ontologies and semantic web

Ontologies and Semantic Web

STANLEY WANG SOLUTION ARCHITECT, TECH LEAD @SWANG68 http://www.linkedin.com/in/stanley-wang-a2b143b

Ontologies and Semantic Web

• In general, an ontology describes formally a domain of discourse and consists of a finite list of terms and the relationships between the terms;

• The terms denote important concepts, classes of objects of the domain, e.g. in a University Model, staff members, students, courses, modules, lecture theatres, and schools are some important concepts;

In the context of the Web, ontologies provide a shared understanding of a domain,

which is necessary to overcome the difference in terminology.

Ontology Engineering

Ontological Vision of Semantic Web

• An ontology is document or file that formally and in a standardized way defines the hierarchy of classes within the domain, semantic relations among terms and inference rules;

• Sharing semantics of your data across complex distributed applications: Gene Ontology, Glycomics, Pharmaceutical Drug, Treatment-Diagnosis, Repertoire Management, Equity Markets, Anti-Money Laundering, Suspicious Activity Monitoring, OFAC, Financial Risk, Terrorism, Customer Profile, etc;

Ontology model can be Public, Government, Limited Availability, Commercial.

Formal, explicit specification of a shared conceptualization

Machine readable

Concepts, properties, functions, axioms are explicitly defined

Consensual knowledge

Abstract model of some phenomena

in the world

What is an ontology?

What is an Ontology?

A model of (some aspect of) the world

• Introduces vocabulary

relevant to domain, e.g.:

o Anatomy

8





o Anatomy

o Cellular biology

9





o Anatomy

o Cellular biology

o Aerospace





o Anatomy

o Cellular biology

o Aerospace

o Dogs





o Anatomy

o Cellular biology

o Aerospace

o Dogs

o Hotdogs

o …




relevant to domain

• Specifies meaning of terms

Heart is a muscular organ that

is part of the circulatory system




relevant to domain

• Specifies meaning of terms

Heart is a muscular organ that

is part of the circulatory system

• Formalised using suitable logic

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PhD Student AssProf

AcademicStaff

rdfs:subClassOf rdfs:subClassOf

cooperate_with

rdfs:range rdfs:domain

<swrc:AssProf rdf:ID="sst">

<swrc:name>Steffen Staab

</swrc:name>

...

</swrc:AssProf>

http://www.aifb.uni-karlsruhe.de/WBS/sst

Anno-

tation

<swrc:PhD_Student rdf:ID="sha">

<swrc:name>Siegfried

Handschuh</swrc:name>

...

</swrc:PhD_Student>

Web

Page

http://www.aifb.uni-karlsruhe.de/WBS/sha URL

<swrc:cooperate_with rdf:resource =

"http://www.aifb.uni-

karlsruhe.de/WBS/sst#sst"/>

instance of instance

of

Cooperate_with

Ontology and Annotation

Links have explicit meanings!

Ontology Model Example - Agency Partnership

These ontologies accessed

at remote locations

Ontology Model Example: Terrorist Organization

Ontology

Personalization:

is mechanism, which

allows users to have

own conceptual view

and be able to use it for

semantic querying of

search facilities.

“Driver”

“Driver”

“Driver”

“Driver” “Driver”

Common ontology

Search

Ontology Model Example: Customer Profile

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Ontology F-Logic

similar

Ontology F-Logic

similar

PhD Student Doktoral Student

Object

Person Topic Document

Tel

PhD Student PhD Student

Semantics

knows described_in

writes

Affiliation described_in is_about

knows P writes D is_about T P T

D T T D

Rules

subTopicOf

Researcher Student

instance_of

is_a

is_a

is_a

Affiliation

Affiliation

Siggi

AIFB +49 721 608 6554

Ontology Model Example: University Research

A Typical Enterprise Semantic Application Lifecycle

Build Ontology • Build Schema(model level representation)

• Populate with Knowledgebase (people, location, organizations, events)

Automatic Semantic Annotation (Extract Semantic Metadata)

• Any type of document, multiple sources of documents

• Metadata can be stored with or sparely from documents

Applications: semantic search (ranked list of documents), portal integration, summarize & explain, analyze, make decisions;

• Reasoning Techniques: Graph Analysis, Logic Inference

Ontology

Semantic Query

Server

1. Ontology Model Creation (Description) 2. Knowledge Agent Creation

3. Automatic aggregation of Knowledge 4. Querying the Ontology

Ontology Creation and Maintenance

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Ontology Editors and Environments

• Protégé, SWOOP, GrOWL, TopBraid, Ontotrack, SemanticWorks, ..

