Building semantic applications

1
Building semantic applications
ACAI05/SEKT05 ADVANCED COURSE ON KNOWLEDGE
TECHNOLOGIES

• Paul Warren
• BT
• paul.w.warren_at_bt.com

2
Introductions myself, and SEKT

• Paul Warren
• Next Generation Web Research, BT
• http//www.bt.com
• http//www.btplc.com/Innovation
• SEKT http//www.sekt-project.com
• SEmantic Knowledge Technologies
• machine learning for ontology creation
• HLT for metadata extraction
• managing and reasoning with ontologies
• Motivated by corporate knowledge needs
• looking forward to the Semantic Web

3

• Motivation
• The need for semantics
• Acquiring and using semantics
• Integrating information
• SEKT Applications
• Ontology engineering in SEKT
• Applications
• Challenges

4
Motivation

• Knowledge management
• Knowledge worker productivity is the biggest
  challenge facing organisations
• 40 of U.S. workforce are knowledge workers
• Peter Drucker
• Information integration
• Heterogeneous data sources
• across or within organisations
• sensor networks

5
The need for semantics

• Corporate workers are overwhelmed with
  information
• from intranets, emails, external newslines
• but may still lack the information they require
• They need information identified
• by semantics, not just keywords
• precise and complete
• by their interests and their task context
• defined semantically

6
Higher precision, greater recall
the need for semantics

• Precision
• Find me information about Washington the man, not
  the state or city
• Find me information about a company called X
  which operates in industry Y
• Recall
• Ask for information about George W Bush and be
  given documents on the President

7
Precision in searching
the need for semantics
8
Interests and context
the need for semantics

• Need information about Jaguar?
• interested in cars, the natural world, South
  America
• with a context defined by current activities
• Not just about searching
• interest context to share information
• and to push information to user
• plus many integrated applications

9
Too much relevant information
the need for semantics

• They may even have too much relevant information
• Need to
• aggregate from disparate sources
• remove duplication
• present meaningfully
• classified
• summarised

10
In the right form
the need for semantics

• Depending on physical context
• mobile phone, PDA, blackberry
• With appropriate visualisation
• relation between documents concepts
• And expressed in natural language
• where this aids understanding
• multilingual
• Integrated
• into the desktop applications
• Seamless
• proactive, not reactive

11
Visualisation knowledge
the need for semantics
Key white - concepts orange projects
lighter shading - clusters of projects
12
The goal
the need for semantics

• Finding and sharing knowledge through its
  semantics
• for improved precision and recall
• for the users interests and current context
• Presenting information
• visually
• in natural language
• Extracting information
• in a meaningful way, without duplication
• Displaying all relevant information
• from the document and the knowledgebase

13
Acquiring and using semantics

• Some manually generated
• for high value applications
• e.g. life sciences
• Most (semi-)automatically generated
• machine learning / statistical techniques
• HLT ontology-based information extraction
• from context

14
Context
acquiring and using semantics

• What is known about the author?
• use his interests to disambiguate
• What is attached to an object
• import an object, import its metadata
• Where is it?
• position in folder structure
• Provenance
• attachment from email

