Next Generation Knowledge Management applying semantic web technology

1
Next Generation Knowledge Managementapplying
semantic web technology

• John Davies
• Manager, Next Generation Web Research

2
Overview

• Introduction to the Semantic Web
• XML, RDF, OWL
• Ontologies
• Semantic Web Knowledge Management
• SEKT project
• Research technology
• Applications
• Exploitation

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Limitations of the Web today

• Machine-to-human, not machine-to-machine

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The Semantic Web

• Tim Berners-Lee
• an extension of the current web in which
  information is given well-defined meaning, better
  enabling computers and people to work in
  cooperation
• An open platform allowing information to be
  shared and processed
• adding context and structure

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Scientific American, May 2001
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Where we are Today the Syntactic Web
Hendler Miller 02
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i.e. the Syntactic Web is

• A place where
• computers do the presentation (easy) and
• people do the linking and interpreting (hard).
• Why not get computers to do more of the hard
  work?

Goble 03
8
Hard Work using the Syntactic Web

• Complex queries involving background knowledge
• Find information about animals that use sonar
  but are not either bats, dolphins or whales
• Locating information in data repositories
• Travel enquiries
• Prices of goods and services
• Results of human genome experiments
• Delegating complex tasks to web agents
• Book me a holiday next weekend somewhere warm,
  not too far away, and where they speak French or
  English

Horrocks 03
9
XML is a first step

• Semantic markup
• HTML ? layout
• use bold font
• Insert an image here
• XML ? content
• this part of the document is the product price
• this document describes a telecommunications
  service

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XML

• ltplaygt
• lttitlegtThe Life and Death of King
  Johnlt/titlegt
• ltDramatis Personaegt
• ltpersonagtThe Earl of PEMBROKElt/personagt
• ltpersonagtThe Earl of ESSEXlt/personagt
• lt/Dramatis Personaegt
• ltStagedirgtSCENE England, the
  Court.lt/Stagedirgt
• ltactgtAct 1
• ltscenegtScene I.
• ltspeechgt
• ltspeakergtJohnlt/speakergt
• ltlinegtNow, Chatillon, what would
  France with us?lt/linegt
• lt/speechgt

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XML is a first step

• Semantic markup
• HTML ? layout
• XML ? content
• Metadata (with limitations)
• within documents, not across documents
• prescriptive, not descriptive
• No commitment on vocabulary and modelling
  primitives
• ltvehiclegt
• ltcargtford
• ltenginegtxyz123-4lt/enginegt
• ltmodelgtmondeogtlt/mondeogt
• lt/cargt
• lt/vehiclegt
• RDF and ontologies are the next step

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XML limitations for semantic markup

• XML per se makes no commitment on
• Domain specific ontological vocabulary
• Which words shall we use to describe a given set
  of concepts?
• Ontological modelling primitives
• How can we combine these concepts, e.g. car is
  a-kind-of (subclass-of) vehicle
• ? requires pre-arranged agreement on vocab and
  primitives

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What are Ontologies?

• Ontologies provide a shared and common
  understanding of a domain (medicine, finance, )
• a shared specification of a conceptualisation
• A simple example - Yahoo
• BusinessEconomy gt Finance gt Banking
• for WWW, defined using RDF(S) OWL

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Taxonomies
Animals
Vertebrates
Invertebrates
..
Insects
Arachnids
Reptiles
Mammals
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Ontology of People and their Roles
Employee
Expert
Analyst
Manager
Programme Mgr
Project Mgr
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Structure of an Ontology

• Ontologies typically have two distinct
  components
• Names for important concepts and relationships in
  the domain
• Elephant is a concept whose members are a kind of
  animal
• Herbivore is a concept whose members are exactly
  those animals who eat only plants or parts of
  plants
• Background knowledge/constraints on the domain
• Adult_Elephants weigh at least 2,000 kg
• No individual can be both a Herbivore and a
  Carnivore

Horrocks 03
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Why develop an ontology?

