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Language (Formalisms) For Ontology Building. Neda Alipanah 22 October 2012. Content. Why Ontologies ? Machine Process able Knowledge Knowledge Exchange Big Data Relevant Technologies Layered Architecture Building Tools and Visualization Ontology Application Information Integration

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content
Content
  • Why Ontologies?
      • Machine Process able Knowledge
      • Knowledge Exchange
      • Big Data
  • Relevant Technologies
      • Layered Architecture
      • Building Tools and Visualization
  • Ontology Application
      • Information Integration
      • Web Database Management
      • Web Services
why ontologies
Why Ontologies?
  • Machine readable and understandable process of data
  • Consistent Knowledge Presentation for Enterprise application integration (Knowledge Exchange)
  • Nodes and links that essentially form a very large database with specific rules
why ontologies1
Why Ontologies?

1. Machine readable and understandable process of data

John Smith is Assistant Professor of Computer Science in University of X.

He is teaching several courses including Course A, B, C.

Assistant Professor

University X

John Smith

why ontologies2
Why Ontologies?

2. Consistent Knowledge Presentation for Enterprise application integration (Knowledge Exchange)

Disease

Symptoms

Patient

Address

why ontologies3
Why Ontologies?

3. Nodes and links that essentially form a very large database with specific rules.

Database capture the data and relations (Entity Relations) but not the semantic and rules

Disease

Symptoms

Patient

Address

Concept 1 is reverse of Concept 2.

Concept 2 is subclass of Concept 3.

Concept 100 has isA relation with Concept 2000 and is reverse of Concept 500.

content1
Content
  • Why Ontologies?
      • Machine Process able Knowledge
      • Knowledge Exchange
      • Big Data
  • Relevant Technologies
      • Layered Architecture
      • Building Tools and Visualization
  • Ontology Application
      • Information Integration
      • Web Database Management
      • Web Services
technologies layered architecture
Technologies- Layered Architecture

Tim Berners Lee Architecture

Logic, Proof and Trust

TRUST

P

R

I

V

A

C

Y

Rules/Query

Other

Services

RDF/Ontologies

XML/XML Schemas

URI/UNICODE

technologies layered architecture1
Technologies- Layered Architecture

URI (Uniform Resource Identifiers):

  • Simple and Extensible means for Identifying a Resource
  • Universal Resource Identifiers in WWW
  • Example http://www.nih.gov/

http://www.ncbi.nlm.nih.gov/gap

http://www.ucsd.edu

http://www.semanticweb/JohnSmith

what is xml about
What is XML about?
  • XML= eXtensibleMarkup Language by the W3C (World Wide Web Consortium)
  • Transport and Store Data (Structured Knowledge)
  • Key to XML is Document Type Definitions (DTDs)
    • Defines the role of each element of text in a formal model
  • Compound Documents(Multiple files)
xml example
XML Example

Year: 2002

Asset report

Name: U. Of X

Assets

Patents

Equipment

Other assets

Dept

Funds

Patent

news

Expenses

Name:

BioInformatics

Contracts

ID

Author

title

Grants

xml file example
XML File Example

Alice Brown

University of X

CS

BioInformatics

John James

University of X

BioInformatics

Senior

technologies layered architecture2
Technologies- Layered Architecture

Tim Berners Lee Architecture

Logic, Proof and Trust

TRUST

P

R

I

V

A

C

Y

Rules/Query

Other

Services

RDF/OWL Ontologies

XML/XML Schemas

URI/UNICODE

slide14
RDF
  • RDF = Resource Description Framework
  • Adds semantics with the use of ontologies, XML syntax
  • RDF Concepts
    • Basic Model
      • Resources, Properties and Statements
    • Container Model
      • Bag, Sequence and Alternative
slide15
RDF
  • RDF/RDFS Elements
    • Class (School, Department, Person)
      • Rdfs:SubClassOf
    • Properties (Works)
      • Rdfs:SubPropertiesOf
    • Domain and Range of Property
      • Rdfs: domain (School)
      • Rdfs: range (Person)

Works

School

Person

SubClass

Department

rdf vs xml views
RDF vs. XML Views

An iPhone is a Product that has a price of $200″

iPhone

$200

$200

XML Views

iPhone

200

OntTeaching:product1 rdf:type OntTeaching:Product OntTeaching:product1 OntTeaching:title “iPhone” OntTeaching:product1 price “200″

RDF View

owl web ontology language
OWL Web Ontology Language
  • OWL: Semantic Markup Language for Publishing/Sharing Ontologies
      • Enumeration on Classes

owl web ontology language1
OWL Web Ontology Language
  • OWL
    • Value Constraints
      • owl:allValuesFrom
      • owl:someValuesFrom
      • owl:hasValue

owl web ontology language2
OWL Web Ontology Language
  • OWL: Cardinality constraints
      • owl:maxCardinality
      • owl:minCardinality
      • owl:cardinality

2

owl web ontology language3
OWL Web Ontology Language
  • OWL: Intersection, union and complement
  • owl:intersectionOf
  • owl:unionOf
  • Owl:complementOf

Not Meat

owl web ontology language4
OWL Web Ontology Language
  • OWL: Equivalent Class, Disjoint Class

how to build owl rdf files
How to Build OWL/RDF files?
  • Do we need to remember all the OWL language syntax?
  • How to do it easy to use and remember?
content2
Content
  • Why Ontologies?
      • Machine Process able Knowledge
      • Knowledge Exchange
      • Big Data
  • Relevant Technologies
      • Layered Architecture
      • Building Tools and Visualization
  • Ontology Application
      • Information Integration
      • Web Database Management
      • Web Services
how to build rdf owl files
How to Build RDF/OWL files?
  • Different Building and Visualization Tools
    • Protégé, http://protege.stanford.edu/
    • Gruff, http://www.franz.com/agraph/gruff/

(Download version 3.3)

  • Using Programming Languages
    • Java and Jena API
    • http://jena.apache.org/
    • http://jena.sourceforge.net/tutorial/RDF_API/
prot g tool open source ontology editor1
Protégé Tool- Open Source Ontology Editor
  • Property (Object/Data properties)
gruff tool a grapher based triple store browser for allegrograph
Gruff Tool- A Grapher-Based Triple-Store Browser for AllegroGraph

What is triple Store?

