Intelligent Information Systems
8. Educational Challenges
Gio Wiederhold
EPFL,
April-June 2000, at 14:15 - 15:15, room INJ 289
7/26/2016
EPFL - Gio spring 2000
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Schedule
Presentations in English -- but I'll try to manage discussions in French and/or German.
1. 13/4 Historical background, enabling technology:ARPA, Internet, DB, OO, AI., IR
2. 27/4 Search engines and methods (recall, precision, overload, semantic problems).
3. 4/5 Digital libraries, information resources. Value of services, copyright.
4. 11/5 E-commerce. Client-servers. Portals. Payment mechanisms, dynamic pricing.
5. 19/5 Mediated systems. Functions, interfaces, and standards. Intelligence in
processing. Role of humans and automation, maintenance.
6. 26/5 Software composition. Distribution of functions. Parallelism. [ww D.Beringer]
7. 31/5 Application to Bioinformatics.
8. 15/6 Educational challenges. Expected changes in teaching and learning.
9. 22/6 Privacy protection and security. Security mediation.
10.29/6 Summary and projection for the future.
• Feedback and comments are appreciated.
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EPFL - Gio spring 2000
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Open question?
• Web enables remote education
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EPFL - Gio spring 2000
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Stanford Model
• Based on TV courses offered to industry
• Part of normal curriculam
– TV operator in special classroom
shows notes (must be legible), blackboard, teacher
– tutor at remote site (has taken class earlier)
– voice link for questions (if live TV)
• Can be replayed on web in students rooms, …
– morning classes getting to be empty
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EPFL - Gio spring 2000
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Threat to smaller schools
Alternatives
• Overloaded professor with older material
• Inaccessible professor with up-to-date material
– technology from the entertainment industry
• Education when and where wanted
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EPFL - Gio spring 2000
5
HPKB Master file on Birch
S
K
C
Scalable Knowledge Composition
September 1997
Gio Wiederhold
Stanford University
An abstract concept is like a valise with a false bottom.
you may put in what you please, and take them out again,
without being observed.
Alexis de Toqueville, Democracy in America, 1838.
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What are Ontologies?
Ontologies list the terms and their relationships that
allow communication among partners in
enterprises (in machine-readable form)
Relationships determine meaning - parent, school, company
Databases use ontologies during design
in
their E-R diagrams
(Implicitly)
and
represent the leaf nodes in their schemas
Knowledge-bases use ontologies (often implicitely)
add class definition (to hold instances), constraints, and
operations among the terms
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Functions of Ontologies
.
• Define Terms used in System Construction
to enable Correctness in Understanding
system = designers, implementors, users, maintainers
designers = implementors = users = maintainers
• Define Higher-level Abstractions needed
to communicate in larger contexts
managers, decision-makers, systems in own, other domains
• Share the Cost of Knowledge Acquistion &
Maintenance
reuse encoded knowledge, remain up-to-date as domains change
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Ancestors of Ontologies
 Lexicons: collect terms used in inform. systems
 Taxonomies: categorize, abstract, classify terms
 Schemas of databases: attributes, ranges filed
 Data dictionaries: integration of files, attributes
 Object libraries:
grouped attributes, methods
 Symbol tables: collect terms used in a program
 Domain object models:
 . . . More Knowledge
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re-engineering terms
Establishing Ontologies
Top-down:
– Commonly acceptable UPPER layers
Domain-specific
– Sharing tools
– Object based
Bottom-up
– Pragmatic, TASK-specific collections
– Database schemas and models
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IFIP note
Ich weiss nicht was soll es bedeuten,
...
-- an early complaint about semantics
[Heinrich Heine: Die Lorelei]
Ontologies in Use
Implicit Ontologies are a prerequisite for
communication among humans and organizations.
Knowledge is explicitely represented in AI-systems;
sometimes the ontology is explicit as well.
Database schemas are partial explicit ontologies
• Relational schemas only terms & 1:1 dependencies.
• E-R designs contain 1:n, m:n cardinalities
• Structural schemas contain semantic dep. types
Conceptual graphs define terms of discourse and
a modest number of relationship types
Variables in software represent ontologies poorly.
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Ontologies at work
per Hans Akkermans (VU Amsterdam, consulting)
• Knowlegde elicitation for experts
– tacit knowledge in organizations
• PDES/STEP annotation
• adding knowlegde to processes [Unilever]
• Software requirements engineering
–
–
–
–
what does the cient really want
definition of domain content for CS folk
reuse across very disparate domains [viz Musen]
relates to OO work and recognition of patterns
– distributed service integration (AMR, DA,
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Large Ontologies?
 Have all the Knowledge together
+ simple for customers of KBs
– hard for owners of KBs
 Large KB will cover multiple domains
 created by a committee -- slow
 maintained by a committee -- costly
 Differences in level of abstraction -- efficiency
 homeowner: nail
 carpenter: sinker, brad, boxnail, . . .