15
Ontology modelling
acquiring and using semantics
sells to
employee size
operates in
16
Understanding ontologies
acquiring and using semantics
On the left is a hierarchical classification of
companies. This distinguishes between private
and public companies and EU and non-EU companies.
Note that, unlike in a taxonomy, a class may
have more than one superclass. So that
companies on the New York Stock Exchange is
both a subclass of the class non E.U. companies
and also of the class public companies. The
classes are made up of instances, in this case
individual companies, which are not shown here.
Instances of a class are, of course, also
instances of its superclasses. So any instance
of companies on the London Stock Exchange (e.g.
BT) is also an instance of public companies,
E.U. companies and companies. On the right
is shown another part of the ontology, this time
concerned with classifying industries. All
classes in an ontology are related by a chain of
superclasses to a class Thing which contains
all instances in the ontology As well as classes
and instances, an ontology contains properties.
Properties, shown by arrows in the diagram, are
defined on a given class and are of two kinds.
One kind of property relates the instances of the
class to some literal value. An example of this
is the property employee size which could be
used to describe how many employees a company
has. The other kind of property relates
instances of one defined class to instances of
another, or the same, defined class. The
property operates in relates companies to
industries whilst sells to relates companies
to one another. The properties shown here apply
to all the subclasses of company (since
instances of the subclasses are also instances of
company), whilst we could have defined
additional properties specific to any of the
subclasses. Ontologies have formally defined
semantics. This means that computers can reason
about the constructs in an ontology. Computer
scientists, mathematicians and logicians have
developed a great deal of formal theory to
understand how to do this most effectively and
efficiently. Recently this has resulted in the
standardisation by the W3C of the ontology
language, OWL (http//www.w3.org/2004/OWL/). OWL
exists in a variety of species, which correspond
to varying degrees of implementational and
computational difficulty.
17
Metadata
acquiring and using semantics

• Describing
• documents, sub-documents, pages
• author, creation date, topic(s), related to,
• entities within documents
• classes people, companies, roles
• relations CEO of
• building a knowledgebase

18
Accessing a knowledgebase
acquiring and using semantics
19
The knowledgebase
acquiring and using semantics
20
Ontology-based information extraction
acquiring and using semantics

• Ryanair announced yesterday that it will make
  Shannon its next European base, expanding its
  route network to 14 in an investment worth around
  180m. The airline says it will deliver 1.3
  million passengers in the first year of the
  agreement, rising to two million by the fifth
  year.

21
Information integration

• Motivated by
• Incompatible legacy systems
• Mergers and acquisitions
• Rapidly forming virtual organisations and supply
  chains
• Sensor networks
• Goal
• Merging information from heterogeneous
  unstructured (text) sources
• with structured information

22
Mapping ontologies
information integration

• Semi-automatic techniques
• based on similarities of name structure
• or even sound (for 4)
• e.g. PROMPT suite plug-ins for Protégé
• Semantic mapping set based
• equality() mismatch (-)
• more general(?) more specific(?)
• overlap (n)

23
Applications in SEKT
24
Intelligent content management
SEKT applications
BT digital library

• Currently
• Two major document databases
• million articles abstracts plus some full text
• Text-based and some attribute-based querying
  e.g. author, date
• information spaces defined by queries

25
Improving and extending
SEKT applications intelligent content management

• Better precision and recall
• in searching, alerting, sharing
• Automatic document annotation
• extending the knowledgebase
• clicking through to the knowledgebase
• An extended document corpus
• focussed crawling from Web and intranet
• Automatic classification
• extending and improving manual approach
• Browsing related documents
• Driven by interests and context
• learned from users behaviour

26
SEKT architecture
SEKT applications
creating amending concepts, instances
annotating correcting annotations
27
Knowledge management
SEKT applications
building on Siemens knowledgemotion
sharing and reusing knowledge across a global
team
28
Improving knowledge sharing
SEKT applications knowledge management

• Sharing
• Elements
• presentations, lessons learned
• Solutions
• application module, graphical interface
• Project approaches
• methodologies, models
• Pre-packaged projects
• with direct sales impact

29
Intelligent decision support
SEKT applications
a database of frequently asked questions using
semantic distance to identify questions and
answers
with justification drawn from comprehensive legal
databases
combining formal and informal knowledge
30
Semantic distance
SEKT applications intelligent decision support

• Semantic distance is based on weighted path
  length between concepts
• Path length is based on navigation from one
  concepts to another through any relation
  available
• Is-a
• Part-of
• Follows
• Actor

Source iSOCO
31
Better decisions
SEKT applications intelligent decision support

• Using
• Ontology of Professional Legal Knowledge
• developed with DILIGENT methodology
• Rulings
• a variety of legal databases
• Mapping between models of PLK and rulings

