Knowledge Access Semantic technology for KM

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Knowledge AccessSemantic technology for KM
ACAI 05 SEKT SUMMER SCHOOL ON KNOWLEDGE
TECHNOLOGY

• John Davies
• BT Research
• john.nj.davies_at_bt.com

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Overview

• Introduction to the Semantic Web
• Language stack
• Semantic Search and Browse
• Knowledge Sharing
• Natural Language Generation Summarisation
• Knowledge Delivery via Device Independence
• Quiz!

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Limitations of the Web today

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

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The Semantic Web

• allowing information to be shared and processed
• adding context and structure 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

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Semantic Web
The Semantic Web is an extension of the current
web in which information is given well-defined
meaning, better enabling computers and people to
work in co-operation. Berners-Lee et al.,
2001
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... Semantic Web HISTORY
10.2.2004 Resource Description Framework
(RDF) Web Ontology Language (OWL) become W3C
recommendations
Source http//www.zakon.org/robert/internet/tim
eline/
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Semantic Web Layers
Entailment of the Implicit
Explicit Semantics
Relational Distributed Data
Data Exchange
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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
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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

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Motivation Knowledge Management

• Knowledge workers are overwhelmed with
  information
• from intranets, emails, external newslines
• but may still lack the information required
• They need information identified
• by semantics, not just keywords
• by their interests and their task context
• in a form appropriate to their current physical
  context
• mobile phone, PDA, blackberry, laptop,

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Knowledge access

• context-aware tools for access to
  semantically-annotated knowledge
• search, browse, share, summarise
• integrated into day-to-day business processes
• automatic knowledge delivery based on current
  context
• activity, location, device, interests
• support multiple end-user devices

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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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QuizXML

• Standard search engine
• WWW pages indexed
• maps keywords to WWW pages
• QuizXML
• A finer-grained index
• maps keywords to documents and the XML tags in
  which they occur

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• QuizXML demo

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XML is a first step

• Metadata (with limitations)
• within documents, not across documents
• prescriptive, not descriptive
• No commitment on vocabulary and modelling
  primitives (subclass, instance, etc)
• ltvehiclegt
• ltcargtford
• ltenginegtxyz123-4lt/enginegt
• ltmodelgtmondeogtlt/mondeogt
• lt/cargt
• lt/vehiclegt
• RDF and ontologies are the next step

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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
• Concept map
• 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

• Typically 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 those
  animals who eat only 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

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Why develop an ontology?

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

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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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Ontology and Logic

• Reasoning over ontologies
• Inferencing capabilities
• X is author of Y ? Y is written by X
• X co-wrote D Y co-wrote D ?
• X and Y collaborate
• Cars are a kind of vehicle
• Vehicles have 2 or more wheels ?
• Cars have 2 or more wheels

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RDF and RDF-S

• W3C standards
• RDF-S defines the ontology
• classes and their properties and relationships
• There are books and authors. 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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QuizRDF

• Searching RDF-annotated web resources

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RDF metadata annotations
Data (WWW document)
Annotation (metadata)
Lost information

• Subjective
• One of several interpretations
• Not exhaustive

RDF
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RDF as an Enrichment
Text
Annotation
RDF
Text
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Precision and recall - the IR dilemma

• Trade-off between precision and recall
• recall - how many of relevant were found
• precision - how many of found were relevant
• Holy grail high precision high recall
• QuizRDF offers both
• separately
• closely-coupled

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Indexing data model
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Multidimensional Indexing

• Traditional search engine indexing
• term ? documents
• employee ? URI1, URI3, URI9
• miller ? URI3, URI7
• QuizRDF indexing
• ltliteral,class,propertygt ? URIs
• ltgeorge, Employee, first_namegt ? URI2
• ltmiller, Employee, last_namegt ? URI1, URI3
• ltmiller, Employee, ?gt ? URI1, URI3, URI7

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QuizRDF demo
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Two Retrieval Channels
Browser interface
Keyword query
RQL

