september 2001
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04/19/23 SKC Synopsis
September 2001
Gio Wiederhold, Shrish Agarwal, Stefan Decker, Jan Janninck, Prasenjit Mitra, et al.
Stanford University, CSD
S K C
Scalable Knowledge Composition
04/19/23 SKC Synopsis Gio Wiederhold 2
Data + Knowledge Information
• Apply relevant Knowledge to relevant Data
ObservationsQuality Filters
Analyses
• to obtain: Information for decision-making
Aggregation of instances
Selection
SKC focusComposition of source information
04/19/23 SKC Synopsis Gio Wiederhold 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
04/19/23 SKC Synopsis Gio Wiederhold 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
04/19/23 SKC Synopsis Gio Wiederhold 5
Semantic Mismatches
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
04/19/23 SKC Synopsis Gio Wiederhold 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
04/19/23 SKC Synopsis Gio Wiederhold 7
Two Mismatch Solutions
1. 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
2. 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
04/19/23 SKC Synopsis Gio Wiederhold 8
Our approach (SKC project)
1. Define Terminology in a domain precisely Schemas, XML DTDs Ontologies
2. 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
3. Develop tools to support the methods Ontology matching
04/19/23 SKC Synopsis Gio Wiederhold 9
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
The glue that holds the bricks together
04/19/23 SKC Synopsis Gio Wiederhold 10
Sample Operation: INTERSECTION
Source Domain 1:Owned and maintained by Store
Result contains shared termsArticulates the two domains
Source Domain 2:Owned and maintainedby Factory
Terms usefulfor purchasing
04/19/23 SKC Synopsis Gio Wiederhold 11
Sample Intersections
Shoe Store• Shoes { . . . }• Customers { . . . }• Employees { . . . }
size = size color =table(colcode)
style = style
Ana-tomy {. . . }
• Material inventory {...}• Employees { . . . }• Machinery { . . . }• Processes { . . . }• Shoes { . . . }
Shoe Factory
Hard-ware
Articulation ontologymatching rules :
foot = foot Employees Employees Nail (toe, foot) Nail (fastener). . . . . .
Department Store
04/19/23 SKC Synopsis Gio Wiederhold 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
* Compromises hide valuable details
Domain Ontology
04/19/23 SKC Synopsis Gio Wiederhold 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)
04/19/23 SKC Synopsis Gio Wiederhold 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
04/19/23 SKC Synopsis
INTERSECTION support
Store Ontology
Articulation ontology
Matching rules that use terms from the 2 source domains
Factory Ontology
Terms usefulfor purchasing
04/19/23 SKC Synopsis
Other Basic Operations
typically priorintersections
UNION: mergingentire ontologies
DIFFERENCE: materialfully under local control
Arti-culation ontology
04/19/23 SKC Synopsis Gio Wiederhold 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 1. remote, deep domain expertise
2. rapid recomposition for application domain
Expect also thatOperations can be composedOperations can be rearranged
Alternate arrangements can be evaluatedOptimization is enabled
04/19/23 SKC Synopsis Gio Wiederhold 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
04/19/23 SKC Synopsis Gio Wiederhold 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
04/19/23 SKC Synopsis Gio Wiederhold 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
04/19/23 SKC Synopsis Gio Wiederhold 21
Articulation Generator
Driver
Ont1 Ont2
Phrase Relator
Thesaurus
StructuralMatcher Semantic
Network(Nexus)
Context-basedWord Relator
OntA
Human Expert
Being built by Prasenjit Mitra
04/19/23 SKC Synopsis
Articulationknowledgefor U
U
U
(A B)U
(B C)U
(C E)
Principle of Knowledge Composition
Knowledge resource
B
Knowledge resource
A
Knowledge resource
C
Knowledge resource
D
U
(C D)
U
(B C)
Articulationknowledge
Composed knowledge forapplications using A,B,C,E
Knowledge resource
E
U
(C E)
Legend:
U : union
U
: intersection
Articulationknowledgefor (A B)
U
04/19/23 SKC Synopsis
Exploiting the result (future plans)
Processing & query evaluation is best performed withinSource Domains & by their engines
Result has linksto source
Avoid n2 problem of interpretermapping [Swartout HPKB year 1]
04/19/23 SKC Synopsis
Support Domain Specialization
• Knowledge Acquisition (20% effort) &• Knowledge Maintenance (80% effort *)
to be performed• Domain specialists (SMEs)• Professional organizations• Field teams
of modest size
Empowermentautomouslymaintainable
* based on experience with software
04/19/23 SKC Synopsis Gio Wiederhold 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
Society ofSociety of specialistsspecialists
Our OntologyOur Ontology
04/19/23 SKC Synopsis Gio Wiederhold 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, www.UN, www.OPEC
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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