Product1

200

type

Product

price

iPhone

title

iPhone

200

Subject Predicate Object

OntTeaching:product1 rdf:type OntTeaching:Product OntTeaching:product1 OntTeaching:title “iPhone” OntTeaching:product1 price “200″

gruff tool a grapher based triple store browser for allegrograph1
Gruff Tool- A Grapher-Based Triple-Store Browser for AllegroGraph

Create a New Triple Store

Choose a Path for the Ontology

Load Ontology

Present the Ontology Triples

Query the Triples

programming with ontologies java jena api
Programming with OntologiesJava + Jena API
  • Collection of Tools and Java Libraries
  • For Developing Linked-data Apps, Tools and Servers
  • Store Information in RDF Triples in Directed Graphs
  • An Ontology API for Handling OWL and RDFS Ontologies
  • ARule-based Inference Engine for Reasoning with RDF and OWL data sources
  • Efficient Storage of Triples on Disk
  • Aquery engine compliant with the latest SPARQL
ontology building using jena
Ontology Building using Jena

Ontology: iPhone.owl

Product1

200

price

type

Product

iPhone

title

Subject Predicate Object

Triples

http://www.semanticweb.org/ontologies/2012/9/OntTeaching.owl#Product1 http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.semanticweb.org/ontologies/2012/9/OntTeaching.owl#Product

Product1 http://www.semanticweb.org/ontologies/2012/9/OntTeaching.owl#title “iPhone”

Product1 http://www.semanticweb.org/ontologies/iPhone.Owl#price “200”

ontology building using jena1
Ontology Building using Jena

The code to create this graph, or model, is simple:

// some definitions

  • static String productURI= "http://www.semanticweb.org/ontologies/Product";

// create an empty Model

  • Model model = ModelFactory.createDefaultModel();

// create the resource

  • Resource product= model.createResource(productURI);

// add the property

  • product.addProperty(title, ”iPhone”);
  • product.addProperty(price, ”200”);
slide36
Jena
  • How to read Ontology?

// list the statements in the Model

StmtIteratoriter = model.listStatements();

// print out the predicate, subject and object of each statement

while (iter.hasNext()) {

Statement stmt = iter.nextStatement(); // get next statement

Resource subject = stmt.getSubject(); // get the subject

Property predicate = stmt.getPredicate(); // get the predicate

RDFNode object = stmt.getObject(); // get the object

System.out.print(subject.toString());

System.out.print(" " + predicate.toString() + " ");

if (object instanceof Resource) {

System.out.print(object.toString());

} else {

// object is a literal

System.out.print(" \"" + object.toString() + "\"");

}

System.out.println(" .");

}

sparql query
SPARQL Query
  • Query on Triples with Exact Pattern Matching (Subject of query is Product1)

SELECT ?b ?c Where {

?b ?c

}

  • Result
content3
Content
  • Why Ontologies?
      • Machine Process able Knowledge
      • Knowledge Exchange
      • Big Data
  • Relevant Technologies
      • Layered Architecture
      • Building Tools and Visualization
  • Ontology Application
      • Information Integration
      • Web Database Management
      • Web Services
ontology applications
Ontology Applications

The database of Genotypes and Phenotypes (dbGaP) is archiving

the results of different Genome Wide Association Studies (GWAS).

  • Phenotype variables are not harmonized across studies.
  • Redundent phenotype identifiers for the same phenotype.
  • dbGaP lacks semantic relations among its variables.
  • Search on phenotypes is inefficient and inaccurate .
  • Goal is to standardize dbGaP information to allow accurate, reusable and quick retrieval of information
ontology applications1
Ontology Applications
  • Several Available dbGAP Studies

phs000284.v1.pht001901.v1.CFS_CARe_Sample.data_dict_2011_02_07

id=”phv00122015”,

Description=”Age at time of Study”,

name=”age”,

version=“1”, Logical Max=”65”, Logical Minimum=”18”, unit=”Years”, type=”decimal”

id=”phv00122058”,

Description=”Age of patient at the time of Study”, name=”age”,

version=“1”, Logical Max=”90”, Logical Minimum=”20”, unit=”Years”, type=”decimal”

phs000284.v1.pht001903.v1.CFS_CARe_ECG.data_dict_2011_02_07

ontology applications2
Ontology Applications
  • Building Information Model (Ontology)

Individual

id=”phv00122058”

Age

id=”phv00122015”

ontology applications3
Ontology Applications
  • Information Retrieval and Ranking Phenotypes

Query={Age of Subject}

conclusion
Conclusion
  • Benefits of Structured Data (XML, OWL)
  • Tools to Create and Visualize Ontologies
  • Jena API for Building Ontologies
  • Sparql Queries on Ontologies
  • Applications uses Ontologies
contacts
Contacts
  • NedaAlipanah
  • Division of Biomedical Informatics9500 Gilman Dr., Bldg 2 #0203E
  • Email: [email protected]
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