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SKC Objective
Provide for Maintainable Ontologies
• devolve maintenance onto many
domain-specific experts / authorities
• provide an algebra to compute
composed ontologies that are
limited to their articulation terms
• enable interpretation within the source
contexts
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SKC
SKC Working 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
• Real-world object:
an entity instance with a physical manifestation
• Abstract object:
a concept which refers to other objects
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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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We consider to be ontologies:
• Object oriented class hierarchies,
(snapshots of executing programs capture object instances)
• Database schemas,
(via their E-R or structural models)
• Semi-structured databases,
(OEM <OID, label, type, value>)
• Definitional thesauri,
(UMLS: see http://www.lexical.com)
•
Knowledge bases (CYC, Ontolingua)
SKC specifically does not restrict its applicability to a purely extensional (object) or
intensional (schema) definition of ontology, since its purpose is to support useful
processing of extensions using intensional knowledge for all parties. To that end it is
important that the intensional specifications include predicates or methods that permit
the collection of extensional access to real-world objects.
We do not require ontologies to be complete specifications of a domain, but rather that
usage of an ontology provide results complete with respect to the ontology.
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Aspects that Focus SKC
• The mapping of terms to objects differs between
autonomous domains.
• The collections of real-world objects provides a
grounding for the definitions, and an opportunity for
validation of the meaning of the terms being
employed.:
• Relationships have semantic, and derived from that,
structural significance. Multiple relationship types
may share structural characteristics, as IS-A,
Ownership, Part-of, Reference,
• We will keep the number of primitive relationships
limited,
• The mapping of relationship types differs between
autonomous domains.
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Heterogeneity among Domains
If interoperation involves distinct
domains mismatch ensues
• Autonomy conflicts with consistency,
– Local Needs have Priority,
– Outside uses are a Byproduct
Heterogeneity must be addressed
• Platform and Operating Systems 4 4
• Representation and Access Conventions 4
• Naming and Ontology :
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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
rules that link domain ontologies
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matching
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
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
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)
...
Other Basic Operations
DIFFERENCE: material
fully under local control
UNION: merging
entire ontologies
Articulation
ontology
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typically prior
intersections
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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Knowledge Composition
Composed knowledge for
Articulation
knowledge
Legend:
U
U
for
applications using A,B,C,E
(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
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
Exploiting the result
Result has links
to source
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.
Avoid n2 problem of interpreter
mapping as stated by Swartout
as an issue in HPKB year 1
Processing & query
evaluation is best
performed within
Source Domains
& by their engines
SKC Synopsis
• Research: Reliable query answers from
heterogeneous, imperfect data sources
• Sources:
– General: CIA World Factbook ‘96, UN WWW
– Topical: OPEC, BattleSpace Sensors
• Client: DARPA High Performance Knowledge Base
(HPKB) project
• Theory: Rule-based algebra
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– Translation
& Composition primitives
•
•
•
•
Innovation in SKC
No need to harmonize full ontologies
Focus on what is critical for interoperation
Rules specific for articulation
Potentially many sets of articulation rules
• Maintenance is distributed
– to n sources
– to m articulation agents
is m < n2 , depending on architecture
density a research question
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Domain Specialization
.
• Knowledge Acquisition (20% effort) &
• Knowledge Maintenance (80% effort *)
to be performed
• Domain specialists
• Professional organizations
• Field teams
of modest size
automously
maintainable
7/26/2016*
Empowerment
based on experience with software
Rules for Real-Time
Data
if [base_station.receiving] = true
then satellite_data = [base_station]
satellite_data.timestamp = now
if [satellite_data.age] < 24 hours
or [radio_jamming.level] > 30%
then recon_data = [satellite_data]
except when [flight_data.age] < 1 hour
or [rain_sensor.daytotal] > 1 inch
then recon_data = [flight_data]
assert [recon_data]
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Sample Processing in
HPKB
• What is the most recent year an
OPEC member nation was on
the UN security council?
– Related to DARPA HPKB
Challenge Problem
– SKC resolves 3 Sources
• CIA Factbook ‘96 (nation)
• OPEC (members, dates)
• UN (SC members, years)
– SKC obtains the
Correct Answer
• 1996 (Indonesia)
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– Problems resolved by SKC
* Factbook has out of date
OPEC & UN SC lists
– 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
• intent of original question
– Temporal variants
Status
September 1997
• Base HPKB funding from AFOSR
– New World Vistas
– some industrial co-funding
• Prior work supported through Commercenet
– support for common representation, an interlingua
• Acquiring ontologies that
–
–
–
–
are interesting to HPKB projects
not trivial, I.e., represent realistic activities
intersectable
Logistics: DoD CIM, CIA, Cyc, . . .
• Starting smart students
• Integrating into architecture managed by TFS
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.
Information Flow for Training Initiative
sample
scenarios
scenario
refinement
trainer /
controller
aggregation/
analysis/
evaluation
ISI scenario language
Scenarios
Objectives
tasks
explosion
aggregation
doctrine
TRADOC
mediator
knowledge
base
Requirements
exercise
design
Legend
sources
scenario
justification
Data
collection
Probepoint
settings
draft 1
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
(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)))
KIF
KQML
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
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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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•
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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