Title: Semantic%20Knowledge%20Management%20Finding%20Information%20Through%20Meaning%20Not%20Words
1Semantic Knowledge Management Finding
Information Through Meaning Not Words
- Alistair Duke
- alistair.duke_at_bt.com
- Next Generation Web Research
2The 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
3The 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
4We 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
5Semantic 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.
6In three words
Semantic information management classifies,
finds, distributes, shares and uses information
based on meaning not on the particular words used
to represent meaning.
7The 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
8The 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
9Major 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
10Extracting the semantics
- Information extraction
- using human language technology
- Knowledge discovery
- machine learning and statistical methods
- Existing metadata, e.g. database schemas
- mapping and merging
11Semantic Annotation
12Precision 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
13PROTON 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/
14PROTON 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
15KAON2 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 .
16Search and Browse in SEKT
- Squirrel
- A Semantic Search and Browse Tool
SEKTagent A Semantic Search Alerting Service
17The 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
18SEKTagent 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
19SektAgent Demonstration
20Squirrel - 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
21Squirrel Demonstration
22Result 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
23Query Hurricane Katrina
24Text-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
25Result Consolidation Demonstration
26For more information
- http//www.keapro.net/sekt