The document discusses controlled natural languages (CNLs) for knowledge capture. It defines CNLs as restricted versions of natural languages designed to be unambiguous and translatable to formal logic. The document outlines challenges in balancing expressiveness with parseability in CNL design and provides examples of how concepts, relations and ambiguity are handled. It also discusses CNL implementation methods and gives an example of using CNL to help domain experts construct ontologies.
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Controlled Natural Languages for Knowledge Capture
1. Introduction
Controlled Natural Languages
Conclusion
Controlled Natural Languages for Knowledge
Capture
Intelligence Augmentation Forum
R. Denaux
School of Computing
University of Leeds
Leeds, 7th of June 2010
Denaux Controlled Natural Languages
2. Introduction
Controlled Natural Languages
Conclusion
Outline
1 Introduction
Context
Problem
2 Controlled Natural Languages
Definition
Design Issues
Implementation
Demo
3 Conclusion
Denaux Controlled Natural Languages
3. Introduction
Context
Controlled Natural Languages
Problem
Conclusion
Outline
1 Introduction
Context
Problem
2 Controlled Natural Languages
Definition
Design Issues
Implementation
Demo
3 Conclusion
Denaux Controlled Natural Languages
4. Introduction
Context
Controlled Natural Languages
Problem
Conclusion
Knowledge Capture
What are we capturing?
Denaux Controlled Natural Languages
5. Introduction
Context
Controlled Natural Languages
Problem
Conclusion
Knowledge Capture
What are we capturing?
Denaux Controlled Natural Languages
6. Introduction
Context
Controlled Natural Languages
Problem
Conclusion
Knowledge Capture
What are we capturing?
Denaux Controlled Natural Languages
7. Introduction
Context
Controlled Natural Languages
Problem
Conclusion
Knowledge Capture
What are we capturing?
Denaux Controlled Natural Languages
8. Introduction
Context
Controlled Natural Languages
Problem
Conclusion
Knowledge Capture
What are we capturing?
Denaux Controlled Natural Languages
9. Introduction
Context
Controlled Natural Languages
Problem
Conclusion
Knowledge Capture
What are we capturing?
Denaux Controlled Natural Languages
10. Introduction
Context
Controlled Natural Languages
Problem
Conclusion
Knowledge Capture
What are we capturing in OWL terminology?
Denaux Controlled Natural Languages
11. Introduction
Context
Controlled Natural Languages
Problem
Conclusion
Knowledge Capture
What are we capturing in OWL terminology?
Denaux Controlled Natural Languages
12. Introduction
Context
Controlled Natural Languages
Problem
Conclusion
Why do we need it?
Intelligence Augmentation
Denaux Controlled Natural Languages
13. Introduction
Context
Controlled Natural Languages
Problem
Conclusion
Outline
1 Introduction
Context
Problem
2 Controlled Natural Languages
Definition
Design Issues
Implementation
Demo
3 Conclusion
Denaux Controlled Natural Languages
14. Introduction
Context
Controlled Natural Languages
Problem
Conclusion
Formalisation of Knowledge
People have the real knowledge (sometimes encoded in
documents, wikis, databases, etc.)
Knowledge formalisation is not trivial and needs understanding
of logical formalism: 1st order logic, OWL, etc.
Denaux Controlled Natural Languages
15. Introduction
Context
Controlled Natural Languages
Problem
Conclusion
Wikis
Conceptual Knowledge: Yes
Factual Knowledge: Yes
However:
Not formal
Knowledge extraction results are limited, despite
semi-structured format
Denaux Controlled Natural Languages
16. Introduction
Context
Controlled Natural Languages
Problem
Conclusion
Semantic Wikis
Conceptual Knowledge: Yes, but cumbersome
Factual Knowledge: Yes
Denaux Controlled Natural Languages
17. Introduction
Context
Controlled Natural Languages
Problem
Conclusion
Ontology Engineering
Conceptual Knowledge: Yes
Factual Knowledge: Yes
However:
Requires knowledge elicitation phase
Results are difficult to understand by domain experts
Denaux Controlled Natural Languages
18. Definition
Introduction
Design Issues
Controlled Natural Languages
Implementation
Conclusion
Demo
Outline
1 Introduction
Context
Problem
2 Controlled Natural Languages
Definition
Design Issues
Implementation
Demo
3 Conclusion
Denaux Controlled Natural Languages
19. Definition
Introduction
Design Issues
Controlled Natural Languages
Implementation
Conclusion
Demo
What is a Controlled Natural Language(CNL)?
Unfortunately...
Denaux Controlled Natural Languages
20. Definition
Introduction
Design Issues
Controlled Natural Languages
Implementation
Conclusion
Demo
What is a Controlled Natural Language(CNL)?
Unfortunately...
Denaux Controlled Natural Languages
21. Definition
Introduction
Design Issues
Controlled Natural Languages
Implementation
Conclusion
Demo
What is a Controlled Natural Language(CNL)?
Unfortunately...
Denaux Controlled Natural Languages
22. Definition
Introduction
Design Issues
Controlled Natural Languages
Implementation
Conclusion
Demo
What is a Controlled Natural Language(CNL)?
Unfortunately...
Denaux Controlled Natural Languages
23. Definition
Introduction
Design Issues
Controlled Natural Languages
Implementation
Conclusion
Demo
What is a Controlled Natural Language(CNL)?
Unfortunately...
Denaux Controlled Natural Languages
24. Definition
Introduction
Design Issues
Controlled Natural Languages
Implementation
Conclusion
Demo
What is a Controlled Natural Language(CNL)?
Constraints
Every CNL is a kind of
Engineerd Language
Denaux Controlled Natural Languages
25. Definition
Introduction
Design Issues
Controlled Natural Languages
Implementation
Conclusion
Demo
What is a Controlled Natural Language(CNL)?