• Precise
• Machine readable
• Subjective
• Incomplete
• Higher precision

• Original content
• Complete
• Imprecise
• Higher recall

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Contribution

• Combination of
• User familiar keyword search
• More precise RDF querying
• Data and metadata as complementary
• Low threshold, high ceiling
• Works on non-RDF information
• Exploits RDF where it exists
• Integrates browsing and querying
• Fits users info seeking behavior

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Conclusions about RDF(S)

• Next step up from plain XML
• (small) ontological commitment to modeling
  primitives
• possible to define domain vocabulary
• limited reasoning
• subsumption, but no transitivity, symmetry,
• limited expressive power
• no cardinality constraints, equality,
  disjointness,

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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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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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QuizOWL!
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Re-cap

• XML, RDF, OWL language stack
• Increasingly sophisticated search
• QuizXML
• subdocument searching
• QuizRDF
• browsing by concept and across relations
• searching on metadata and full-text
• Next steps in semantic search
• identification of named entities within documents
• Exploitation of world knowledge
• KIM (Ontotext)

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The KIM Platform

• A platform offering services and infrastructure
  for
• (semi-) automatic semantic annotation
• ontology population
• semantic indexing and retrieval of content
• query and navigation
• Based on an Information Extraction technology
• Aim to underpin Semantic Web applications
• by providing a metadata generation technology
• in a standard, consistent, and scalable framework

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Ontologies
http//proton.semanticweb.org/

• PROTON - a light-weight upper-level ontology
• 250 NE classes
• 100 relations and attributes
• covers mostly NE classes, and to a smaller degree
  general concepts

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Ontologies II
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KIM World KB

• Aims to cover the most popular entities in the
  world
• Entities of general importance like the ones
  that appear in the news
• KIM knows about
• Organizations, all important sorts of business,
  international, political, government, sport,
  academic
• Specific people, (e.g. Politicians)
• Locations countries, regions, cities, roads,
  etc.

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KIM World KB Content

• Collected from various sources, like geographical
  and business intelligence gazetteers.
• KIM also learns from documents indexed
• via GATE information extraction
• KB scale
• RDF Statements Small KB Full KB
• - explicit 444,086 2,248,576
• - after inference 1,014,409 5,200,017

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KIM Scaling on Data

• The Semantic Repository is based on Sesame/OWLIM.
• Our practical tests demonstrate a perfect
  performance on top of
• 1.2M entity descriptions
• about 15M explicit statements
• above 30M statements after forward chaining.
• Fulltext indexing with Lucene
• .5M docs, retrieval in milliseconds

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Semantic Annotation
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Simple Usage Highlight, Hyperlink, and
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Simple Usage Explore and Navigate
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People search for People

• A recent large-scale human interaction study on a
  personal content IR system, carried out by
  Microsoft demonstrated that
• The most common query types in our logs were
  People/places/things, Computers/internet and
  Health/science. In the People/places thing
  category, names were especially prevalent. Their
  importance is highlighted by the fact that 25 of
  the queries involved peoples names ... . In
  contrast, general informational queries are less
  prevalent.

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Semantic Queries

• The standard IR query is
• give me documents that contain the words
  company, Europe, telecommunication
• KIM provides indexing retrieval wrt NEs
• More precise specification and satisfaction of
  information needs
• specify the NEs we are interested in, and to
  restrict them by their attributes and relations
• Give me documents that mention a company in
  Europe from the telecommunications industry
  sector

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Precision in Semantic Search

• KIM can match
• a query Documents concerning a telecom company
  in Europe, John Smith, and a date in the first
  half of 2002.
• With a document containing At its meeting on
  the 10th of May, the board of Vodafone appointed
  John G. Smith as CTO"
• Classical IR cannot do the required reasoning
• Vodafone is a mobile operator, which is a kind of
  telecom company
• Vodafone is in the UK, which is a part of Europe.
• 5th of May is a "date in first half of 2002
• John G. Smith matches John Smith.