• To make define web resources more precisely and
  make them more amenable to machine processing
• To make domain assumptions explicit
• Easier to change domain assumptions
• Easier to understand and update legacy data
• To separate domain knowledge from operational
  knowledge
• Re-use domain and operational knowledge
  separately
• A community reference for applications
• To share a consistent understanding of what
  information means

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Types of Ontologies
Guarino, 98
Describe very general concepts like space, time,
event, which are independent of a particular
problem or domain. It seems reasonable to have
unified top-level ontologies for large
communities of users.
Describe the vocabulary related to a generic
domain by specializing the concepts introduced in
the top-level ontology.
Describe the vocabulary related to a generic task
or activity by specializing the top-level
ontologies.
These are the most specific ontologies. Concepts
in application ontologies often correspond to
roles played by domain entities while performing
a certain activity.
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Ontologies - Some Examples

• General purpose ontologies
• The Upper Cyc Ontology, http//www.cyc.com/cyc-2-1
  /index.html
• IEEE Standard Upper Ontology, http//suo.ieee.org/
  
• Domain and application-specific ontologies
• RDF Site Summary RSS, http//groups.yahoo.com/grou
  p/rss-dev/files/schema.rdf
• Dublin Core, http//dublincore.org/
• UMLS, http//www.nlm.nih.gov/research/umls/
• Open Biological Ontologies http//obo.sourceforge
  .net/
• FOAF www.foaf.org
• Ontologies in a wider sense
• Agrovoc, http//www.fao.org/agrovoc/
• UNSPSC, http//eccma.org/unspsc/
• DAML.org library http//www.daml.org/

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RDF and RDF-S

• W3C standards
• RDF-S defines the ontology
• classes and their properties and relationships
• what concepts do we want to reason about and how
  are they related
• there are authors, and authors write books
• RDF defines the instances of these classes and
  their properties
• Mark Twain is an author
• Mark Twain wrote Adventures of Tom Sawyer
• Adventures of Tom Sawyer is a book

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An example RDF Schema
Annotation of WWW resources and semantic links
domain
range
Writer
Book
hasWritten
subClassOf
FamousWriter
type
Schema(RDFS)
Data(RDF)
25/12/68
type
DoB
hasWritten
/twain.com/mark
books.com/ISBN00010475
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RDF
hasName (http//www.famouswriters.org/twain/mark
, Mark Twain) hasWritten (http//www.famousw
riters.org/twain/mark, http//www.books.org/ISB
N00001047582) title (http//www.books.org/ISBN0
0001047582, The Adventures of Tom
Sawyer) XML version ltrdfDescription
rdfabouthttp//www.famouswriters.org/twain/markgt
ltshasNamegtMark Twainlt/shasNamegt ltshasWritten
rdfresourcehttp//www.books.org/ISBN0001047/gt lt
/rdfDescriptiongt
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Conclusions about RDF(S)

• Next step up from plain XML
• (small) ontological commitment to modeling
  primitives
• possible to define vocabulary
• However
• no precisely described meaning
• no inference model

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Ontology and Logic

• Reasoning over ontologies
• Inferencing capabilities
• X is author of Y ? Y is written by X
• X is supplier to Y Y is supplier to Z ?
• X and Z are part of the same supply
  chain
• Cars are a kind of vehicle
• Vehicles have 2 or more wheels ?
• Cars have 2 or more wheels

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Web Ontology Language Requirements

• Desirable features identified for Web Ontology
  Language
• Extends existing Web standards
• Such as XML, RDF, RDFS
• Easy to understand and use
• Should be based on familiar KR idioms
• Formally specified
• Of adequate expressive power
• Possible to provide automated reasoning support

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OWL Language

• OWL is based on Description Logics knowledge
  representation formalism
• OWL (DL) benefits from many years of DL research
• Well defined semantics
• Formal properties well understood (complexity,
  decidability)
• Known reasoning algorithms
• Implemented systems (highly optimised)
• Three species of OWL
• OWL Full maximum expressivity, undeciable
• OWL DL based on SHIQ DL, decidable
• OWL Lite - subset of OWL DL, most efficient
  reasoning

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Semantic Web Layers
Entailment of the Implicit
Explicit Semantics
Relational Distributed Data
Data Exchange
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Why OWL?

• OWL Web Ontology Language
• Owls superior intelligence is known throughout
  the Hundred Acre Wood, as are his talents for
  Writing, Spelling, other Educated and Special
  tasks.
• "My spelling is Wobbly. It's good spelling, but
  it Wobbles, and the letters get in the wrong
  places."

