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Next Generation Knowledge Management applying semantic web technology

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Title: 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

3
Limitations of the Web today
  • Machine-to-human, not machine-to-machine

4
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

5
Scientific American, May 2001
6
Where we are Today the Syntactic Web
Hendler Miller 02
7
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

10
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

11
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

12
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

13
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

14
Taxonomies
Animals
Vertebrates
Invertebrates
..
Insects
Arachnids
Reptiles
Mammals
15
Ontology of People and their Roles
Employee
Expert
Analyst
Manager
Programme Mgr
Project Mgr
16
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
17
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

18
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.
19
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/

20
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

21
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
22
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
23
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

24
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

25
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

26
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

27
Semantic Web Layers
Entailment of the Implicit
Explicit Semantics
Relational Distributed Data
Data Exchange
28
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."

29
  • Semantic Web Knowledge Management

30
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

31
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

32
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
33
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

34
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

35
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

36
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

37
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

38
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

39
Multidisciplinary approach
KD/HLT
Management evolution
KD/HLT
  • Need to determine appropriate technology mix
  • Semi-automatic

40
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

41
Semantic Annotation
42
Semantic Browsing
43
Semantic Browsing
44
Semantic Search
45
Semantic Search Results
46
Semantic Search, Referring Documents
47
Semantic Search, Referring Documents
48
Real-life applications
helping newly-appointed judges
helping IT consultants
a corporate digital library
  • Use/refinement of SEKT methodology
  • Usability, business benefits and benchmarking

49
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

50
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

51
Project Overview
52
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

53
Acknowledgements
  • York Sure, University of Karlsruhe
  • Sean Bechhofer, University of Manchester

54
Thank youwww.sekt-project.comjohn.nj.davies_at_bt.c
om
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