Using Knowledge to
Exploit Data
March 2001
Gio Wiederhold
Stanford University
www-db.stanford.edu/people/gio.html
7/26/2016
Gio Wiederhold IBM R 1
Data + Knowledge Information
Knowledge Loop
Data Loop
Storage
Selection
Integration
Abstraction
Experience
Projection
Information
Education
Recording
State changes
Decision-making
Action
Gio Wiederhold IBM R 2
4 Information Technology Tasks
Selection: SQL
.
one verb language
Integration: Middleware
Mediation* .
Mediation
Actionable Information is
created at the confluence
of data -- the state
&
knowledge -- the ability to
select, integrate, abstract
and project the state
into the future
Abstraction: Visualization
Summarization
Projection*: Simulation .
Spreadsheets
* Focus of my talk
Gio Wiederhold IBM R 3
Today's sources and needs
Many resources
• computing
– databases
– analytical
software
– simulations
• people
– organizations
– domains
Many objectives
• operations
– reports
– routine actions
– optimization
• decision-making
– crisis response
– allocations
– conflict resolution
» semantics
Gio Wiederhold IBM R 4
Integration Problems:
Source Autonomy
•
•
•
•
•
•
 Heterogeneity
Hardware platforms . . . . . . Hidden by operating system
Operating systems . . . . . . Choices are reducing: NT, UNIX, ...
Programming languages . . Irrelevant in remote access
Database system models . . Relational and E-R common
Database systems . . . . . . . Standards, convergence
Coverage . . . . . . . . . . . . . . . Source dependent
– Attributes . . . . . . . . . . . . . . .
– Scope . . . . . . . . . . . . . . . . . . .
documented, catenable
undocumented, intersecting
• Data representation . . . . . Conversion problems, nulls
• Data semantics . . . . . . . . . Requires multi-domain knowledge
Gio Wiederhold IBM R 5
Managing Large Scale Problems
• Build big systems ?
– cost
– obsolescence
– inflexibility
– unmaintainable
• Compose systems ?
– limits to control
– remote maintenance
– heterogeneity
Gio Wiederhold IBM R 6
Semantic Heterogeneity
If interoperation involves distinct
domains mismatch ensues
• Autonomy conflicts with consistency,
– Local Needs have Priority,
– Outside uses are a Byproduct
Semantic Heterogeneity must be addressed
• Naming of object classes and objects
• Ontology -- relationships
• Scope of object classes and attributes
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Gio Wiederhold IBM R 7
Semantic Mismatch Examples
Information comes from many autonomous sources
Differing viewpoints
- by source – differing terms for similar items
{ lorry, truck, shag }
– same terms for dissimilar items
trunk ( luggage, car )
– differing coverage
vehicles ( DMV, police, AIA )
– differing granularity
trucks ( shipper, manufacturer )
– different scope
student ( Stanford museum, classes )
employee (Stanford personnel, payroll )
• Hinders use of information from disjoint sources
– missed linkages
– irrelevant linkages
loss of information, opportunities
overload on user & application program
• Poor precision when merged
Still ok for web browsing , poor for business & science
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Gio Wiederhold IBM R 8
Need for precision
More precision is needed as data volume increases
--- a small error rate still leads to too many errors
False Positives have to be investigated, cause work
1
( attractive-looking supplier - makes toy trucks
apparent drug-target with poor annotation )
Information Wall
..........................
False positives = poor precision
typically cost more than
false negatives = poor recall
data errors
False Negatives cause
lost opportunities,
2 suboptimal to some degree
information quantity
adapted from Warren Powell, Princeton Un.
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Gio Wiederhold IBM R 9
Proposed Language Solution
Specify and define terminology usage: ontology
• Domain-specific ontologies XML DTD assumption
–
–
–
–
–
Small, focused, cooperating groups
high quality, some examples – oil trading, SLE, Shakespeare plays
allows sharable, formal tools
ongoing, local maintenance affecting users - annual updates
poor interoperation, users still face inter-domain mismatches
Cannot achieve global consistency, although that would be
wonderful for users and their programs
–
–
–
–
too many interacting source domains
long time to achieve, 2 sources ( UAL, LH ), 3 (+ trucks), 4, … all ?
