Making Information Systems
Intelligent
Dr. Geoffrey P Malafsky
TECHi2
The Need
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Information overload
Time compression
Uncertainty
Proactive decision making and actions
2
What is Intelligence
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Turing test
Reasoning
Accuracy
Fusion and
transformation of
inputs
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Sensor
Data
Learning
3
Time and Certainty
4
Intertwined Complex Information
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Example from DARPA
Evidence Extraction & Link
Discovery
Today’s Situation: ~10k
messages/day from multiple
sources read by multiple
analysts and analyzed in
multiple manual nonintegrated tools
Similar to Social Network
Analysis
5
Knowledge is Personal
“Set the soldering iron to 350 degrees”
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information from manual for general use
knowledge from expert for specific
manufacturing process
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“If the soldering iron is even 20 degrees
hotter or colder, the connection will fail
and the part will be returned and
eliminate all profit. Watch carefully for the
color of the solder”
6
Taxonomy Complexity
80. INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND
TECHNOLOGY
81. Materials science
81.05.t
Specific materials: fabrication, treatment, testing and analysis

Superconducting materials, see 74.70 and 74.72
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Magnetic materials, see 75.50
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Optical materials, see 42.70
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Dielectric, piezoelectric, and ferroelectric materials, see 77.80
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Colloids, gels, and emulsions, see 82.70.D, G, K respectively

Biological materials, see 87.14
81.05.Bx
Metals, semimetals, and alloys
81.05.Cy
Elemental semiconductors
81.05.Dz
II–VI semiconductors
81.05.Ea
III–V semiconductors
81.05.Gc
Amorphous semiconductors
81.05.Hd
Other semiconductors
81.05.Je
Ceramics and refractories (including borides, carbides, hydrides, nitrides,
oxides, and silicides)
81.05.Kf
Glasses (including metallic glasses)
81.05.Lg
Polymers and plastics; rubber; synthetic and natural fibers; organometallic
and organic materials
81.05.Mh
Cermets, ceramic and refractory composites
81.05.Ni
Dispersion-, fiber-, and platelet-reinforced metal-based composites
81.05.Pj
Glass-based composites, vitroceramics
81.05.Qk
Reinforced polymers and polymer-based composites
81.05.Rm
Porous materials; granular materials
7
What We Need: IT Conversations
From James Hendler, Agents and the Semantic Web, IEEE Intel Sys, Mar/Apr 2001
8
Current Technology Performance
Complexity
Aspects of Knowledge Discovery
Knowledge
Representation
Complex Relational
Information
Advanced
Discovery
Vast
Relations across time
and space for people,
places & things
Relations among
people, places &
things
Human-Computer
Interaction
attributes, links,
nodes
Status
Iterative
Incremental
>106
Simple Relational
Information
Guided
Discovery
Data
Volume
Active Learning
Far
Beyond
State-of-Art
Unspecified, evolving
problem
Substantial
Interactive
103 - 104 attributes,
links, nodes
User-specified
problem, with
suggested retargeting
Beyond
State-of-Art
Some prior knowledge
Propositional
Information
Naïve
Discovery
Simple attributes for
people, places &
things
Minimal
Negligible
100s of attributes,
links, nodes
User-specified
problem
State-of-Art
No prior knowledge
9
Current Performance
Performance
Human
Augmented Cognition: Large
KB+Models+Human engineering
Intelligent Systems
Natural
Language+Ontology
Maturity
D
en
si
ty
Search/
classification
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Systems Engineering: Matching
Functional Components
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Coupling to the Human
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DARPA Augmented Cognition
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Multisensor Fusion
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DARPA EELD: Knowledge Creation
Technologies
AI/KR
Expert
Knowledge
Engineering
Domain
Expert
(e.g. HPKB)
Knowledge
Acquisition
(e.g. RKF)
Pattern
Learning
Labeled
Examples
N
N N
N N N
N N N N
P
P P
P P P
P P P P
Upper
Ontology
Patterns
(models)
Core
Theories
Domain-Specific
Theories/Models
Facts
(Database)
Text
Documents
Link
Discovery
Evidence
Extraction
Negative Positive
Examples Examples
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Semantic Web
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Create a Web where
information can be
“understood” by
machines as well as
humans
Must convey machineaccessible semantics
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Ontology Contains Context and
Relationships
- Madache, Schnurr, Staab &
Studer, Representation LanguageNeutral Modeling of Ontologies
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Integrated Presentation
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DRAFT OV-1
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