32
OPLK classes identified
SEKT applications intelligent decision support
33
SEKT applications intelligent decision support
Intuitive ontological subdomains
PROCEEDINGS
34
Using factorial analysis
SEKT applications intelligent decision support
35
Ontological subdomains
SEKT applications intelligent decision support
36
Architecture of Iuriservice
SEKT applications intelligent decision support
37
Ontology engineering in SEKT

• PROTON PROTo ONtology
• 250 classes 100 properties
• domain independent
• compliance with popular standards
• good coverage of concrete entities
• people, organisations, numbers
• OWL Lite

38
Person class
Ontology engineering in SEKT

• Subclass of Agent
• Superclass of Man and Woman
• hasPosition
• Person -gt JobPosition
• hasProfession
• Person -gt Profession
• hasRelative, isBossOf
• Person -gt Person

39
Property and class hierarchies
Ontology engineering in SEKT
hasRelative
Agent
Group
Organization
Charity
Commercial Organization
Company
Airline
Bank
Insurance Company
Media Company
40
Profiles in the Digital Library
Ontology engineering in SEKT
41
Topics
Ontology engineering in SEKT

• UserProfile isCurrentlyInterestedIn Topic
• InspecRecord hasSubject Topic
• Topics are instances
• of the class Topic
• Compare taxonomic approach
• Avoids classes as property values
• OWL Full

42
Classes as property values
Ontology engineering in SEKT
Source Representing Classes as Property Values,
Natasha Noy, W3C
43
Diligent
Ontology engineering in SEKT

• DIstributed Loosely-controlled and evolvInG
  Engineering of oNTologies
• Motivated by the need to develop shared
  ontologies for sharing knowledge
• Ex-post analysis in biology domain
• Based on Rhetorical Structure Theory
• seeks to explain the coherence of texts
• identifies relations
• elaboration, evaluation, justification, contrast,
  alternative, example, counter example, background
  knowledge, motivation, summary, solutionhood,
  restatement, purpose condition, preparation,
  circumstance, result, enablement, list
• DILIGENT uses subset of these

44
Distributed and loosely controlled
Ontology engineering in SEKT

• The steps
• build domain experts, users
• local adaption users
• analysis and revision board
• local update - users

45
Diligent Wiki
Ontology engineering in SEKT
46
More applications
applications

• Portals
• building on content management
• Knowledge discovery
• Business intelligence
• Inter-enterprise cooperation
• overcoming heterogeneity
• Semantic desktop
• Communication
• Collaboration
• Semantic Grid

47
Knowledge discovery
applications

• Extracting information from heterogeneous sources
• knowing your customer
• national security
• e.g. Semagix http//www.semagix.com
• Sentiment analysis
• IBMs WebFountainTM
• http//www.almaden.ibm.com/webfountain
• Intelliseek
• http//www.intelliseek.com

48
Business intelligence
applications

• Text-driven business intelligence
• e.g. ClearForest
• http//www.clearforest.com
• Identifying trends and patterns
• Merging with structured data from databases

49
The semantic desktop
applications

• Personal information management
• Desktop data as web resources
• Interoperable applications through common
  (RDF-based) data standards
• Items are first class objects
• Gnowsis http//www.gnowsis.org
• Haystack - http//haystack.lcs.mit.edu
• Fenfire - http//fenfire.org

50
Extensible and interoperable
applications
app3, e.g. diary
context
mapping
Ontology and knowledgebase OWL
reasoning, ontology management and evolution
text mining
app1, e.g. diary
app2, e.g. idea management
51
Keeping the context
applications

• When a file is emailed context is lost
• creation, classification
• and more is lost when the received file is
  stored
• sender, email thread
• Use to create metadata to enhance, e.g. search

52
Communication
applications

• Using information extraction to detect linkages
• between personal databases
• onto intranet or Web

