semantic technology and ontology: down to business

Copyright © 2010 Michael Uschold. All rights reserved.

Semantic Web: Down to Business

Michael Uschold, PhDSemantic Arts

.The majority of this talk is taken from “Semantic Web: Down to Business”,

presented Monday November 15, 2010

Taxonomy Boot Camp– Washington DC

1

http://www.semanticarts.com/

Engineering, Operations & Technology | Phantom Works E&IT | Mathematics and Computing Technology

Page: 2Copyright © 2010 Michael Uschold. All rights reserved.

Objectives

I will understand:

• What is the “Semantic Web”

• Real world applications of Semantic Web Technology

• Where the value comes from

• What I might want to do in my organization.

• That the Semantic Web future is bright



Outline

• Introducing Semantic Web Technology

• Practical Applications

• The Future is Bright

• What can I do?


What is “Semantic Technology”?

Fundamental properties:

1. Data wears its meaning on its sleeve – metadata

2. Meaningful connections between data

3. Computer draws conclusions

Benefits:

1. AGILITY: faster, cheaper, flexible and adaptable

2. INTEGRATED: data connections, integrated applications

3. INTELLIGENT APPLICATIONS: new things are possible

4Copyright © 2010 Michael Uschold. All rights reserved.

I am w

hat I am


Artificial Intelligence and Semantic Technology


Speech recognition

Vision

Robotics

Natural Language Processing

Creativity

Artificial Intelligence

Semantic Technology

PlanningSPARQLTriple Stores

OWL RDF

XML

Unicode

URI

Knowledge

Representation &Reasoning

Machine Learning Intelligent Agents


What do we mean by “semantics”?

• The word “semantics” means: MEANING.

Variations:• Everyday language: “its just semantics” quibbling over words

• Natural language processing: syntax, semantics, pragmatics

• Logic: guaranteed to draw correct conclusions

• Data: meaning in the real world

• Meaning is all about context & relationships


• Meaning for computers, not just people

Bank

pilot

aircraftturn

Bank

account

savingsdeposit



Data Wearing Meaning on its Sleeve

Simple Task:

Find documents about mechanical devices.

The purpose of this review is to

remind operators of the

existence of the Operations

Manual Bulletin 80-1, which

provides

information regarding flight

operations with low fuel

quantities,

and to provide supplementary

information regarding main tank

boost pump low pressure

indications.

747 FUEL PUMP LOW PRESSURE

INDICATIONS

When operating 747 airplanes with

low fuel quantities for short



The purpose of this review is

to remind operators of the

existence of the Operations

Manual Bulletin 80-1, which

provides

and to provide supplementary

information regarding main

tank

boost pump low pressure

indications.747 <concept

id=fuel-pump>FUEL PUMP

</concept> LOW PRESSURE

INDICATIONS

When operating 747 airplanes

with low fuel quantities for

short

Shared Hydraulics Repository (SHR)Pump

a owl:Class ;

rdfs:comment "A mechanical device for

raising, compressing, or transferring

fluids.“; ;

rdfs:subClassOf MechanicalDevice;

rdfs:subClassOf

[ a owl:Restriction ;

owl:hasValue Piston ;

owl:onProperty hasPart

] .

Hey, I know about,

SHR, so now I know

something about

Fuel Pump.

What the heck

is a Fuel Pump?

a owl:class;

rdfs:subClassOf SHR: pump

Semantic Annotation

fuel-pump

<concept id=fuel-pump>FUEL PUMP</concept>

Meaningful Connection and Automated Reasoning



Computers Drawing New Conclusions

Deriving new information from existing information.

BENEFITS & USES:

• Question Answering

• Information Integration

• Filtering

• Reduced need to build custom processing engines

• Guarantees of correctness – consistency checking

• Compact representation, e.g. transitivity• Easier to understand and maintain



What goes on the sleeve?

Comes from a “Semantic Model” for some subject area:

• A way to capture meaning

• Agreed terms, definitions and relationships

Page: 10

Hydraulic System

Fuel System

Pumping

Hydraulic Pump

Aircraft Engine Driven Pump

Pump

Mechanical Device

Engine

Jet EngineFuel Pump

Fuel Filter

has-part

done-by

part-of connected-to

supplies-fuel-to

I am w

hat I am



Different ways to capture meaning...

Examples:

• Data dictionary, Glossary, Controlled Vocabulary

• Thesaurus

• Taxonomy

• Ontology

• Many others



Example: Controlled Vocabulary / Glossary

Pump: “A mechanical device for raising, compressing,

or transferring fluids”

Engine: “a machine that turns energy into

mechanical motion”

Mechanical Device: “a physical device with parts that

move relative to each other”



Example: Taxonomy

Hydraulic Pump


Pump

Mechanical Device

Engine

Jet EngineFuel Pump

= Generalization



Example: Thesaurus

Hydraulic System

Fuel System

Pumping

Hydraulic Pump


Pump

Mechanical Device

Engine

Jet EngineFuel Pump

Fuel Filter

= Broader Term

= Associated Term

+ Synonym &

Homonym



Hydraulic System

Fuel System

Pumping

Hydraulic Pump


Pump

Mechanical Device

Engine

Jet EngineFuel Pump

Fuel Filter

has-part

done-by

part-of connected-to

supplies-fuel-to

Ontology: Strict Taxonomy + Formal Relationships

= Generalization

= Other

Relationships

•Taxonomy with multiple link types,

each with precise meaning,

is usually called an “ontology”.