costly maintenance, since all sources evolve
no world-wide authority to dictate conformance
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Gio Wiederhold IBM R 10
Ontology Creation
Three Common Alternatives:
 Create a committee to define everybody’s terms
Takes many years, until people are worn out
 Ignored when changes make deviation necessary
 Collect all terms and put them into large model
[ Cyc, UMLS, Federated Schemas, . . . ]
 Can be rapid
 Hides conflicts, leads to low precision
 Hard to maintain (requires committee)
 Focus on local Terms
 Requires local effort
 Ignores conflicts among distinct domains
 Empowers participants
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Gio Wiederhold IBM R 11
Central Solutions do not Scale
What works
with 7 modules
and one person
in charge
fails when we
have 100 and need
a committee
Any changes in resources affects the central module
Gio Wiederhold IBM R 12
The semantics problem
Common assumption in assembling and integrating
distributed information resources:
• The language used by the resources is the same
• Sub languages used by the resources are subsets of
a globally consistent language
This assumption is provably false
Working towards the goal of globally consistency is
1. naïve -- the goal cannot be achieved or maintained
2. inefficient -- languages are efficient in local contexts
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Gio Wiederhold IBM R 13
Domains and Consistency
.
• a domain will contain many objects
• the object configuration is consistent
• within a domain all terms are consistent &
• relationships among objects are consistent
Domain Ontology
• context is implicit
No committee is needed
to forge compromises *
within a domain
 Compromises hide valuable details
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Gio Wiederhold IBM R 14
Assumption
• The assumption of consistency defines
the scope of a domain
• When it is false, we get semantic trickery:
When the going gets tough, the tough
get going .
[Alfred Spector, when convincing me
Tuesday to brave the storm of the decade ]
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Gio Wiederhold IBM R 15
Mediated Architecture:
Transform Data to Information
Application
Layer
decision-makers at workstations
CORBA / XML
Mediation
Layer
domain specific value-added services
SQL/SimQL
Foundation
Layer
data and simulation resources
Gio Wiederhold IBM R 16
Sample: Integration provides precision
Problem posed in DARPA HPKB Program
• What is the most recent year – Subproblems resolved by SKC
an OPEC member nation was
* Factbook – a secondary source
on the UN security council (SC)?
-- has out of date OPEC & UN
• SKC resolves 3 Sources
SC lists
» CIA Factbook ‘96 (nation)
» OPEC (members, dates)
» UN (SC members, years)
– SKC obtains the Correct
Answer
» 1996 (Indonesia)
• Other groups used only the
integrated source: CIA Factbook;
– obtained more,
but factually wrong answers
7/26/2016
• Indonesia not listed
• Gabon (left OPEC 1994)
* different country names
• Gambia => The Gambia
* historical country names
• Yugoslavia
» UN lists future security council
members
• Gabon 1999
needed temporal reasoning
Gio Wiederhold IBM R 17
Formalize precise integration
• Start with local, well defined and
maintained ontologies.
• Do not assume mutual consistency
• Create means to link those ontologies
precisely: articulations
– Concepts
– Tools for ontology support
– Tools for ontology exploitation
• Support to rapidly update the articulations
when sources are updated
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Gio Wiederhold IBM R 18
SKC grounded(Dbish)definition
.
• Ontology:
a set of terms and their relationships
• Term:
a reference to real-world and abstract objects
• Relationship:
a named and typed set of links between objects
• Reference:
a label that names objects
• Abstract object:
a concept which refers to other objects
• Real-world object – captured in databases :
an entity instance with a physical manifestation
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Gio Wiederhold IBM R 19
An Ontology Algebra
A knowledge-based algebra for ontologies
Intersection
Union
Difference
create a subset ontology
keep sharable entries
create a joint ontology
merge entries
create a distinct ontology
remove shared entries
The Articulation Ontology (AO) consists of
matching rules that link domain ontologies
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Sample Operation: INTERSECTION
Result contains
shared terms
Source Domain 1:
Owned and maintained
by Store
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Terms useful
for purchasing
Source Domain 2:
Owned and maintained
by Factory
Gio Wiederhold IBM R 21
Other Basic Operations
DIFFERENCE: material
fully under local control
UNION: merging
entire ontologies
Articulation
ontology
typically prior
intersections
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Gio Wiederhold IBM R 22
Sample Intersections
Articulation
size = size
ontology
matching rules : color =table(colcode)
style = style
Anatomy
{. . . }
Shoe Factory
Shoe Store
• Shoes { . . . }
• Customers { . . . }
• Employees { . . . }
foot = foot
Employees
Nail (toe, foot)
...
