The document discusses the rediscovery of knowledge engineering and its relevance for developing semantic web applications using reusable reasoning patterns. It identifies seven semantic web tasks and five inferences, outlining a methodology aimed at enhancing the design and implementation of such applications. The authors emphasize the importance of these patterns to enable higher-level analysis and effective component reuse.

1. Knowledge Engineering rediscovered:
Towards Reasoning Patterns
for the Semantic Web
Frank van Harmelen, Annette ten Teije,
Vrije Universiteit Amsterdam
Holger Wache
Univeristy of Applied Sciences Northwestern
Switzerland
Paper at
http://www.cs.vu.nl/~frankh/postscript/KCAP09.pdf
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2. Knowledge Engineering rediscovered:
Towards Reasoning Patterns for the Semantic Web
Lessons from Knowledge Engineering
– How to build knowledge-based systems
out of reusable components
(CommonKADS, Generic Tasks)
– All based on Newell’s work on
Knowledge-level / Symbol-level reasoning
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3. Knowledge Engineering rediscovered:
Towards Reasoning Patterns for the Semantic Web
• Research question:
– Can we identify reusable patterns and
components that can help designers and
implementers of SW applications?
– First step towards a methodology for building
semantic web applications out of reusable
components
• Notice:
– Existing work on reusable ontology patterns,
but little work on reusable reasoning patterns
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4. Overview
• KE best practices: CommonKADS
• Identify Semantic Web Tasks
• Validation of those tasks
• Identify Inferences
• Quasi-validation of those inferences
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5. Knowledge Categories
Task Knowledge
• Goal + method
• Decomposition
• Control
Inference knowledge
• Functions
• Roles
• Domain mapping
Domain Knowledge
• Scheme
• Knowledge
• Facts
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6. Example of inference structure:
Diagnosis
hypothesis specify observable
complaint select obtain
cover hypothesis verify finding
result
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7. Can we identify reusable semantic web
tasks and inferences?
• Seven tasks, defined by:
– Input/output types
– Definition of functionality
– Decomposed into inferences
• Five inferences, defined by:
– Input/output types
– Definition of functionality
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8. 1. Search task (semi-formal)
Input: query concept
Knowledge: ontology = schema + instance set
Output: members of the instance set
• Search: c x O → I’
• Input: a query concept c
• Knowledge: T(O) (set of 〈ci,⊆,cj〉) and
I(O) (set of 〈i,∈,ck〉)
• Output: search(c,O) =
{〈i,∈,c〉|∃cj: 〈cj,⊆,c〉 ∈ T(O) ∧ 〈i,∈,cj〉 ∈ I(O))}
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9. Is this the only possible
definition?
• Other definitions are possible and reasonable
• Alternative:
O |-- 〈cj,⊆, c〉 ∈ T(O) ∧ O |-- 〈i,∈,cj〉 ∈ I(O))}
• Leads to a typology of search tasks
• Helps implementers:
– Makes choices explicit
– Reusable components
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10. Other Tasks
2. Browse:
– input: a concept
– knowledge: ontology + instance set
– output: members of the instance set or concepts
of the ontology
2. Data integration:
– input: multiple ontologies with their instance sets
– output: a single ontology and an instance set
2. Personalisation and recommending
– input: instance set plus profile
– knowledge: ontology
– output: reduced instance set
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11. Other Tasks
5. Web-service selection:
– input: required functionality
– Knowledge: ontology + set of instances of
services
– Output:
5. Web-service composition:
– input: required functionality
– Knowledge: ontology + set of instances of
services
– output: compositions of service instances
5. Semantic Enrichment:
– input: an instance
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– output: set of triples with the given instance as

12. Validation of tasks:
how complete are the 7 tasks?
• We classified all (38) Semantic Web
Challenge applications of 2005-2007
• 37 applications could be classified
• Often, a single application belongs to
multiple tasks
• We found no web-service selection,
but some web-service composition
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13. question answering
web-service compo
Validation of tasks on Semantic web challenges 2005-2007
semantic enrichme
web-service selecti
data integration
Personalization
Application Use Cases
browse
search
ArnetMiner (2007) x x x
Cantabria (2007) x x x
CHIP (2007) x x
COHSE (2006) x x x
Collimator (2006) x
CONFOTO (2005) x x
Dartgrid (2006) x x
Dbin (2006) x x ~
DORIS (2007) x x
DynamicView (2005) x x
EachWiki (2007) x
EKOSS (2006) x
eMerges (2006) x
Falcon-S (2006) x x
Foafing the Music (2006) x
FungalWeb (2005) x x
Geospatial Semantic Web Services (2006) x
GroupMe (2007) x
iFanzy (2007) x x x
Int.ere.st (2007) x
JeromeDL(2007) x x
MediaWatch (2007) x x x
mle (2007) x x
MultimediaN E-Culture (2006) x x x
Notitio.us (2007) x x
Oyster (2005) x
Paperpuppy (2006) x
Personal Publication Reader (2005) x x x
Potluck (2007) x x
Revyu (2007) x x
RKB Explorer (2007) x
Semantic MediaWiki (2006) x
SemClip (2007) x
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SMART (2007) x 13
swse (2007) x x
Web Services Execution Environment (2005) x
wwwatch (2007) x x

14. Five Primitive Inferences
1. Realisation:
– Given an instance, find the concepts of
which it is a member
– Instance x Ontology → concept (o |-- i ∈ c)
1. Subsumption:
– Determining whether one concept is a
subset of another
– C1 x C2 x Ontology → boolean
(Ontology |-- C1 ⊆ C2)
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15. Five primitive inferences
3. Mapping:
– Finding correspondence relation between two concepts
– Concept x Concept → relation
3. Retrieval:
– Inverse of realisation: which instances belong to a
given concept
– Concept → instances (i ∈ c)
3. Classification:
– Determining where a given class should be placed in a
subsumption hierarchy
– Concept x hierarchy → <C1,C2>
(c1 ⊆ concept ⊆ c2)
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16. Decomposing Tasks in
Basic Inferences
Example:
Search =
Classification: locate the query
concept in the ontology in order to find
its direct sub- or super-concepts
+
Retrieval: to determine the instances of
those sub- and super-concepts, which
form the answers to the query.
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17. Inference structure
for Search
C Classify Subconcepts
T
Retrieve
I
Instances
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18. Decomposing Tasks in
basic Inferences (2)
Example 2:
Personalisation (eg. based on previously bought
items) =
Realisation: to obain the concepts that
describe these items
+
Classification: to find closely related
concepts
+
Retrieval: to obtain instances of such related
concepts, since these instances might of be
interest to the user
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19. Inference structure
for Personalisation
Iprofile realise concepts classify subconcepts
T
Retrieve
Idata
Instances
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20. Quasi-Validation of inferences
Task types Primitive inference steps
realisation Subsumption mapping retrieval
&
classification
search x x
browse x x x
Data x x x
integration
personalisation x x x
service x x
selection
Service x
composition
Semantic
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enrichment

21. Conclusion
• First step towards a methodology for
building semantic web applications out
of reusable components:
tasks and inferences
• Attempt at a knowledge level
description of semantic web reasoning
• Enables analysis and reuse at a higher
level of abstraction.
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22. Next steps
• Further investigation of set of inferences
and tasks:
– E.g. Right level of inferences (too DL-driven?)
– E.g. More complex task definitions
• Model real applications as inference
structures.
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23. Our hope is that you will...
Iprofile realise concepts classify subconcepts describe your
T
application at this level
Retrieve
of abstraction!
Idata
Instances
C Classify Subconcepts
T
Retrieve
I
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