builtins.*;

import edu.stanford.smi.protegex.owl.swrl.bridge.exceptions.*;

public class SWRLBuiltInMethodsImpl implements SWRLBuiltInMethods

{

public boolean stringEqualIgnoreCase(List arguments) throws BuiltInException { ... }

....

} // SWRLBuiltInMethodsImpl

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Example Implementation for Built-in swrlb:stringEqualIgnoreCase

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private static String SWRLB_SEIC = "stringEqualIgnoreCase";

public boolean stringEqualIgnoreCase(List arguments) throws BuiltInException

{

SWRLBuiltInUtil.checkNumberOfArgumentsEqualTo(SWRLB_SEIC, 2, arguments.size());

String argument1 = SWRLBuiltInUtil.getArgumentAsAString(SWRLB_SEIC, 1, arguments);

String argument2 = SWRLBuiltInUtil.getArgumentAsAString(SWRLB_SEIC, 2, arguments);

return argument1.equalsIgnoreCase(argument2);

} // stringEqualIgnoreCase

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Invocation from Rule Engine

Use of swrlb:stringEqualIgnoreCase in rule should cause automatic invocation.

SWRL rule engine bridge has an invocation method.

Takes built-in name and arguments and performs method resolution, loading, and invocation.

Efficiency a consideration: some methods should probably be implemented natively by rule engine, e,g., add, subtract, etc.

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Using SWRL to Express Protocol Constraints

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Patient(?p) ^

hasExtendedEvent(?p, ?eevent1) ^ hasExtendedEvent(?p, ?eevent2) ^ temporal:hasValue(?eevent1, ?event1) ^ temporal:hasValidTime(?eevent1, ?event1VT) ^ temporal:hasTime(?event1VT,

?event1Time) ^ temporal:hasValue(?eevent2, ?event2) ^

temporal:hasValidTime(?eevent2, ?event2VT) ^ temporal:hasTime(?event2VT, ?event2Time) ^

hasVisit(?event1, ?v1) ^ hasVisit(?event2, ?v2) ^

hasActivity(?event1, ?a1) ^ hasName(?a1, "Omalizumab") ^

hasActivity(?event2, ?a2) ^ hasName(?a2, "Immunotherapy") ^

temporal:before(?event2Time, ?event1Time) ^

temporal:durationMinutesLessThan(60, ?event2Time, ?event1Time)

-> NonConformingPatient(?p)

On days that both immunotherapy and omalzumab are administered,

omalzumab must be injected 60 minutes after immunotherapy.

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SWRL and Querying

SWRL is a rule language, not a query language

However, a rule antecedent can be viewed as a pattern matching specification, i.e., a query

With built-ins, language compliant query extensions are possible.

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A SWRL ‘Query’

Person(?p) ^ hasAge(?p, ?age) ^ swrlb:greaterThan(?age, 17) -> swrlq:select(?p) ^ swrlq:orderBy(?age)

Return all adults in ontology:

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SWRLQueryTab

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SWRLQueryTab: Displaying Results

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SWRLQueryTab

Query functionality added with built-ins

Interactive query execution with tabular results display

Low-level JDBC-like API for use in embedded applications

Can use any existing rule engine back end

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Use of SWRL as Query Language is Attractive

Cleaner semantics than SPARQL OWL-based, not RDF-based Very extensible via built-ins, e.g.,

temporal queries using temporal built-ins

Can work with reasoners

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Querying: Semantic Issues

Syntactic SWRL conformance is easy

However, SWRL is based on OWL-DL so assumes open world semantics

Querying closes the world, e.g., how many adults in ontology?

Should not make inferences based on query results – nonmonotonicity!

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Dealing with Relational Data

Almost all data are relational Relational queries are at the

database level not at the knowledge level

We would like results of queries and analyses to be added to our store of knowledge

We need to bridge the gap Triple stores a longer term

solution

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Querying and Databases

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Querying and Databases

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Model Mismatch

Relational n-ary tuples vs. RDF-triples

Relational databases can store a lot of knowledge; typically they don’t

Some mappings can be inferred The more normalized the database,

the easier it is to infer mappings Manual user-driven mapping is

usually required

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Solution Requirements

A schema ontology to describe schema of arbitrary relational database

A mapping ontology to describe mapping of data from tuples to triples

Mapping software to dynamically map

A query language A query engine

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Mapper

OWL

KB Bridge

User Interface

Data

Knowledge

Engine

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Dynamic Relation-to-OWL Mapping

Bridge generates optimized relational queries to retrieve data

Current SPARQL-based systems D2RQ D2OMapper

Approach used successfully in BioSTORM project for surveillance data

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Optimization

Current ontology tools not scalable

Databases are scalable: – offload as much work to RDBMS as possible

Query engines must optimize Built-ins are a difficulty and an

opportunity

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Built-in Optimization

Person(?p) ^ hasAge(?p, ?age) ^ swrlb:greaterThan(?age, 17) -> swrlq:select(?p) ^ swrlq:orderBy(?age)

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Two Approaches

Built-in optimization by annotating built-in definitions and exploiting in query engine Numerical built-in optimizations Temporal built-in optimizations

In-query optimization to avoid redundant data requests Jess with Java Fact Storage Provider

Framework

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Rule/Query Distinction

Significant optimizations possible for a queries

Optimizations for entire rule bases not as dramatic – however, still possible, e.g., Analyzing temporal ‘slices’ Analyzing spatial regions

Dealing with reasoners Database updates?

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Lessons learned so far

SWRL provides a useful though not magical increase in expressivity

Suited well to some tasks, not to others

Can work well as a query language Built-ins provide a very nice way to

increase expressivity Triple-stores are a longer term

solution, but dealing with relational data now is crucial

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Part A

Describe the Need for a Rule Engine

Describe the Generic Execution Procedure of Rules using a Rule Engine.

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Design Rules for your Domain

For your project: Describe using Pseudo-code, some

essential Rules Describe using SWRL syntax, the

same Rules Two Rules must contain the use of

Swrl built-ins Verify if the Rules are consistent

with your OWL Definitions and Constraints

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Essential Readings on SWRL

Supporting Rule System Interoperability on the Semantic Web with SWRL Martin O’Connor1, Holger

Knublauch1, Samson Tu1, Benjamin Grosof2, Mike Dean3, William Grosso4, Mark Musen1

Semantic Web Tutorial –Vahid 2008 How to Make SWRL Rules Safe?