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• Material inventory {...}
• Employees { . . . }
• Machinery { . . . }
• Processes { . . . }
• Shoes { . . . }
Department
Store
Hardware
Employees
Nail (fastener)
...
Gio Wiederhold IBM R 23
INTERSECTION support
Articulation ontology
Terms useful
for purchasing
Matching
rules that use
terms from the
2 source domains
Store
Ontology
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Factory
Ontology
Gio Wiederhold IBM R 24
Interoperation via Articulation
At application definition time
– Match ontologies
– Establish articulation rules.
– Record the process
At execution time
– Query rewriting
– Optimization based on an Ontology Algebra.
For maintenance
–Regenerate rules using the stored
formulation
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Gio Wiederhold IBM R 25
Features of an algebra
Operations can be composed
Operations can be rearranged
Alternate arrangements can be evaluated
Optimization is enabled
The record of past operations can be
kept and reused
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Gio Wiederhold IBM R 26
Example: Nexus creation
Term linkages automatically extracted from 1912 Webster’s dictionary *
using algebra primitives.
* free, other sources
Term definitions using the term 'Vehicle'
One graph from network
have been processed.
.
Notice presence
of 2 domains:
chemistry, transport
Terms used in defining the term 'Vehicle'
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Gio Wiederhold IBM R 27
Re-Use Example
•
Nexus Creation
(A graph derived from a dictionary
linking headwords and definitions)
1. Created from public, 1913 Webster’s
(OCRed , 5% errors)
–
50Mb, 97K defs  17 weeks, 480 primitive rules
2. Recreated from revised 1913 Webster’s (> 10%)
–
50Mb, 97K  2 weeks, 440 primitive rules
3. Applied to Roget’s Thesaurus
–
1Mb, 1K defs  <1 week, 40 primitive rules
4. Applied to Oxford English Dictionary
–
570Mb, 5130K defs  5 weeks, 400 primitive rules
[ Jan Jannink thesis Stanford CSD 2001 ]
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Gio Wiederhold IBM R 28
Articulation Generator
Being built by Prasenjit Mitra
Thesaurus
OntA
Phrase Relator
Context-based
Word Relator
Driver
Structural
Matcher
Ont1
Ont2
Semantic
Network
(Nexus)
Human Expert
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Gio Wiederhold IBM R 29
Semi-automatic approach
Provide library of automatic match heuristics
• Lexical Methods -- spelling
• Structural Methods – walk the graphs and try
• Nexus to link terms (buyer[DMV]owner[Ford])
• Reasoning-based Methods
• Hybrid Methods
– generally iterative methods
GUI tool to
- display matches and
- verify generated matches using human expert
- expert can also supply matching rules
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Gio Wiederhold IBM R 30
Lexical Methods
• Preprocessing rules.
-Expert-generated seed rules.
e.g., (Match O1.President O2.PrimeMinister)
-Context-based preprocessing directives.
Existing narrow domain ontologies
• Thesaurus - synonyms, relationships
• Nexus – definition-based graph linkage
• Distance of words as measure of
relatedness.
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Gio Wiederhold IBM R 31
Tools to create articulations
Graph matcher
for
Articulationcreating
Expert
Transport
ontology
Vehicle
ontology
Suggestions
for articulations
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Gio Wiederhold IBM R 32
continue from initial point
Also suggest similar terms
for further articulation:
• by spelling similarity,
• by graph position
• by term match nexus
Expert response:
1. Okay
2. False
3. Irrelevant
to this articulation
All results are recorded
Okay’s are converted into articulation rules
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Gio Wiederhold IBM R 33
Using the nexus
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Navigating the nexus
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Exploiting the result
Result has links
to source
R=f(KR1, IE1)
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.
