Swrl Tutorial -spring 2009

SEMANTIC WEB RULE LANGUAGE (SWRL) BY AMNA BASHARAT FAST – NU (SPRING 2009) Adapted by Martin O’Connor Stanford Medical Informatics, Stanford University FAST -NU Spring 2009 - Amna Basharat

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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 FAST -NU Spring 2009 - Amna Basharat

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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. FAST -NU Spring 2009 - Amna Basharat

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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 FAST -NU Spring 2009 - Amna Basharat

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SWRL Rule Format (1) Head

Body

(Consequent)

(Antecedant)


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.

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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 FAST -NU Spring 2009 - Amna Basharat

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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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SWRL EXAMPLE

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Example SWRL Rule: Has uncle

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. FAST -NU Spring 2009 - Amna Basharat

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SWRLTAB

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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 thirdparty rule engines Mechanisms for users to define built-in method libraries Supports querying of ontologies

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SWRLTab: http://protege.cim3.net/cgibin/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. FAST -NU Spring 2009 - Amna Basharat

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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. FAST -NU Spring 2009 - Amna Basharat

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EXECUTION OF SWRL RULES

Need of a Rule Engine FAST -NU Spring 2009 - Amna Basharat

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Including SWRL Data in Protege

Knowledge

SWRL

Ontology

APPLICATION

CLASSES

Base

INSTANCES

RULES

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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

RULE ENGINE FACTS

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). FAST -NU Spring 2009 - Amna Basharat

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Actions for Execution of SWRL Rules based on Protégé Input

Knowledge

CLASSES

RULES (1)

SWRL

Ontology

APPLICATION

(3)

RULE (2)

Base

ENGINE FACTS

(4) INSTANCES

RULES

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New FACTS

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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. FAST -NU Spring 2009 - Amna Basharat

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SWRL RULE ENGINE BRIDGE

SWRL JESS INTEGRATION FAST -NU Spring 2009 - Amna Basharat

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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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SWRL Rule Engine Bridge

GUI

OWL KB

SWRL Rule

+

Engine Bridge

Rule Engine

SWRL

Data Knowledge FAST -NU Spring 2009 - Amna Basharat

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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. FAST -NU Spring 2009 - Amna Basharat

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SWRL AND QUERYING

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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’

Return all adults in ontology: Person(?p) ^ hasAge(?p, ?age) ^ swrlb:greaterThan(?age, 17) -> swrlq:select(?p) ^ swrlq:orderBy(?age)

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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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QUERYING AND DATABASES

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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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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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User Interface

OWL KB

Bridge

Engine

Mapper

Data Knowledge FAST -NU Spring 2009 - Amna Basharat

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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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GROUP EXERCISE CUM GROUP QUIZ

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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?

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