53
Collaboration
applications

• Plus using semantics
• to find the right partners, e.g. in project
  set-up
• to create the right context for a conference
• agenda, minutes, documents

54
Semantic Grid
applications
Source http//www.semanticgrid.org

• Definitions
• flexible, secure coordinated resource sharing
  (David de Roure)
• see also Wikipedia http//en.wikipedia.org/wiki/
  Semantic_grid

55
Grid services and resources
applications the semantic grid

• Semantic description for, e.g.
• resource discovery
• matchmaking
• negotiation
• composition
• monitoring
• Must be stateful compare current web services

56
Semantic grid - challenges
applications the semantic grid

• Automated virtual organisations
• their formation and management
• Service negotiation and contracts
• Security, trust and provenance
• Self organisation
• David de Roure
• University of Southampton

57
State-of-the-art
applications

• Text mining well developed
• Semagix, Intelliseek, ClearForest
• point solutions
• Standardisation currently mostly at XML level
• Little use yet of
• context
• OWL
• reasoning

58
Challenges

• What do users really want?
• how not to overwhelm them?
• alerts, hyperlinks
• Differentiate between users?
• novice, sophisticate
• varying at different times
• What kind of user interfaces?
• to make use of all the metadata

59
Bibliography - 1

• The semantic desktop
• Sauermann, L, The Gnowsis Semantic Desktop for
  Information Integration, at the IOA Workshop of
  the ISWC2005 Conference
• Decker, S., Frank, M., The Networked Semantic
  Desktop, in WWW2004 Workshop Application Design,
  Development and Implementation Issues in the
  Semantic Web
• Chirita, P.A. et al, Activity Based Metadata for
  Semantic Desktop Search, in The Semantic Web
  Research and Applications, Springer, May / June
  2005, p.p. 439-454
• The semantic grid
• De Roure, D, Jennings, N., Shadbolt, N., The
  Semantic Grid Past, Present and Future, in
  Proceedings of the IEEE, Vol. 93, No. 3, March
  2005, p.p. 669-681

60
Bibliography - 2

• Semantic annotation
• Kiryakov, A., et al, Semantic Annotation,
  Indexing and Retrieval, Journal of Web Semantics,
  Vol. 2, December 2004, p.p. 49-79
• http//www.ontotext.com/publications/SemAIR_SWJ.pd
  f
• Information integration
• Bouquet, P., Serafini, L., Zanobini, S., Semantic
  Coordination A new approach and an application,
  in Proceedings of ISWC 2003
• Giuinchiglia, F., and Shvaiko P., Semantic
  Matching in The Knowledge Engineering Review,
  18(3)265-280, 2004

61
Bibliography - 3

• Ontology engineering
• Noy, N., Representing Classes as Property Values
  on the Semantic Web, W3C Working Group, April
  2005
• http//www.w3.org/TR/2005/NOTE-swbp-classes-as-val
  ues-20050405/
• Tempich, C., Pinto, S., Sure, Y., Staab, S., An
  Argumentation Ontology for DIstributed,
  Loosely-controlled and evolvInG Engineering of
  oNTologies (DILIGENT), ESWC2005, p.p. 241-256
• Legal case study
• Benjamins, R., The Semantic Web Legal
  Application, iSOCO, May 2005
• http//bibo.incubadora.fapesp.br/portal/CursoSeman
  ticWeb/Iuriservice.ppt

62
Bibliography - 4

• General
• Introducing Semantic Technologies and the Vision
  of the Semantic Web, Semantic Interoperability
  Community of Practice (US)
• http//colab.cim3.net/file/work/SICoP/WhitePaper/S
  ICoP.WhitePaper.Module1.v5.4.kf.021605.doc
• Evaluation and Market Report (WonderWeb project),
  Top Quadrant
• http//wonderweb.semanticweb.org/deliverables/docu
  ments/D25.pdf