Toward a Cooperatively Built Ontology of Knowledge Engineering

1
Toward a Cooperatively Built Ontology of
Knowledge Engineering

• Dr Philippe Martin1,   Dr Michel Eboueya2
• 1 Griffith University,   2 La Rochelle
  Universitye-mail pm at phmartin dot info
• My research question how to organise and share
  information in an efficient and scalable way?

2
Plan

• Need for a Well-Structured Cooperatively-Updated
  Semantic Network

• Minimal Requirements

• The "Semantic Web Topics Ontology" of ISWC 2006

• Example of Details about Processes

• Example of Details about Structures

• Example of Argumentation Structures

• Conclusion

3
Need for a Well-Structured Cooperatively-Updated
Semantic network
How should information be written and shared to
permit 1) the precise and scalable organisation
of information 2) the efficient, precise and
complete retrieval and comparison of information

• What are the characteristics of the various
  theories and implemented parsers related to
  Functional Dependency Grammar and how do these
  theories and parsers respectively compare to
  each other?

• What are all the tasks that should be done in
  software engineering according to the various
  existing "traditional system development life
  cycle" models?

• What are the arguments and objections for the
  use of an XML-based format for the exchange
  of knowledge representations?

- Not in informal/formal documents no/poor
conceptual organisation- Not in a database
predefined, implicit conceptual organisation-
Not in an informal network (topic hierarchy/map)
poor conceptual organisation
4
Minimal Requirements

• A large lexical ontology and an initial well
  organized domain ontology

• Knowledge modelling guidelines, for example
  - whenever possible, use singular nouns for
  concept/relations names - whenever possible,
  use "subtype" relations instead of "instance"
  relations - whenever possible, use basic
  relations (especially transitive ones such as
  "subtask")Relations such as "proposes" and
  "proposedBy" are typical of nowadays schemas.

• Concise, expressive and normalising textual/
  notations

• Methods to detect and resolve redundancies and
  inconsistencies. Example
• any wnbird is pmagent of a
  wnflight'(John) has for pmcorrective_special
  ization   most wnhealthy wnFrench wnbird
  are able to be pmagent of a pmflight'
  '(Joe).

• Procedures to value contributions and
  contributors, and thus ease knowledge filtering

5
The Semantic Web Topics Ontology of ISWC 2006
RDFOWL ontology of "topics", "techniques" and
"projects", built via Protege and a wiki, for
document indexation purposes. Extract translated
in FL Knowledge_Representation
topic_usesTechnique neural_networks
heuristic_question_answering, topic_usedIn
"library classification" "information
processing", topic_relatedTo
Knowledge_Discovery,
(Artificial_Intelligence
topic_supports Web_Services_Composition,
topic_usedIn "Pattern Recognition"
"Computer
Vision", topic_subtopic
Machine_Learning)
6
Example of Details about Processes
kmKM_task__knowledge_management__KM__knowledge_en
gineering__KE supertype isinformation_sciences
_task, subtype kmknowledge_comparison
kmknowledge_inference/reasoning/generation
kmknowledge_validation
kmknowledge_representation
kmknowledge_retrieval_task
kmknowledge_sharing
kmknowledge_mapping/merging/federation
kmlanguage/structure_specific_task, object
kmKBkmknowledge_inference/reasoning/generatio
n subtype kmgeneralizing kmspecializing
kmanalogy_making kmmonotonic_reasoni
ng kmnon_monotonic_reasoning
kmconsistent_inference kminconsistent_inferenc
e kmcomplete_inference
kmincomplete_inference
kmstructure-based_inference kmrule_based_infer
ence, subtask (kmknowledge_comparison
subtype (kmgraph_matching subtype
kmCG_matching))kmgeneralizing__generalization
__generalising__generalisation subtype
kmdeduction kmabduction kminduction
7
Example of Details about Structures
kmKM_structure supertype issymbolic_structure
, subtype kmbase_of_facts/beliefs
kmontology kmKB_category
kmKB_statement kmKB kmKA_model
kmKR_language kmlanguage_specific_str
ucturekmCG_structure supertype
kmlanguage_specific_structure, subtype
kmCG_statement kmCG_language
8
Example of Argumentation Structures
"XML is useless for knowledge representation,
exchange or storage" argument ("using XML
tools for KBSs is a useless additional task"
argument "KBSs do not use XML internally"
(pm, objection "XML can be used for
knowledge exchange or
storage" (joe, objection "it is as
easy to use other formats for
knowledge exchange or storage" (pm),
objection "a KBS (also) has to use other
formats for knowledge
exchange or storage" (pm))) )(pm)
9
Example 2 of Argumentation Structures
"XML can be used for knowledge exchange or
storage" argument - "an XML notation permits
classic XML tools (parsers, XSLT,
...) to be re-used" (pm) - "classic
XML tools are usable even if a
graph-based model is used" (pm), argument of
("a KRL should (also) have an XML notation",
specialization "the Semantic Web
KRL should
have an XML notation" (pm),
specialization of "a KRL can have an
XML notation" (pm)
)(pm)
10
Conclusion

• A large lexical ontology and an initial well
  organized domain ontology

• Knowledge modelling guidelines, for example
  - whenever possible, use singular nouns for
  concept/relations names - whenever possible,
  use "subtype" relations instead of "instance"
  relations - whenever possible, use basic
  relations (especially transitive ones such as
  "subtask")Relations such as "proposes" and
  "proposedBy" are typical of nowadays schemas.

• Concise, expressive and normalising textual/
  notations

• Methods to detect and resolve redundancies and
  inconsistencies. Example
• any wnbird is pmagent of a
  wnflight'(John) has for pmcorrective_special
  ization   most wnhealthy wnFrench wnbird
  are able to be pmagent of a pmflight'
  '(Joe).

• Procedures to value contributions and
  contributors, and thus ease knowledge filtering

11
Annex 1 Querying
Category querying WebKB permits to find
categories (types or instances) according to
their names, creators, relations connected to
them, and permits to display all the objects
(categories and statements) directly or
indirectly connected to them on a single screen.
Most other tools impose much more browsing to
access information and hence make itdifficult to
retrieve and compare information in any realistic
amount of knowledge.
Category comparison WebKB permits to find which
relations exist between two given categories
(this feature is sometimes useful but not very
common).
Statement querying FCG (instead of FL) can be
used to express and retrieve statements that are
more complex than relations between categories.
Various search operators are provided "spec",
"gen", "?" (a combination of "spec" and "gen"),
etc. ? a person, agent of a sell Ned, agent
of (a sell, object a car)(pm, 21/2/2001) 3
cars, object of (2 sells, agent Ned,
time21/1/2001)(pm,12/7/2005) John, believer
of not Ned, agent of a sell(jj,3/12/2004)
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Annex 2 Comparing statements in a scalable way

• compare pmWebKB-2 kmOntolingua on
• (support of a isIR_task, output_language
  a kmKR_notation,
• part a isuser_interface), maxdepth 5
• 
  WebKB-2 Ontolingua
• support of
• isIR_task
  
• islexical_search
  
• isregular_expression_based_search
  .
• kmknowledge_retrieval_task
  .
• kmgeneralization_structural_retrieval
  .
• output_language
• kmKR_notation
  
• (expressivity kmFOL)
  
• kmFCG
  .
• kmKIF .
  
• kmXML-based notation
  .
• kmRDF
  -