Not yet done in SKC
Processing and
evaluation is best
performed within
Source Domains
R=f(KRn, IEn )
Gio Wiederhold IBM R 36
Next Steps
1. We now have integrated multiple sources
– with great care
2. This result is likely to be large, needs
– Visualization
– Summarization
– Abstraction
– Modeling
3. Projection into the future
– a major gap today’s information systems
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Gio Wiederhold IBM R 37
Current state of DM Support
past
now
time
organized support
Data integration
Databases
distributed, heterogeneous
future
disjointed support
x17 @qbfera
ffga 67 .78 jjkl,a
nsnd nn 23.5a
Intuition +
• Spreadsheets
• Planning of allocations
• Other simulations
various point assessments
Gio Wiederhold IBM R 38
Information Systems should also
Project into the Future
past
now
future
time
Databases,
accessed via SQL or
CORBA compliant
wrappers
Simulations,
accessed via SimQL and
compliant wrappers
Msgs,
sensors
Human decision-making is essential when inputs do
not have common metrics: Price vs quality of goods/ healthcare
Gio Wiederhold IBM R 39
Types of simulation services
1. Continously executing:
weather prediction
– SimQL result reports best match samples
2. Execution specific to query: what-if assessment
– primary tool today: Spreadsheets
– may require HPC power for adequate response
3. Low-level simulations results stored in a base:
use of smart materials in product design
– perform inter-/extra-polations to match queries
4. Combinations, i.e., 2. + 3.: top layer simulation
using stored partial lower level results:
product performance in new setting
All with uncertainty parameters
Gio Wiederhold IBM R 40
Use of Simulation Results
A
0.6
0.3
0.5
0.2
0.1
0.5
0.07
0.03
0.5
0.2
0.1
time
0.4
0.2
0.1
0.3
0.1
B
Simulation results can be composed for
planning alternative Courses-of-actions
A: Composition should include computation and
recomputation of likelihoods
B: x values of outcomes leads to present values
Likelihoods change as now moves forwards and
eliminates earlier alternatives.
Gio Wiederhold IBM R 41
1000
500
700
1200
-200
600
950
-100
Prototype Implementation
Developer
Customer
Query
Development
Interaction
Help
Schema
Manager
Parser
Schema
Commands
Metadata
Manager
Filing of
Access
Specs Metadata
Use of
Access
Specs
Help
Production
Interaction
Schema
Commands
Query
manager
Initiation and
Results of
Simulations
Error
reports
Wrapped ..
Simulations
Gio Wiederhold IBM R 42
Interoperation for Simulation
Databases
• serve clients via SQL by
Sharing a Model (The Schema)
A query language over the model
the SQL interface enables
• independence of
application development
DBMS technology development
reuse of infrastructure
Today
• most new systems use a
DBMS for data storage
even with less performance,
inability to handle all problems,
but enough of them well enough.
Simulations should
• serve clients via SimQL by
Sharing a Model (research q.)
A query language over the model
a SimQL interface will enable
• independence of
application development
simulation technology develop’t
reuse of infrastructure
Objective
• build information systems
combining DBMS, Simulations
even with less performance,
inability to handle all problems,
but enough of them . . .
Gio Wiederhold IBM R 43
Even the present needs SimQL
last recorded observations
point-in-time for
situational
assessment
simple simulations
to extrapolate data
past
Not all data are current:
now
time
future
• Where is the customer now?
• Is the delivery truck in X?
• Where will they meet (mobile setting) ?
• Is the right stuff on the truck?
• Will the loading crew be at X?
Gio Wiederhold IBM R 44
The language is an interface
Applies to database and simulation services
• A service service program can be written in
any language
» C, C++, Fortran, Java
• A simulation service must be compliant to
the interface specifications
» SQL, SimQL schema
» SQL, SimQL query language
» XML for output voluminous semantic tags
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Gio Wiederhold IBM R 45
Moving to a Service Paradigm
•
•
•
•
Server is an independent contractor, defines service
Client selects service, and specifies parameters
Server’s success depends on value provided
Some form of payment to be received for services
x,y
Databases are a current example.
Simulations have the same potential.
Gio Wiederhold IBM R 46
Knowledge maintenance
Changes of
user needs
Application
Interface
Software & People
Models, programs,
rules, caches, . . .
Resource Interfaces
Owner/ Creator
Maintainer
Lessor - Seller
Advertisor
Domain
changes
Resource
changes
Gio Wiederhold IBM R 47
Domain Specialization
• Knowledge Acquisition (20% effort) &
• Knowledge Maintenance (80% effort *)
to be performed by
• Domain specialists
• Professional organizations
• Field teams of modest size
autonomously
maintainable
Empowerment
* based on experience with software
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Gio Wiederhold IBM R 48
Today DM support is disjoint
does not interoperate
Planning Science
Distribution
extensions to move
to networked support
are also disjoint
Gio Wiederhold IBM R 49
Research Goals
Reduce volume
especially type 2 errors
• Among intermediate processing nodes
– bandwidth requirements
• To end-users
– mobile displays
– human capabilities 7±2
Services reduce bandwidth requirements
– Effective mediation (not just middleware)
–
smarts can reduce volume
while increasing information contents
– Model of customer-class needs > personalization
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Gio Wiederhold IBM R 50
Research Questions
Manage the Complexity
"only simple stuff works"
• Number of interfaces
– Among n domains m < n 2 ?
