1. February 2001 Gio Wiederhold Stanford UniversityK C
An Algebraic Approach to
Articulate Ontologies for
Information Integration
February 2001
Gio Wiederhold
Stanford University
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2. 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 implicitly) add class definition (to hold instances), constraints, and, sometimes, operations among the terms
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3. Functions of Ontologies .
Enable Precision in Understanding
People = designers, implementors, users, maintainers
Systems = implementors = users = maintainers
Share the Cost of Knowledge Acquistion & Maintenance
reuse encoded knowledge, remain up-to-date as domains change
Enable Information Interoperation *
Define the terms that link domains
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4. Ancestors of Ontologies .
Lexicons: collect terms used in informtion systems
Taxonomies: categorize, abstract, classify terms
Schemas of databases: attributes, ranges, constraints
Data dictionaries: systems with multiple files, owners
Object libraries: grouped attributes, inherit., methods
Symbol tables: terms bound to implemented programs
Domain object models: (XML DTD): interchange terms
More Knowledge formalized
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5. 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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6. 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 
Representation and Access Conventions 
Naming and Ontology 
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7. Semantic Mismatches
Information comes from many autonomous sources
Differing viewpoints (by source)
differing terms for similar items { lorry, truck }
same terms for dissimilar items trunk(luggage, car)
differing coverage vehicles (DMV, AIA)
differing granularity trucks (shipper, manuf.)
different scope student museum fee, Stanford
Hinders use of information from disjoint sources
missed linkages loss of information, opportunities
irrelevant linkages overload on user or application program
Poor precision when merged
Still ok for web browsing , poor for business & science
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8. Need for precision Information Wall
More precision is needed as data volume increases
--- a small error rate still leads to too many errors
False Positives have to be investigated
( attractive-looking supplier - makes toys
apparent drug-target with poor annotation )
False Negatives cause
lost opportunities,
suboptimal to some degree
False positives = poor precision
typically cost more than
false negatives = poor recall
data errors
information quantity
human limit
acceptable limit
human with tools?
Information Wall
adapted from Warren Powell, Princeton Un.
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9. Ontology Sharing Three Alternatives
Create a committee to define everybody’s terms
Takes many years, until people are worn out
Ignored when changes make deviation necessary
Get all terms and put them into large model
[ Cyc, UMLS, Federated Schemas, ]
Can be rapid
Ignores conflicts
Hard to maintain (requires committee)
Keep all Terms distinct, except where sharing
Requires initial effort
Empowers participants
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10. Proposed Language Solutions
Specify and define terminology usage: ontology
Domain-specific ontologies XML DTD assumption
Small, focused, cooperating groups
high quality, some examples - genomics, arthritis, Shakespeare plays
allows sharable, formal tools
ongoing, local maintenance affecting users - annual updates
poor interoperation, users still face inter-domain mismatches
Cannot achieve globally consistency
wonderful for users and their programs
too many interacting sources
long time to achieve, sources (UAL, BA), 3 (+ trucks), 4, … all ?
costly maintenance, since all sources evolve
no world-wide authority to dictate conformance
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11. An unsolved 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
2. inefficient -- languages are efficient in local contexts
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12. General 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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13. Structural Heterogeneity
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14. 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
context is implicit
No committee is needed to forge compromises * within a domain
Domain Ontology
Compromises hide valuable details
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15. SKC grounded 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:
an entity instance with a physical manifestation
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16. Domain-specific Expertise .
Knowledge needed is huge
Partition into natural domains
Determine domain responsibility and authority
Empower domain owners
Provide tools
Consider interaction
Society of
specialists
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17. An Ontology Algebra A knowledge-based algebra for ontologies
The Articulation Ontology (AO) consists of matching rules that link domain ontologies
Intersection create a subset ontology
keep sharable entries
Union create a joint ontology
merge entries
Difference create a distinct ontology
remove shared entries
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18. Sample Operation: INTERSECTION
Result contains
shared terms
Terms useful
for purchasing
Source Domain 1:
Owned and maintained
by Store
Source Domain 2:
Owned and maintained
by Factory
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19. INTERSECTION support Articulation ontology Matching rules that use
terms from the
2 source domains
Terms useful
for purchasing
Store
Ontology
Factory
Ontology
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20. color =table(colcode)
Sample Intersections
Articulation
ontology
matching rules :
size = size
color =table(colcode)
style = style
Ana-
tomy
Material inventory {...}
Employees { }
Machinery { }
Processes { }
Shoes { }
Shoe Factory
Shoe Store
Shoes { }
Customers { }
Employees { }
{. . . }
Hard-
ware
foot = foot
Employees Employees
Nail (toe, foot) Nail (fastener)
Department
Store
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21. Other Basic Operations
UNION: merging
entire ontologies
DIFFERENCE: material
fully under local control
Arti-
culation
ontology
typically prior
intersections
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22. 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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23. Sample Processing in HPKB
What is the most recent year an OPEC member nation was on the UN security council (SC)?