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Entity Pattern Search
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Pattern Search Entity Results
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Entity Pattern Search KIM Explorer
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Predefined Pattern Search
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Pattern Search Multiple-Entity Results
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Pattern Search, Referring Documents
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Document Details
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KIM - summary

• KIM is a platform for
• semantic annotation,
• ontology population,
• semantic indexing and retrieval,
• providing an API for remote access and
  integration,
• based on Information Extraction (IE) using mature
  HLT (GATE).
• powered by massive world knowledge
• http//www.ontotext.com/kim

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SEKTAgent

• Periodic agent search for named entities
• e.g. a person in an organisation
• Returns relevant documents and metadata
• Proactive knowledge delivery
• Linked to device indepedence module (see later)
• Based upon KIM architecture
• Result-led indexing
• Adds relevant pages to next crawl list

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SEKTAgent demo
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TAP

• Uses Google for traditional search
• Augments results with relevant data aggregated
  from distributed (and semantically annotated)
  data
• Offers distributed query interface

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TAP
tap.stanford.edu for more information
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Swoogle

• Searching for semantic web documents and
  ontologies
• See swoogle.umbc.edu

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Google vs. Swoogle

• How to find a popular ontology that defines the
  concept of person?
• Ask Google?
• Type Person filetyperdf
• Type Person filetypeowl
• More complicated query person rdfsClass
  filetyperdf
• Ask Swoogle?
• Type person in document search
• 1 http//xmlns.com/foaf/0.1/index.rdf

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Find Time Ontology
We can use a set of keywords to search ontology.
For example, time, before, after are basic
concepts for a Time ontology.
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Beyond search, beyond documents

• a long list of documents is rarely the ultimate
  information need of the end user
• theres too much relevant information!
• support for the next step - the analysis of the
  returned information
• e.g. key points on a topic from a large document
  you dont want to read
• e.g. creation of a digest of information from
  multiple documents about Bushs statements on a
  given topic

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Search Engine trends
markets

• Seamless and integrated
• one search engine for Web and desktop
• implicit queries based on user activity
• Personalisation
• based on user interaction
• Beyond document lists
• sub-document analysis
• Taxonomies and classification
• taxonomy / enterprise search growing at 10 p.a.
• Ontologies and semantic annotation
• A coherent approach to all these issues

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Knowledge Sharing

• Sharing knowledge through an organisation
• learning from success and failures of others
• avoiding duplication of effort
• (Virtual) communities of practice
• Groups with shared interests who will benefit
  from collaboration and sharing knowledge
• (Using WWW technology to increase collaborative
  radius)

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Communities the Semantic Web

• Communities require a shared conceptual
  vocabulary
• Consensual, evolving concept map
• Ontologies!
• OntoShare
• automates sharing of knowledge in an
  organisation via community-based RDF(S) ontologies

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OntoShare

• Sharing and Classifying resources according to an
  Ontology
• Informs users when relevant document added to
  store
• Ontology-based personalisation
• Provides knowledge store for browsing and
  searching

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OntoShare Sharing knowledge

• User shares knowledge
• WWW document
• Any textual data
• Can supply annotation

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OntoShare Sharing knowledge

• System automatically extracts keywords summary
• System assigns knowledge to concepts

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OntoShare Sharing knowledge

• System emails an alert to selected users based on
  match to user profile

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OntoShare Evolving Ontologies

• OntoShare automatically suggests changes to
  concept characterisation
• Concept characterisations evolve over time

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OntoShare Evolving Ontologies

• User can suggest new concepts for ontology at any
  time
• System emails community on suggestion (à la
  Usenet) and counts votes

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Finding People Collaboration

• Use of personal profiles
• Who else is interested in this document?
• Who else is interested in this topic?
• Encouraging exchange of tacit knowledge
• Discussion threads around shared knowledge
• Adding value to the knowledge stored

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SWAP Semantic Web and Peer-to-Peer

• Distributed Knowledge Management
• Different participants with different
  conceptualizations of their domain
• Different knowledge sources
• Physically distributed, dynamic environment
• Peer-To-Peer Approach
• Decentralized nature Local control
• Symmetry Everyone is provider and consumer
• P2P networks as a reflection of social networks
• Flexible collaboration beyond hierarchical
  structures

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Case Study The Bibster System

• Scenario Sharing of bibliographic metadata in a
  Peer-to-Peer network
• Bibliographic metadata is created and maintained
  in a decentralized manner,
• Researchers are willing to share their data
• Use of semantics is crucial in this setting
• The Bibster system allows users to
• Easily share bibliographic data
• Save work in finding this data
• Avoid re-typing this data by hand