• Complexity of interfaces
– Variety of standards,
–
used to establish dominance
– Feature overload (recovery, security, ... )
• Intellectual domain interfaces
– Acceptance of service paradigm,
–
rather than ownership
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Gio Wiederhold IBM R 51
Vision for Information Systems
Predictions
Predictions
Models
CoAs
Decisions
Effects &
Reactions
sensors
sensors
past
now
future
time
Gio Wiederhold IBM R 52
Backup Viewgraphs
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Functional Layer
User interface
Service
interface
Human-computer
Interaction
Applicationspecific code
MEDIATION
Resource access
interface
Domainspecific
code
Sourcespecific
code
Real-world
interface
Gio Wiederhold IBM R 54
Requirements . . . assessment
•
•
•
•
•
•
•
•
•
•
•
Access to remote data . . . . .
Resolution of heterogeneity . .
Integration of matched data . .
Access to knowledge . . . . .
Access to sensor data . . . . .
Access to simulation tools . .
Creation of alternate CoAs . .
Assessment of alternate CoAs
Showing alternatives . . . . .
Provision of drill-down . . . .
Security . . . . . . . . . . . . .
.
.
.
.
.
.
.
.
.
.
.
. good progress
I3
. ok, - semantics
I3, ..
. good progress
DDB
. progress
HPKB
. ok, poor integration BADD
. poor, manual
. isolated tools
DRPI
. isolated tools
. good progress
. poor, many barriers
. poor integration
SURV
Gio Wiederhold IBM R 55
SKC Summary
.
• Algebra enables Interoperation by
dealing explicitly with differences by knowledge
identifying maintenance domains
keeping sources autonomous
• Assumes domain has a common ontology
composing domain ontologies requires the algebra
to manage the linkages where articulation occurs
processes are best executed within the domains
• Knowledge about articulation is disjoint
allows integration specialists to work independently
supports multiple intersections and views
• Maintenance is structured and partitioned
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Gio Wiederhold IBM R 56
Current Directions
• Experience with real world (imperfect) data
confirms validity of our approach
– Expert sources are better maintained than
general sources
– Rules applied to multiple sources provide
more reliable and accurate query results
– Component architecture enables scalable,
maintainable knowledge base development
• Developing proof of concept environment with
HPKB standard knowledge base connectivity
interface
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Gio Wiederhold IBM R 57
Components of Ontologies
.
• Vocabularies
• words as handles for classes and concepts
• from database schemas. textbooks, catalog
indexes
• identified with their domain or context
• Relationships
• meaning is primarily defined through relationships
 Employee of a Company; Nails in a Shoe
• Annotations
• material to clarify the meaning of the terms
• contributed by users as well as original authors
• best with some examples
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Gio Wiederhold IBM R 58
Knowledge Composition
Composed knowledge for
applications using A,B,C,E
Articulation
knowledge
Legend:
U
U
for
(A B) U
(B C) U
(C E)
Articulation
knowledge
(C E)
U
U : union
U
: intersection
Knowledge
resource
E
U
Knowledge
resource
A
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U
(B
C)
Knowledge
resource
B
Knowledge
resource
C
(C
U
U
Articulation
knowledge
for (A B)
D)
Knowledge
resource
D
Gio Wiederhold IBM R 59
Primitive Operations
Model and Instance
Unary
• Summarize -- structure up
• Glossarize - list terms
• Filter - reduce instances
• Extract - circumscription
Binary
• Match - data corrobaration
• Difference - distance
measure
• Intersect - schem
discovery
• Blend - schema extension
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Constructors
• create object
• create set
Connectors
• match object
• match set
Editors
• insert value
• edit value
• move value
• delete value
Converters
• object - value
• object indirection
• reference indirection
Gio Wiederhold IBM R 60
Innovation in SKC
•
•
•
•
No need to harmonize full ontologies
Focus on what is critical for interoperation
Rules specific for articulation
Tools for creation and maintenance
– Maintenance is distributed
» to n sources
» to m articulation agents
• Potentially many sets of articulation rules
is m < n2 , depends on semantic architecture density
a research question
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Gio Wiederhold IBM R 61
SKC Synopsis
• Research:
– Reliable query answers from heterogeneous,
imperfect data sources
• Sources:
– General: CIA World Factbook ‘96, UN-www, OPEC-www
Webster’s Dictionary, Thesaurus, Oxford English Dictionary
– Topical: OPEC, BattleSpace Sensors, Logistics Servers
• Client:
– DARPA High Performance Knowledge Base project
• Theory:
– Rule-based algebra
– Translation & Composition primitives
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Gio Wiederhold IBM R 62
ArcRank
• For All source s and target t nodes in graph
sort outgoing vs ,t j , rank by sorted order res (vs ,t j )
sort incoming vs ,t , rank by sorted order ret (vsi ,t )
i
for each arc es ,t compute mean( res (vs ,t ), ret (vs ,t ))
• In ranking
– Equal values take same rank
– Ranks numbered consecutively
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Gio Wiederhold IBM R 63
Vehicle
sales
ontology
Vehicle
registration
ontology
Tools to create articulations
Suggestions
for articulations
7/26/2016
Combine ontology graphs
with expert selection
based on spelling,
graph matching, and
a nexus derived from
a dictionary (O.E.D.)
