Terminology and Knowledge Engineering in Fraud Detection - PowerPoint PPT Presentation

1 / 26
About This Presentation
Title:

Terminology and Knowledge Engineering in Fraud Detection

Description:

Semantics Technology and Applications Research Laboratory (STAR Lab) ... ad operare in qualit di 'imprese di investimento' ai sensi della dsi, in ... – PowerPoint PPT presentation

Number of Views:222
Avg rating:3.0/5.0
Slides: 27
Provided by: Koen56
Category:

less

Transcript and Presenter's Notes

Title: Terminology and Knowledge Engineering in Fraud Detection


1
Terminology and Knowledge Engineering in Fraud
Detection
Koen KerremansRita Temmerman
Gang Zhao
Centrum voor Vaktaal en Communicatie
(CVC)Department of Applied LinguisticsErasmusho
geschool Brusselhttp//cvc.ehb.be
Semantics Technology and Applications Research
Laboratory (STAR Lab)Department of Computer
ScienceVrije Universiteit Brusselhttp//www.sta
rlab.vub.ac.be
2
  • How are terminology and knowledge engineering
    used in the fight against financial fraud?
  • How to organise terminology and knowledge
    engineering methods into a development process of
    technological solutions in the fight against
    financial fraud?

3
General outline
  • FF POIROT
  • Aims
  • Cases
  • Partners
  • Methodologies
  • AKEM (knowledge engineering)
  • Termontography (terminology engineering)
  • Interaction of methodologies
  • Future work
  • Conclusion

4
FF POIROT
Financial Fraud Prevention Oriented Information
Resources using Ontology Technology
  • Aims
  • Apply Semantic Web technology to fraud detection
    and prevention, thereby showing the potential of
    ontologies in these areas
  • Construct multililingual terminological as well
    as formal knowledge repositories covering the
    domains of interest
  • Propose methods and guidelines in terminology and
    knowledge engineering
  • Develop new and/or improve existing tools to
    support terminology and knowledge engineering

5
FF POIROT cases
  • VAT carousel fraud
  • VAT fraud in which fraudsters sell goods at VAT
    inclusive prices and disappear without paying the
    VAT paid by their customers to the tax
    authorities
  • Companies unwittingly involved in this type of
    fraud can be held responsible for the missing
    VAT
  • Each company has to find out whether or not it is
    safe to do business with a trader from another
    EU country
  • On-line investment fraud
  • the selling of overpriced or worthless shares,
    bonds, or other financial instruments to the
    general public
  • In Italy, Consob searches suspicious websites via
    traditional search engines such as Google,
    Altavista,

6
FF POIROT
  • Use of the ontology
  • Knowledge management of fraud investigative
    expertise
  • Information exchange between investigative bodies
  • Automation of parts of monitoring or
    investigative procedures with knowledge-based
    applications (e.g. information extraction)
  • Use of multilingual terminology
  • Dicionary purposes
  • Multilingual keywords in information extraction
  • Explanation of reasoning in natural language
  • Knowledge resource consulted during ontology
    development

7
FF POIROT partners
8
AKEM
  • Application Knowledge Engineering Methodology
  • Development cycle
  • Knowledge scoping (result stories)
  • Knowledge analysis
  • Ontology development
  • Deployment

9
AKEM
  • Based on DOGMA
  • Developing Ontology-Guided Mediation for Agents
  • Ontology a set of lexons and their commitments
    in particular applications
  • Lexon a grouping element stored in a lexon base
    and composed of terms and roles
  • ltContext, Term_1, Role_1, Term_2, Role_2gt

10
AKEM
  • Why Application Knowledge Engineering
    Methodology?
  • There is a need to organise a geographically
    distributed, multidisciplinary team of domain
    experts, knowledge analysts and engineers in a
    methodical traceable development cycle
  • There is a need to examine how knowledge can be
    extracted from different perspectives on fraud to
    improve the quality of the fraud ontology

11
AKEM
H1
  • An example of a legal view Wigmore chart
  • Blue hypothesis
  • Red claim
  • Purple evidence
  • Green fact