84
Semantic Methods in Bibster

• Semantic representation and querying of metadata
• Extraction and classification from e.g. BibTeX
  files
• Semantic Web Research Community Ontology andACM
  Topic hierarchy as light-weight ontologies
• Peer selection using semantic topologies
• Scalability requires intelligent query routing
• Semantic descriptions of peers expertise as
  basis for peer selection
• Semantic duplicate detection
• Highly redundant and inconsistent representation
  of bibliographic metadata
• Semantic similarity measures to detect duplicates

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Bibster Screenshot
Open Source http//bibster.sourceforge.net/
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NLG - Summarisation

• NLG takes as input structured data in a knowledge
  base or ontology and produces natural language
  text
• Applied to provide automatic documentation of
  ontologies or generate textual reports from
  formal knowledge
• Keeps texts constantly up-to-date so they reflect
  changes in the ontology
• OntoSum, University of Sheffield

87
The Property Hierarchy

• Special linguistically-motivated properties
  introduced to make the NLG modules more generic
• active-action (e.g. works-for)
• passive-action (e.g., published-by)
• Attribute (e.g. has-age, has-web-address)
• part-whole (e.g., consists-of)
• All properties from the ontology were made
  sub-properties of one of these 4
• Attribute properties recognised using heuristics,
  such as property name starts with has
  (hasWebPage)

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Summary Structuring

• Capture regular patterns can be applied
  recursively
• Describe-Instance -gt Describe-Attributes, Descri
  be-Part-Whole, Describe-Active-Actions, Describe
  -Passive-Actions
• Describe-Attributes -gt
• attribute(Instance, Attribute),
• Describe-Attributes
• Collect all subproperties of Attribute property
  relating to Instance
• Attribute(John, hasMobileNumber)..

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Ontology-Based Aggregation

• Joining attribute and part-whole properties with
  the same first argument to have more coherent
  sentences
• ATTR(Researcher XXX, Appellation
  Dr)ATTR(Researcher XXX, string
  my_email_at_sheff)ATTR(Researcher XXX, string
  012344567)ATTR(Researcher XXX, string
  www.mypage.ac.uk)
• Without aggregationKalina Bontcheva has a Dr
  appellation. Kalina Bontcheva has email
  my_email_at_shef.com. Kalina Bon
• With aggregationKalina Bontcheva has a Dr
  appellation, email my_email_at_shef.com and

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Lexicalisation of Classes Properties

• 3 options
• Specified by ontology engineer
• Same as concept/property name
• Added manually when parameterising OntoSum

91
Description of HSBC
Financial Institution
Person
Organisation
Bank
lendsTo
lendsTo
HSBC
employees
market-cap
43bn
137000
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Description of HSBC
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Innovative aspects

• Can tailor summary to device profile
• Apply length restriction
• e.g. for text message for mobile phone
• Generate HTML for web browser or plain text for
  email
• See device independence (next!)
• Readability heuristics
• introduce lists when verbalising more than 3
  attributes
• Use of ontology mapping rules to run same system
  on multiple ontologies

94
Related work

• Wilcock (Helsinki)
• Fully automatic, no lexicon
• Talking OWLs, ISWC-03
• MIAKT
• Some manual input
• More effort, more fluency
• OntoSum based on MIAKT
• Bontcheva, NLDB04

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OntoSum demonstration
96
Device Independence

• context-aware tools for access to
  semantically-annotated knowledge
• search, browse, share, summarise
• integrated into day-to-day business processes
• automatic knowledge delivery based on current
  context
• activity, location, device, interests
• support multiple end-user devices

97
Device independence

• 3 approaches
• Hand-craft different sites for different devices
• Labour intensive, difficult to maintain
• Extend HTML to describe interaction, navigation
  and selection
• Server software generates output in suitable
  format using CC/PP
• Inflexible difficult to control output
  precisely
• No support for large volume sites
• Unclear what extensions are necessary and
  sufficient
• SEKT approach
• Use templates to format data content appropriate
  for each class of device
• Fine control of output based on CC/PP profiles
• can handle large volumes of structured data -
  XML databases
• device-dependencies coded in the templates, e.g.
  mouse capability