Gio Wiederhold IBM R 64
Relative Arc Importance
• PageRank (Google) limitations
– node oriented
– high rank to words with little semantic value
» conjunctions, articles
The
» prepositions, pronouns
And
to
it
• Relative arc importance
– contribution of source rank to target rank
– vs ,t   ps / | as |
pt
es ,t
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Gio Wiederhold IBM R 65
Internet requirements for Simulation
• Ubiquitous acess to simulations
of a wide variety of types
• Rapid response to parameter changes
– often High-Performance computation is
needed
– distributed simulations with
synchronization
• Rapid Service Composition
– High bandwidth among simulations
– Acces to multiple services in parallel
Gio Wiederhold IBM R 66
Country Graphs
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To be matched to
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Place of SimQL in Objective-based Planning
Higher Level Objectives, Intel, OB,
ROE, Commanders Guidance & Intent, Etc.
Campaign
1
Status
Execution Feedback Determine
Status
2 Develop *
Objectives
Phased Sequenced
*: w/Measures
Objectives
3 Phase & *
Sequence
Objectives
Prioritized Sequenced
Tasks
4 Assign
Task / Activity
Assessment
Simulation
Plan
results
5 Develop
Assessment Plan
SimQL
Access to Simulations
Required Resources
Simulation
parameters
Determine
Req’mts
Resource Constraints
from JFACC PIP
7 Assess and/or
Rehearse Plan
Plan Assessment Feedback
Gio Wiederhold IBM R 69
Information Flow for Training Initiative
sample
scenarios
scenario
refinement
trainer /
controller
aggregation/
analysis/
evaluation
ISI scenario language
Scenarios
Objectives
tasks
explosion
doctrine
TRADOC
mediator
knowledge
base
Requirements
exercise
design
Legend
sources
scenario
justification
Data
collection
Probepoint
settings
draft 1
aggregation
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Gio Wiederhold IBM R 70
Interlingua(s)
Interlingua:
Query :
Object Exchange Model
Mediator Specification Language
OEM
MSL
{ OID, LABEL, TYPE, VALUE }
<document
{<author AUTHOR> <title TITLE>}:-
<biblioentry
{<author AUTHOR>}>@biblio
<inproceedings {<title TITLE>}>
@sybase
AND
AND
Equal(AUTHOR, “Jeff Ullman”)
Interlingua:
Query:
Knowledge Interchange Format
Knowledge Query and Manipulation Language
KIF
KQML
(PACKAGE :FROM ap001
:TO ap002
:CONTENT
(MSG
:TYPE query
:CONTENT-LANGUAGE KIF
:CONTENT (and (document (author@biblio ?a) (title@sybase ?t))
(eq “Jeff Ullman” ?a)))
7/26/2016
Gio Wiederhold IBM R 71
Support for KB-Algebra
• Ontolingua [Gruber, Fikes @ Stanford KSL]:
Repository for Domain Terminologies
Used for mechanical design, bibliographies, catalogs
• LOOM [MacGregor@ USC ISI]:
Classification-based Expert System
Helps in structuring and processing ontologies
• PROTÉGÉ [Musen@ Stanford MIS]
Reuse
• Penguin [Barsalou, Keller@ Stanford MIS, CIFE]:
Object manipulation based on Relational Algebra
Used for genetics laboratory, building design
7/26/2016
Gio Wiederhold IBM R 72