Semantic%20Knowledge%20Management%20Finding%20Information%20Through%20Meaning%20Not%20Words - PowerPoint PPT Presentation

About This Presentation
Title:

Semantic%20Knowledge%20Management%20Finding%20Information%20Through%20Meaning%20Not%20Words

Description:

Semantic Knowledge Management. Finding Information Through Meaning Not Words. Alistair Duke ... 'the grand vision of 'A Semantic Web' will not be achieved, ... – PowerPoint PPT presentation

Number of Views:65
Avg rating:3.0/5.0
Slides: 26
Provided by: Mon699
Learn more at: http://irsg.bcs.org
Category:

less

Transcript and Presenter's Notes

Title: Semantic%20Knowledge%20Management%20Finding%20Information%20Through%20Meaning%20Not%20Words


1
Semantic Knowledge Management Finding
Information Through Meaning Not Words
  • Alistair Duke
  • alistair.duke_at_bt.com
  • Next Generation Web Research

2
The Semantic Web Is Dead
the grand vision of 'A Semantic Web' will not be
achieved, mostly because users cannot be expected
to annotate media with complex labels but can
only be expected to use simple tags
  • Mor Naaman
  • Yahoo! Research Berkeley
  • Panellist at WWW2007, Banff

3
The need for semantics
  • Knowledge workers overwhelmed by info
  • from intranets, emails, newslines
  • but still lack vital information
  • 80 of corporate data is unstructured
  • including key business decisions
  • subject to regulation, e.g. SOX
  • Companies suffer from
  • decisions made under incomplete knowledge
  • threat of compliance failure

4
We need information
  • Identified by semantics, not just keywords
  • precise and complete
  • Selected by their interests task context
  • defined semantically
  • From heterogeneous sources,
  • accessed uniformly
  • Presented meaningfully
  • and appropriately for the user

5
Semantic Information Management In three words
Semantic information management classifies,
finds, distributes, shares and uses information
based on meaning not on the particular words used
to represent meaning.
6
In three words
Semantic information management classifies,
finds, distributes, shares and uses information
based on meaning not on the particular words used
to represent meaning.
7
The SEKT Project
  • Addressing the semantic knowledge technology
    research agenda
  • European 6th framework IP project
  • end date 31/12/2006
  • 36 months duration, 12.5m budget
  • www.sekt-project.com

8
The inSEKTs
Vrije Universiteit Amsterdam
Siemens
Empolis
University of Sheffield
Universität Karlsruhe
BT
Ontoprise
Kea-pro
Universität Innsbruck
iSOCO
Sirma AI
Universitat Autònoma de Barcelona
Jozef Stefan Institute
9
Major research 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

10
Extracting the semantics
  • Information extraction
  • using human language technology
  • Knowledge discovery
  • machine learning and statistical methods
  • Existing metadata, e.g. database schemas
  • mapping and merging

11
Semantic Annotation
12
Precision in Semantic Web Search
  • Semantic Search could match
  • a query Documents concerning a telecom company
    in Europe with John Smith as a director
  • With a document containing At its meeting on
    the 10th of May, the board of Vodafone appointed
    John Smith as CTO"
  • Traditional search engines 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
  • CTO is a type of director

13
PROTON the SEKT Ontology
  • PROTON - a light-weight upper-level ontology
  • 250 NE classes
  • 100 relations and attributes
  • covers mostly NE classes, general concepts and KM
    concepts
  • Mappings to DC, FOAF, RSS, DOLCE

http//proton.semanticweb.org/
14
PROTON World KB
  • PROTON is populated with a world knowledge base
  • Aims to cover the most popular entities in the
    world
  • Collected from various sources, like geographical
    and business intelligence gazetteers.
  • Organizations business, international,
    political, government, sport, academic
  • Specific people, (e.g. politicians)
  • Locations countries, regions, cities, etc.
  • Automatic identification of these entities within
    documents indexed
  • 2m OWL statements

15
KAON2 Reasoner Mapping to relational model
KAON2 Reasoner Rules Engine
hasCoAuthor
Author
Publication
hasWritten
KAON2
Rules
Mapping
Author id name .
Publication id title .
hasWritten autherId publicationId .
16
Search and Browse in SEKT
  • Squirrel
  • A Semantic Search and Browse Tool

SEKTagent A Semantic Search Alerting Service
17
The BT digital library
  • Two major document databases
  • 5 million articles abstracts plus some full
    text
  • Originally text-based with some attribute-based
    querying e.g. author, date
  • information spaces defined by queries

18
SEKTagent Overview
  • PROTON-based Semantic queries
  • Periodic alerts of matching results
  • Highlights queried entities in results (also
    related entities)
  • Natural Language summaries of ontological
    knowledge
  • Device Independence
  • PC, Palm and Mobile

19
SektAgent Demonstration
20
Squirrel - Overview
  • Hybrid approach - combines free text and semantic
    search
  • Ontology based browsing
  • Meta-result to help guide search
  • Use of rules and reasoning through KAON2
  • Natural Language summaries of ontological
    knowledge
  • User profile based result ranking

21
Squirrel Demonstration
22
Result consolidationDelivering summaries to
users instead of a list of links
  • Identifying the most relevant parts of documents
    returned as query responses
  • Results presented as consolidated summaries.
  • Reduces the need for users to navigate to and
    read multiple documents
  • Document segments and their relevance are
    determined via
  • analysis of the frequency of named entities in
    the text
  • proximity of the text to the user's query and
    interest profile.
  • Semantically Enhanced Text-Tiling

23
Query Hurricane Katrina
24
Text-tiling using named entities
  • Hurricane Katrina is thought to have killed
    hundreds, probably thousands of people in New
    Orleans, the city's mayor, Ray Nagin, has said.
    Mr Nagin said there were significant numbers of
    corpses in the waters of the flood-stricken city,
    while many more people may be dead in their
    homes.
  • There would be a total evacuation of the city, he
    said, warning it could be months before residents
    could return.
  • President George W Bush said the area could take
    years to recover.
  • Cutting short a holiday in Texas to take charge
    of the federal recovery effort, Mr Bush said the
    government was dealing with one of the worst
    natural disasters in US history.
  • "This is going to be a difficult road, the
    challenges we face on the ground are
    unprecedented, but there's no doubt in my mind
    that we'll succeed," he said.
  • Mr Bush, whose Air Force One plane flew low over
    the affected area, was taken aback by the scale
    of the disaster.

Classification against topic ontology
Politics
US Federal Government
US Local Government
25
Result Consolidation Demonstration
26
For more information
  • http//www.keapro.net/sekt
Write a Comment
User Comments (0)
About PowerShow.com