98
Device Profiles in RDF

• CC/PP - W3C RDF standard for describing device
  characteristics
• CC/PP vocabularies define device components and
  component attributes
• UAProf is an application of CC/PP adopted by many
  terminal device manufacturers
• An ontology of devices inheritance and
  specialisation
• Profile references and Profile Diffs are sent
  with an information request
• javax.ccpp package for processing profiles

99
User Profiles

• Effective presentation must take user preferences
  accessibility issues into account
• Font size
• Colour preference
• Hi res/Lo res
• Device characteristics and preference/
  accessibility requirements need to be combined
• Effective screen size depends on both physical
  size and user preferences (e.g. font size)
• Specialisation/extension of UAProf

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Profile Engine

• The Profile engine combines device and user
  profiles to generate a set of conditions
• The engine can be queried by other applications
• PROLOG is being used as a prototyping language
• Arithmetic calculations of effective screen size
  (for example) require more than RDF/OWL
• DL (DIG) interface to SWI-Prolog

101
Content Adaptation

• The content adaptation engine uses conditions
  generated by profile engine queries
• Example conditions
• Screen size x font size ?
• number of characters of text
• GraphicsSupported?
• Colour or BW
• Device characteristic or
• Accessibility issue

102
Content Generation

• Different content must be generated for different
  devices
• The current context (set of conditions) will be
  made available to SEKT applications
• Natural Language Processing techniques are be
  used to generate or modify information
• Mobile phone 400 character text message
• PC multimedia document
• NLG describing ontology-based knowledge in
  natural language (OntoSum!)

103
Device Independence

• A functional presentation of a resource should be
  available via any suitable device
• Requirements include content selection, layout
  transformation and style selection
• At present, no one language can be interpreted by
  all clients
• It follows that content must be formatted for the
  target device on the server

104
Templates

• Declarative templates are used to format the
  (XML-based) data
• Context (conditions) can be used to select
  templates, and sections within templates
• Template 1 WML
• InputEnabled?
• Template 2 HTML
• GraphicsWanted?
• Separation of data storage, processing and
  display
• W3C working group on device independence
• No standard for templates (yet)

105
Overview
UAProf (RDF(S))
Device Properties
Context
User preferences
Repurposed Information
Raw Information
Profiling engine
Content Adaptation
(syntactic semantic)
106
Device Independence demo
107
Device Independence Summary

• Device and User profiles need to be combined
  using a suitable ontology
• A profile reasoning engine is used to generate
  conditions on the format
• Content can be generated according to the context
  (set of conditions)
• NLP techniques can be used to generate/summarise
  text (semantic)
• Templates are used to transform the results to a
  format suitable for the device at hand (syntactic)

108
Conclusion

• Semantic Web technology can offer enhancements to
  a range of KM tools
• Search, Share, Summarise, Deliver
• Also
• Visualisation
• RDF or OWL statements as a graph
• Integration of heterogeneous information
• Outstanding Issues
• Trade-off between reasoning and scalability
• Where does the metadata come from?
• Only KIM starting to address this point
• See also SEKT project (www.sekt-project.com)
• Who will find the killer app?!
• Plenty of topics still on the research agenda

109
Acknowledgements

• Peter Haase, University of Karlsruhe
• Kalina Bontcheva, University of Sheffield
• Naso Kiryakov, Ontotext
• Ian Horrocks, University of Manchester
• Tim Glover Alistair Duke, BT

110
Thank you questions?

• Heres a few for you
• What are the semantic web layers?
• Name 3 ontologies in widespread use today
• Name 3 semantic search tools
• What RDF ontology is used to characterise devices
• Why use NLG techniques on ontological
  information?
• What are the advantages of RDF over XML? And OWL
  over RDF?
• Names 3 trends in search engine development
• Describe briefly the way(s) in which metadata can
  improve search performance
• WIN A PRIZE!!!!!

John Davies Next Generation Web Research,
BT john.nj.davies_at_bt.com