Approaches for Capturing Meaning

Ctld.

VocabTaxonomy Thesaurus Ontology Data Models Object Models

Definition

Defined

terms,

controlled

Controlled

vocab. in a

hierarchy.

Controlled vocab.

in a network.

specification of

conceptualizat’n

Specification of

DB structure

Specification of

a software

application

domain

Notation

Free text,

Definition

structure

varies.

Strict: tree

Or: multi-

parent

Broader/narrower

(maybe taxonomy)

Gnl. association;

Logics,

Taxonomy as

backbone +

atts. & relations.

e.g. ER

diagrams

Entities &

Relations

Hierarchy of

classes, rel's

attributes &

methods

Meaning

Nrl lang

def's

Dictionary;

common

usage

Nrl lang

def's +

meaning of

link

Strictness &

Precision

varies.

Isa, partOf,

similarTo …

Nrl lang def's +

meaning of links.

B/N: various mng's

Gnl Assoc'n: no

specific meaning

Logics w/ fml.

semantics.

Isa hierarchy;

Dom/Range

constraints;

cardinality.

Nrl. language

comments in

the ontology.

Precise, not

logic-based.

Focus on data,

not meaning

(e.g. toss rel'n

names).

Data dictionary

separate.

Increasingly

formal.

Isa hierarchy,

Aggregation /

Composition,

Dom/Range

constraints;

cardinality.

Purpose

Human

communi-

cation

(HC)

HC +

Structure

info. base;

browsing

HC + Structure

digital libraries;

indexing,

browsing & search

Union of all the

others & more.

HC + Structure

(and validate)

databases.

HC + Structure

software

systems.


So What IS the Semantic Web?

It depends who you ask!

• A Web of data

• A set of W3C standards

• A technology base to be used on or off the Web

• An upgrade to the existing Web

• Witness protection plan for AI

• A new application of AI:

An intelligent machine-readable Web of knowledge!

• A revolution in the way we think of data, crowds, & schema



What the Semantic Web ISN’T

• A silver bullet

• A software package

• Limited to being on the Web

• A replacement for the existing Web

• A mere figment of researcher’s imaginations




Outline

• Introducing Semantic Technology



• What can I do?



Out of the Research Labs: Deployed Systems

• Growing number of Real Success Stories

• Many are hidden:• Behind Corporate Firewalls – for competitive advantage

• Classified government projects – for national security

• Increasingly, they are becoming known.


Example: British Broadcasting Corporation

Situation (in 2007)

• Many handcrafted individual micro sitese.g. news, food, gardening

• All data and content disconnected across sites

Hard to Do:

• Query/explore across related topics

• Find everything on a given topic

• Re-purpose content for new sites

• Leverage evolving data from external sites



BBC: Connecting Multiple Data Sets

Wikipedia

Latest Tracks

Audio Previews

John Denver

FlickR?


BBC: Connecting Multiple Data Sets

IMDB

MySpace

MusicBrainz

Last.fm

Played by

Played on

Reviews


External Data Sets: “Linked Data Cloud”


Solution: Web as Content Management System

Benefits:

1. Usability: Have sites on things that people care about

2. User Experience: Visualize resources in new ways

3. User Journeys: animal… program clip, related habit

4. Reuse Data:

1. One page per thing

2. Leverage external linked open data

3. Others can re-purpose BBC data to create new sites

4. Linkable and discoverable by humans and computers

5. SEO: Highly optimized for search engines



Example BBC: Why did it Work?

Why Did It Work?

• Flexibility: DB-backed Web applications brittle

not able to support changing environment.

• Meaning of Data is clear

• Connectivity: linking across data silos including

external data.


Music Radio TV


Example: Manufacturing Quality Assurance (1/4)

Defective Widgets:

• 1 in a 1000 widgets coming of the line are defective

• All have same defect

Challenge:

• Enormously complex manufacturing process

• Countless possible pathways

• Very time consuming to track down, may not succeed




SOLUTION:

• Build models for various aspects of business• Each machine, components and attributes

• Manufacturing process pathways

• Products

• Capture data during manufacturing process;

based on the models

• Query the system:• What is common among all defective widgets?

• Answer: 99% of defective widgets came off one particular line




OUTCOME:

• System uses data & knowledge to draw conclusions

e.g. identify machines as source of problem

• Go look at machines, notice defective part, replace it.

• Generate a report as well

• Used to take a week, now takes 10 minutes.

• Customer: “We love ontologies.” Continued investment.