JENA • Jena is a Java framework for building Semantic Web

applications. It provides a programmatic environment for RDF, RDFS and OWL, including a rule-based inference engine.

• Jena is open source and grown out of work with the HP Labs Semantic Web Program.

• The Jena Framework includes:

• A RDF API

• Reading and writing RDF in RDF/XML, N3 and N-Triples

• An OWL API

• In-memory and persistent storage

• RDQL – a query language for RDF

Jena is one of the most widely used Java APIs for RDF and OWL, providing services for model representation, parsing, database persistence, querying and some visualization tools. Protege-OWL always had a close relationship with Jena. The Jena ARP parser is still used in the Protege-OWL parser, and various other services such as species validation and datatype handling have been reused from Jena. It was furthermore possible to convert a Protege OWLModel into a Jena OntModel, to get a static snapshot of the model at run time. This model, however had to be rebuild after each change in the model.

As of August 2005, Protege-OWL is now much closer integrated with Jena. This integration allows programmers to user certain Jena functions at run-time, without having to go through the slow rebuild process each time. The architecture of this integration is illustrated on the next slide…

Jena Integration of Protégé-OWL

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Jena Integration of Protégé-OWL

The OWLModel API has a new method getJenaModel() to access a Jena view of the Protege model at run-time. This can be used by Protege plugin developers. Many other Jena services can be wrapped into Protege plugins this way, by providing them a pointer to the Model created by Protege.

The key to this integration is the fact that both systems operate on a low-level "triple" representation of the model. Protege has its native frame store mechanism, which has been wrapped in Protege-OWL with the TripleStore classes. In the Jena world, the corresponding interfaces are called Graph and Model. The Protege TripleStore has been wrapped into a Jena Graph, so that any read access from the Jena API in fact operates on the Protege triples. In order to modify these triples, the conventional Protege-OWL API must be used. However, this mechanisms allows to use Jena methods for querying while the ontology is edited inside Protege.

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Joseki - a SPARQL Server for Jena

Joseki: The Jena RDF Server. Joseki is a server for publishing RDF models on the web. Models have URLs and they can be access by HTTP GET. Joseki is part of the Jena RDF framework.

Joseki is an HTTP and SOAP engine supports the SPARQL Protocol and the SPARQL RDF Query language. SPARQL is developed by the W3C RDF Data Access Working Group.

Joseki Features:

RDF Data from files and databases

HTTP (GET and POST) implementation of the SPARQL protocol

SOAP implementation of the SPARQL protocol

Real Life Example: Semantic Application in a Global Bank

• Goal Legislation (PATRIOT ACT) requires banks to identify ‘who’ they are

doing business with;

• Problem Volume of internal and external data needed to be accessed Complex name matching and disambiguation criteria Requirement to ‘risk score’ certain attributes of this data

• Approach Creation of a ‘risk ontology’ populated from trusted sources OFAC ; Sophisticated entity disambiguation Semantic querying, Rules specification & processing

• Solution Rapid and accurate KYC checks Risk scoring of relationships allowing for prioritisation of results; Full visibility of sources and trustworthiness

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Watch List Organization

Company

Hamas

WorldCom

FBI Watch List

Ahmed Yaseer

appears on Watchlist

member of organization

works for Company

Ahmed Yaseer:

• Appears on

Watchlist ‘FBI’

• Works for Company

‘WorldCom’

• Member of

organization ‘Hamas’

Process from Business Perspective

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World Wide Web content

Public Records

BLOGS, RSS

Un-structure text, Semi-structured Data

Watch Lists Law

Enforcement Regulators

Semi-structured Government Data

Scores the entity based on the content and entity relationships

Establishing New Account

Fraud Prevention Application using Semantics

User will be able to navigate the ontology using a number

of different interfaces

Ontology Model

Semantic Technology in Summary

• Semantic Web is not only a technology as many used to name it;

• Semantic Web is not only an environment as many naming it now;

• Semantic Web it is a new context within which one should rethink and re-interpret the existing businesses, resources, services, technologies, processes, environments, products etc. to raise them to totally new level of performance…