1. Stanford University, CSDK C
Scalable Knowledge Composition
September 2001
Gio Wiederhold, Shrish Agarwal, Stefan Decker, Jan Janninck, Prasenjit Mitra, et al.
Stanford University, CSD
12/7/2018
SKC Synopsis

2. Data + Knowledge  Information
Apply relevant Knowledge to relevant Data
Analyses
Composition of source information
SKC focus
Aggregation
of instances
Selection
Observations
Quality Filters
to obtain: Information for decision-making
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SKC Synopsis

3. Many sources, disciplines, people
Extraction of actionable information, so that future benefits can accrue, requires broad-based knowledge
Areas make deep progress in isolation
Benefits are possible when
solid results are available
the results are Integrated or Composed
Broad base leads to heterogeneity and inconsistency of terminologies
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SKC Synopsis

4. Language differences inhibit integration
An essential feature of science
autonomy of fields
differing granularity and scope of focus
growth of fields requires new terms
A feature of technological process
standards require stability
yesterday’s innovations are today’s infrastructure
today’s innovations are tomorrow’s infrastructure
Must be dealt with explicitly
sharing, integration, and aggregation are essential
large quantities of data require precision
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SKC Synopsis

5. Semantic Mismatches Ok for web browsing , poor for business & science
Autonomous sources in all domains have
Differing viewpoints ( by source )
differing terms for similar items { lorry, truck }
same terms for dissimilar items trunk( luggage, car)
differing coverage vehicles ( DMV, police, AIA )
differing granularity trucks ( shipper, manuf. )
different scope student ( museum fee, Stanford )
different hierarchical structures supplier vs. usage 
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
Ok for web browsing , poor for business & science
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SKC Synopsis

6. Heterogeneity among Domains is natural
Interoperation creates mismatch
Autonomy conflicts with consistency,
Local Needs have Priority,
Outside uses are a Byproduct
Heterogeneity must be addressed
Platform and Operating Systems  
Data Representation and Access Conventions 
Metadata: Naming and Ontology 
needed to share data from distinct sources
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SKC Synopsis

7. Two Mismatch Solutions
A Single, Globally consistent Ontology ( Your Hope )
wonderful for users and their programs
too many interacting sources
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
Domain-specific ontologies ( XML DTD assumption )
Small, focused, cooperating groups
high quality, some examples - arthritis, Shakespeare plays
allows sharable, formal tools
ongoing, local maintenance affecting users - annual updates
poor interoperation, users still face inter-domain mismatches
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SKC Synopsis

8. Our approach (SKC project)
1. Define Terminology in a domain precisely
Schemas, XML DTDs  Ontologies
Develop methods to permit interoperation among differing domains (not integration)
Articulation --- support the limited interoperation needed to solve problems in an application domain
Ontology Algebra --- enable scalability to as many sources as are needed to support applications
Develop tools to support the methods
Ontology matching
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SKC Synopsis

9. An Ontology Algebra A knowledge-based algebra for ontologies
The glue that holds the bricks together
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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SKC Synopsis

10. Sample Operation: INTERSECTION
Result contains
shared terms
Articulates the
two domains
Terms useful
for purchasing
Source Domain 1:
Owned and maintained
by Store
Source Domain 2:
Owned and maintained
by Factory
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SKC Synopsis

11. 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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SKC Synopsis

12. Within a Domain Terms have clear Meanings
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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SKC Synopsis

13. 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
(or its representation in a factual database)
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SKC Synopsis

14. Grounding enables implementation
We use many abstract terms in our work
Needed because we are dealing with many objects
Human thinking is limited to short-term memory
Someone must be able to translate them into code reliably
Each abstract term must have a path to reality
One must provide that path for
students and
coders
Without a clear path that is not possible
Not automatically at all – machines need specs
Not reliably by human programmers – failures occur
Without implementation there is no benefit
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SKC Synopsis

15. 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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SKC Synopsis

16. Other Basic Operations
UNION: merging
entire ontologies
DIFFERENCE: material
fully under local control
Arti-
culation
ontology
typically prior
intersections
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SKC Synopsis

17. Features of an algebra The record of past operations can be
kept and reused
(experience: 3 months  1 week for Webster's annual update,
 2 weeks for OED (6 x size) [Jannink:01])
Maintenance is enabled by using
remote, deep domain expertise
rapid recomposition for application domain
Expect also that
Operations can be composed
Operations can be rearranged
Alternate arrangements can be evaluated
Optimization is enabled
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SKC Synopsis

18. Sample Processing in the DARPA HPKB challenge
What is the most recent year an OPEC member nation was on the UN security council (SC)?
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;
they relied on one global source, the CIA factbook.
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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SKC Synopsis

19. Interoperation via Articulation
Process phases:
 At application definition time
Match relevant ontologies where needed
Establish articulation rules among them.
Record the process
 At execution time
Perform query rewriting to get to sources
Optimize based on the ontology algebra.
 For maintenance
Regenerate rules using the stored formulation
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SKC Synopsis

20. Generation of the articulation rules
Provide library of automatic match heuristics
Lexical Methods – spelling similarity --- commonly used by others
Structural Methods -- relative graph position
Reasoning-based Methods
Nexus – a graph we derive from the OED / Websters
links terms based on definitions, not lexical similarity
Hybrid Methods
Iteratively, with an expert in control
GUI tool to
- display matches and
- verify generated matches using the human expert
- expert can also supply matching rules
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SKC Synopsis

21. 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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SKC Synopsis

22. Principle of 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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SKC Synopsis

23. Exploiting the result (future plans)
Avoid n2 problem of interpreter
mapping [Swartout HPKB year 1]
Result has links
to source
Processing & query
evaluation is best
performed within
Source Domains
& by their engines
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SKC Synopsis

24. Support Domain Specialization
Knowledge Acquisition (20% effort) &
Knowledge Maintenance (80% effort *)
to be performed
Domain specialists (SMEs)
Professional organizations
Field teams
of modest size
Empowerment
automously
maintainable
* based on experience with software
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SKC Synopsis

25. Domain-specific Expertise .
Knowledge needed is huge
Partition into natural domains
Determine domain responsibility and authority
Empower domain owners
Exploit domain-specific expertise
Provide computer-science tools
Consider interaction
Our Ontology
Society of
specialists
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SKC Synopsis

26. SKC Project Synopsis Research Objective:
Precise information for applications from
heterogeneous, imperfect, scalably many data sources
Sources for Ontologies used currently:
General: CIA World Factbook ‘96,
Webster’s Dictionary, Thesaurus, Oxford English Dictionary
Topical: NATO, BattleSpace Sensors, Logistics Servers
Theory:
Domain autonomy and exploitation
Rule-based algebra over ontologies
Translation & Composition primitives
Sponsors and collaboration
AFOSR; DARPA DAML program; W3C; Stanford KSL and SMI; Univ. of Karlsruhe, Germany; others.
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SKC Synopsis