Why Did It Work?

• Flexibility: Traditional DB applications brittle,

not able to support changing environment.

• Connectivity: Semantic models are basis for linking across

data silos.

• Drawing conclusions: In complex environment, reduce

information overload.



www.ontoprise.de

© 2007 ontoprise GmbH - 31 -

EXAMPLE: KukaXprt

Kuka: No. 3 largest Robot Manufacturer in World… and growing fast!

Need: disseminate knowledge about robot handling & repair

End User: service engineers sent out for repairs

Goal:

collect knowledge of the experienced service engineers

support new service engineers

www.ontoprise.de


Background

• 65% of all customer in the manufacturing industry change their suppliers because there are not satisfied with the service

• Service engineers spend a lot of time with known problems

Goal

• Capturing and usage of engineers and experts know-how

• Decision support for choosing the right solution

• Increase customer satisfaction

Implementation

• Semantic Customer Service Support

Customer Service Support for Kuka Roboter

www.ontoprise.de


Value proposition & Results Reduce costs:• No more trial and error

• Reduce ‚Time To Fix„ and increase ‚First Time Fix„ • Reduce ‚Spare Part Overtake„

Improve Quality• Guided and quality assured problem solving

Motivation of Service Engineers:• Easier handling compared to paper• Less work

Customer Satisfaction and Competitiveness

“The project was completed successfully, due to the close collaboration with ontoprise and due to highly reliable and high quality of work from ontoprise”

Alwin Berninger:Director Customer SupportKUKA Roboter GmbH

• Find the right solutions faster• More robots working more of the time• Increased customer satisfaction

www.ontoprise.de


How do ontologies and semantics help?

While some of these tasks can sometimes also be accomplished by

conventional technology, ontologies are the superior technology

when it comes to combining these tasks.

They [ontologies] are reuasable knowledge modules that capture the

domain logic as seperate, descriptive assets. They are very flexible

and extendable. They serve as a content backbone to which all the

tasks can refer to.

When using conventional technologies in the Kuka case it would be

much harder to deal with changes in the robots models and to

extend the background knowledge, since a procedural system

would probably require a reimplementation. It would also be much

harder to combine the integration, search and guiding process,

since the ontology as central backbone would be missing.

Wolf Winkler, Ontoprise]

”

“


Why does it work? IT Challenges and Root Causes

35

What does it mean?(ambiguity)

Data Silos Info. Overload & Complexity

Consistency/Reliabilty

Maintenance

Understandability

Interoperability / Integration

Evolution/Agility/Flexibility

Reuse

Cheaper

Hinders

Helps



Outline




• What can I do?



Recent Developments

This Year: • Sept 20: WebMediaBrands buys SemTech Conference

& Site from Semantic Universe

• July 16: Google buys Metaweb (Freebase)

• April 28: Apple purchases SIRI

• April 21: Facebook announces release of social graph

2007-2009• Sept 2009: W3C guidelines for publishing open data

• June 2009: NY Times releases 100 year old thesaurus

• May 2009: Whitehouse unveils Open Gov’t Initiative

• May 2009: Google announces Rich Snippets

• July 2008: Microsoft buys Powerset

• 2007-2009: Linked Data Cloud explodes on the scene


Recent Trends

• Semantic Technology Conf. grows through recession

• Semantic Technology Companies / Consultancies• 2005: a few dozen

• 2010: several hundred

• Bigger companies buying smaller ones

• Patent Applications: • 90s: a handful per year

• Pre-recession: triple digits

• Semantic Web Meetups: • 100% annual growth for 3 yrs



Premiere Business Conference for Semantic Technology

Speakers from 155 different companies for 2010:


• Siemens

• Best Buy

• Google

• Yahoo

• Nokia

• Wells Fargo

• Oracle

• Microsoft

• Boeing

• Merck

• Pfizer

• SAP


Widespread Interest in Semantic Technologies

• Health / Pharmaceutical

Life Sciences

• Enterprise

e.g. Salesforce.com

• Advertising and Marketing

• Retail

e.g. Best Buy, Nokia

• Content Publishing

Digital Libraries

• Finance

• Military Intelligence

• Open Government

• Energy Management




Outline




• What can I do?


What you might want to do…

Create an Agile Semantic Enterprise

1. Invest in training in semantic technology.

2. Develop potential use cases for semantic applications in

your organization. Charter a pilot in coming year.

3. Begin thinking about your data / information architecture

in terms of semantic models and leverage existing

models (“ontologies”) in your industry.

Ref: Cutter IT Journal. Vol. 22, No. 9 September 2009




Building Your Own Semantic Application

• Identify a value proposition

Driven by Business, not IT department!

• What is the role of the semantics technology?• How will semantics help?

• Why is it better than alternative approaches?

• Cost / benefit analysis

• Build proof of concept first...

• ... Then put into production.

semantic technology and ontology: down to business

Business

semantic web future

businessmichael uschold

just semantics

word semantics

data connections

everyday language

new things

fundamental properties