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)
Other groups obtained more,
but factually wrong answers
Problems resolved by SKC
Factbook – a secondary source -- 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
needed ancillary data
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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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25. Semi-automatic approach
Provide library of automatic match heuristics
Lexical Methods -- spelling
Structural Methods -- relative graph position
Reasoning-based Methods
Nexus 
Hybrid Methods
Iterative/Non-iterative Methods
GUI tool to
- display matches and
- verify generated matches using human expert
- expert can also supply matching rules
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26. Articulation Generator
Being built by Prasenjit Mitra
Thesaurus
OntA
Context-based
Word Relator
Phrase Relator
Driver
Semantic
Network
(Nexus)
Structural
Matcher
Ont1
Ont2
Human Expert
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27. Lexical Methods Preprocessing rules. -Expert-generated seed rules.
e.g., (Match O1.President O2.PrimeMinister)
-Context-based preprocessing directives.
Thesaurus - synonyms, relationships
Distance of words as measure of relatedness.
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28. Tools to create articulations
registration
Vehicle
ontology
Vehicle
ontology
sales
Combine ontology graphs
with expert selection
based on spelling,
graph matching, and
a nexus derived from
a dictionary (O.E.D.)
Suggestions
for articulations
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29. Tools to create articulations
Graph matcher
for
Articulation-
creating
Expert
Vehicle
ontology
Transport
ontology
Suggestions
for articulations
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30. continue from initial point
Also suggest similar terms
for further articulation:
by spelling similarity,
by graph position
by term match repository
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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31. Candidate Match Repository
Term linkages automatically extracted from 1912 Webster’s dictionary *
* free, other sources have been processed.
Based on processing headwords ý definitions using algebra primitives
Notice presence of 2 domains: chemistry, transport
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32. Using the match repository
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33. Navigating the match repository
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34. Relative Arc Importance
PageRank (Google) limitations
node oriented
high rank to words with little semantic value
conjunctions, articles And The
prepositions, pronouns to it
Relative arc importance
contribution of source rank to target rank
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35. ArcRank For All source s and target t nodes in graph
sort outgoing , rank by sorted order
sort incoming , rank by sorted order
for each arc compute
In ranking
Equal values take same rank
Ranks numbered consecutively
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36. All Pairs Similarity Compute similarity value for all node pairs
product of inbound arc importance vectors
product of outbound arc importance vectors
similarity =
Similarity Matrix
Initial state: nodes similar only to themselves
Node substitution: terms replace similar ones
Iterative convergence: bounded substitution
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37. Contents Examples Verb (Educate) Adverb (Ever)
Proper Noun (Scotland -- undefined term
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38. Examples (Verb)
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39. Examples (Adverb)
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40. Examples (Proper Noun)
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41. Country Graphs
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42. To be matched to
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43. Knowledge Composition
Composed knowledge for
applications using A,B,C,E
Articulation
knowledge
for U (A B) (B C) (C E)
Articulation
knowledge
Legend:
U : union U
: intersection U (C E)
Articulation
knowledge
for
Knowledge
resource E
Knowledge
resource C (A B) U U (B C) U (C D)
Knowledge
resource A
Knowledge
resource B
Knowledge
resource D
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44. Primitive Operations Model and Instance 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
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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45. Future: exploiting the result
Avoid n2 problem of interpreter
mapping as stated by Swartout
as an issue in HPKB year 1
Result has links
to source
Processing & query evaluation is best performed within Source Domains & by their engines
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46. 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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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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48. 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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49. Backup Viewgraphs
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50. 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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51. 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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52. 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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53. 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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54. Information Flow for Training Initiative
sample
scenarios
ISI scenario language
doctrine
TRADOC
Scenarios
scenario
justification
scenario
refinement
Objectives
trainer /
controller
mediator
knowledge
base
aggregation/
analysis/
evaluation
Requirements
exercise
design
Legend
Data
collection
Probe-
point
settings
sources
tasks
draft 1
explosion
aggregation
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55. Interlingua(s) Interlingua: Object Exchange Model OEM
Interlingua: Knowledge Interchange Format KIF
Query: Knowledge Query and Manipulation Language KQML
(PACKAGE :FROM ap001
:TO ap002
:CONTENT
(MSG
:TYPE query
:CONTENT-LANGUAGE KIF
:CONTENT (and (document ?a) ?t))
(eq “Jeff Ullman” ?a)))
Interlingua: Object Exchange Model OEM
Query : Mediator Specification Language MSL
<document {<author AUTHOR> <title TITLE>}:-
<biblioentry {<author AND
<inproceedings {<title TITLE>}> @sybase AND
Equal(AUTHOR, “Jeff Ullman”)
{ OID, LABEL, TYPE, VALUE }
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56. Support for KB-Algebra
Ontolingua [Gruber, Stanford KSL]:
Repository for Domain Terminologies
Used for mechanical design, bibliographies, catalogs
LOOM USC ISI]:
Classification-based Expert System
Helps in structuring and processing ontologies
PROTÉGÉ Stanford MIS] Reuse
Penguin [Barsalou, Stanford MIS, CIFE]:
Object manipulation based on Relational Algebra
Used for genetics laboratory, building design
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