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• Semantic Web Knowledge Management

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Business Motivation Knowledge Management

• Corporate workers are overwhelmed with
  information
• from intranets, emails, external newslines, DMSs,
  
• but may still lack the information they require
• They need information
• filtered by semantics, not just keywords
• tailored to their interests and their task
  context
• in a form appropriate to their current physical
  context
• mobile phone, PDA, blackberry, laptop,
• aggregated from heterogeneous data sources

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SEKT

• addressing the semantic knowledge technology
  research, development exploitation agenda
• developing Next Generation Knowledge Management
  (NGKM)
• 6th framework IP project
• start date 1/1/2004
• 36 months, 12.5m
• www.sekt-project.com

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The inSEKTs
Vrije Universiteit Amsterdam
Siemens BS
Empolis
University of Sheffield
Universität Karlsruhe
BT
Ontoprise
Kea-pro
Universität Innsbruck
iSOCO
Sirma AI
Universitat Autònoma de Barcelona
Jozef Stefan Institute
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Semantic Web KM

• Making WWW information machine processable
• annotation via ontologies metadata
• offers prospect of enhanced knowledge management
• better knowledge access and sharing
• heterogeneous information sources, proactive
  knowledge delivery, seamless knowledge access
• significant research technology challenges are
  outstanding

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SEKT - The Goal

• To deliver next generation semantic knowledge
  technology through
• Foundational research
• (Semi-)automatic ontology generation and
  population
• Human Language Technology Knowledge Discovery
• Ontology management (mediation, evolution,
  inferencing)
• Innovative technology development
• A suite of knowledge access tools
• Open source ontology middleware platform
• Validated by 3 case studies and
  benchmarking/usability activties
• Supported by a methodology

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Major RTD challenges

• Improve automation of ontology and metadata
  generation
• Research and develop techniques for ontology
  management and evolution
• Develop highly-scalable solutions
• Research sound inferencing despite inconsistent
  models
• Develop semantic knowledge access tools
• Develop methodology for deployment

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Key outcomes

• technological progress through development of
  leading edge, integrated semantically-enabled KM
  software tools
• scientific progress through foundational research
• creation of awareness via dissemination, training
• showcases - 3 case study applications
• exploitation via open source, freeware and
  proprietary software

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Key outcomes

• building the European Research Area through
  collaboration with related IP and NoE projects in
  this area for a coordinated impact strategy
• SEKT, DIP, KnowledgeWeb SDK cluster
• http//sdk.semanticweb.org
• European Semantic Web Symposium
• http//www.esws2004.org/
• Conference series established
• ESWC05 Crete, May 2005
• 260 attendees

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Annotation is a key issue

• How do we handle legacy knowledge?
• automating metadata extraction
• using human language technology
• significant research technology challenges are
  outstanding
• creating and managing ontologies is an overhead
• semi-automatic generation of ontologies
• using knowledge discovery
• semi-automatic maintenance and evolution of
  ontologies
• plus ontology merging and mapping
• needs a multi-disciplinary approach

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Multidisciplinary approach
KD/HLT
Management evolution
KD/HLT

• Need to determine appropriate technology mix
• Semi-automatic

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The semantic desktop

• context-aware tools for access to
  semantically-annotated knowledge tools
• search, browse, visualise, summarise, share,
  infer
• integrated into day-to-day business processes
• automatic knowledge delivery based on current
  context
• activity, location, device, interests
• support multiple end-user devices (RDF-based)
• also support for on-the-fly metadata creation
• metadata creation as a side-effect of data
  creation

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Semantic Annotation
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Semantic Browsing
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Semantic Browsing
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Semantic Search
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Semantic Search Results
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Semantic Search, Referring Documents
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Semantic Search, Referring Documents
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Real-life applications
helping newly-appointed judges
helping IT consultants
a corporate digital library

• Use/refinement of SEKT methodology
• Usability, business benefits and benchmarking

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Exploitation

• Exploitation key roles
• Systems integrator
• Several software vendors
• Sector-specific organisations
• Key outputs
• integrated suite of software components
• open source/freeware environment for semantic
  knowledge applications
• SEKT brand

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Exploitation - Target markets

• Direct exploitation e.g. enterprise search
• growing at 10 p.a.- driven by taxonomies
• Horizontal integration
• iComms - integrated communications
• portals content management
• CRM, eLearning, helpdesk, sales support
• Vertical markets
• tailoring functionality and ontologies
• legal, life sciences, consultancy

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Project Overview
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Summary

• Semantic Web
• machine-processable web-based data
• making the computer a device for computation
  again!
• Application of semantic web to Knowledge
  Management
• Research challenges remain
• Starting to deploy real applications

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Acknowledgements

• York Sure, University of Karlsruhe
• Sean Bechhofer, University of Manchester

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Thank youwww.sekt-project.comjohn.nj.davies_at_bt.c
om