1
1.1
1.2
1.3
1.1.1
E1.2.1
E1.3.1
E1.1.1
F1.1.1.1
F1.1.1.2
12
AKEM
  • H1 Public offer of company X is unlawful
  • 1.1 X solicits investors on the WWW
  • E1.1.1 X manages website that solicits investors
  • F1.1.1.1 Website states name X
  • F1.1.1.2 Registration details indicate X as
    registrant of website
  • 1.2 No advance notice of solicitation to Consob
  • E1.2.1 X did not give a notification to Consob
    regarding public offer to purchase
  • 1.3 No related prospectus filed with Consob
  • E1.3.1 X did not draft or file a prospectus with
    Consob regarding public offer to purchase

13
AKEM
  • Extraction of knowledge constituents and
    abstraction into production rules, allow
    knowledge modellers to identify and organise the
    abstract concepts and relations into a lexon base
  • Example

14
Termontography
  • a terminological approach in which
    (multilingual) terminological knowledge,
    retrieved from texts, is structured according to
    a framework of knowledge (i.e. categorisation
    framework)
  • Why Termontography?
  • Terminographers need a common reference framework
    to scope the terminology work
  • There are significant commonalities between
    terminology compilation and text-based ontology
    development
  • In our view a terminological analysis can
    contribute to the formalisation of a given domain

15
Termontography
Search phase (3)
(mono- or multilingual) domain-specific corpus
first version of termontological database
Ontology
Dictionary
Refinement phase (4)
(mono- or multilingual) termontological database
Information gathering phase (2)
TSR categorisation framework
Verification phase (5)
Domain- experts
Validation phase (6)
Knowledge Analysis phase (1)
16
Interaction of methodologies
AKEM
TERMONTOGRAPHY
KNOWLEDGE SCOPING
17
Interaction of methodologies
  • Knowledge scoping
  • Developing terminological resources and
    ontological repositories requires above all an
    insight in the domain of interest
  • Domain experts can support the knowledge
    acquisition process by pointing out the relevant
    categories/topics (given the envisaged
    tasks/applications)
  • Example Transactions for which no VAT is
    required

18
Interaction of methodologies
  • Terminology base ? ontology development
  • Rationale
  • The AKEM extraction task seeks for basic semantic
    elements and follows linguistic units in natural
    language texts
  • Experience shows that ontology engineers resort
    from time to time to terminological resources for
    background information or exact definitions
  • Characteristics of terminological analysis
  • Special emphasis on documenting semantic contexts
    by means of textual contexts
  • Entries also include linguistic semantic
    descriptions such as agent-predicate-patient/recip
    ient links and cross references among items of
    contents

19
Interaction of methodologies
20
Interaction of methodologies
21
Interaction of methodologies
  • Consequences
  • Productivity of ontology engineers is improved by
    suggestions from terminologists, who examine the
    same knowledge resources
  • Terminography adds a linguistic viewpoint to
    application-specific modeling
  • During ontology development, multilingual
    terminological information can help discover
    semantic gaps across languages due to social
    and cultural differences and facilitate consensus
    building in a multilingual team of developers

22
Future work
  • How to represent meaning variations of
    lexicalisations referring to the same category?
  • E.g. An event on which VAT is to be paid
  • Irish legislation chargeable event
  • VAT will be due on the date the invoice is issued
  • UK legislation chargeable event
  • VAT will be due no later than the 15th day
    following the month in which the supply takes
    place
  • French legislation fait générateur
  • VAT will be due at the moment the goods are
    supplied

23
Future work
  • Termontography workbench

24
Conclusion
  • We have shown how terminology and knowledge
    engineering is used in the fight against
    financial fraud
  • We have shown how the methods of experts with
    different backgrounds have been translated into a
    coherent and traceable workflow

25
Conclusion
26
Conclusion
  • Project
  • http//www.ffpoirot.org
  • Partners
  • STAR Lab http//www.starlab.vub.ac.be
  • CVC http//cvc.ehb.be
  • JBC http//www.cfslr.ed.ac.uk/
  • RACAI http//www.racai.ro
  • KS http//www.knowledgestones.com/
  • LC http//www.landcglobal.com/index.php
  • Consob http//www.consob.it/main/index.html
  • VAT_at_ http//www.vatat.com
Write a Comment
User Comments (0)
About PowerShow.com