Constraints
Every CNL is a kind of
Engineerd Language
Optional Constraints
A CNL usually can be
translated into a Logical
Formalism
Denaux Controlled Natural Languages
26. Definition
Introduction
Design Issues
Controlled Natural Languages
Implementation
Conclusion
Demo
What is a Controlled Natural Language(CNL)?
Constraints
Every CNL is a kind of
Engineerd Language
Optional Constraints
A CNL Sentence usually has
an Unambiguous Logical
Translation
Denaux Controlled Natural Languages
27. Definition
Introduction
Design Issues
Controlled Natural Languages
Implementation
Conclusion
Demo
What is a Controlled Natural Language(CNL)?
Constraints
Every CNL is a kind of
Engineerd Language
Optional Constraints
A CNL Sentence usually is an
Easy-to-Learn Language
Denaux Controlled Natural Languages
28. Definition
Introduction
Design Issues
Controlled Natural Languages
Implementation
Conclusion
Demo
Broad subdivision of CNLs
Simplified NL vs Formally Underpinned CNLs
Every Simplified NL is a kind of CNL.
cannot be directly translated into a Logical Formalism
is used to increase quality of documents
easier to understand by humans (eg non-natives)
easier to process by machines
defined by a set of restrictions on the language:
limited set of words to use
disallowed constructs (eg: passive voice)
eg: Simple English is a Simplified NL. Caterpillar Technical
English is a Simplified NL.
Denaux Controlled Natural Languages
29. Definition
Introduction
Design Issues
Controlled Natural Languages
Implementation
Conclusion
Demo
Broad subdivision of CNLs
Simplified NL vs Formally Underpinned CNLs
Every Formally Underpinned CNL is a kind of CNL.
has a semantic mapping into a Logical Formalism
is used to formalise knowledge
easier (than logic formalism) to understand by humans
directly processable by machines
defined by a formal grammar
eg: ACE, PENG, CPL(By BOEING), Common Logic Controlled
English and Rabbit are all Formally Underpinned CNLs.
Denaux Controlled Natural Languages
30. Definition
Introduction
Design Issues
Controlled Natural Languages
Implementation
Conclusion
Demo
Outline
1 Introduction
Context
Problem
2 Controlled Natural Languages
Definition
Design Issues
Implementation
Demo
3 Conclusion
Denaux Controlled Natural Languages
31. Definition
Introduction
Design Issues
Controlled Natural Languages
Implementation
Conclusion
Demo
Balancing Expressivity and Parseability
Limiting statements per sentence
NL: To describe in an unambiguous manner the inland
hydrology feature classes surveyed by Ordnance Survey
with the intention of improving the use of the surveyed data
by our customers and enabling semi-automatic processing
of these data.
CNL: Ontology describes the OS Inland Hydrology Feature
Classes.
CNL: Ontology aims to improve Data Usage Of OS Data.
CNL: Ontology aims to enable Semi-automatic Processing
of OS Data.
etc.
Denaux Controlled Natural Languages
32. Definition
Introduction
Design Issues
Controlled Natural Languages
Implementation
Conclusion
Demo
Balancing Expressivity and Parseability
Anaphoric Reference
1 A pilot does not have a valid license.
2 It is expired.
Denaux Controlled Natural Languages
33. Definition
Introduction
Design Issues
Controlled Natural Languages
Implementation
Conclusion
Demo
Capturing the formal semantics
Subjunction
An Actor is a Person. (Actor == Person?)
Every Actor is a kind of Person.
Denaux Controlled Natural Languages
34. Definition
Introduction
Design Issues
Controlled Natural Languages
Implementation
Conclusion
Demo
Capturing the formal semantics
Relation vs Definition
Every River flows into a Sea
A River is anything that: is a kind of Body of Water; flows
into a Sea.
Denaux Controlled Natural Languages
35. Definition
Introduction
Design Issues
Controlled Natural Languages
Implementation
Conclusion
Demo
Capturing the formal semantics
Property Domain
The relationship “flows into” must have the subject River.
Everything that “flows into” something is a River.
Denaux Controlled Natural Languages
36. Definition
Introduction
Design Issues
Controlled Natural Languages
Implementation
Conclusion
Demo
Limiting Ambiguity
Lists
Every River flows into a Sea or a Lake.
Every River flows into a Sea or flows into a Lake
Denaux Controlled Natural Languages
37. Definition
Introduction
Design Issues
Controlled Natural Languages
Implementation
Conclusion
Demo
Limiting Ambiguity
Lists with cardinality restrictions
Every River flows into exactly 1 Sea or Lake.
Denaux Controlled Natural Languages
38. Definition
Introduction
Design Issues
Controlled Natural Languages
Implementation
Conclusion
Demo
Limiting Ambiguity
Concept or Relationship?
Every River flows into a Sea.
Every River Flow has a direction.
Denaux Controlled Natural Languages
39. Definition
Introduction
Design Issues
Controlled Natural Languages
Implementation
Conclusion
Demo
Outline
1 Introduction
Context
Problem
2 Controlled Natural Languages
Definition
Design Issues
Implementation
Demo
3 Conclusion
Denaux Controlled Natural Languages
40. Definition
Introduction
Design Issues
Controlled Natural Languages
Implementation
Conclusion
Demo
Discourse Representation Structure
First Order Logic
PENG ACE
Denaux Controlled Natural Languages
41. Definition
Introduction
Design Issues
Controlled Natural Languages
Implementation
Conclusion
Demo
Lightweight NLP
CLoNE Rabbit
Denaux Controlled Natural Languages
42. Definition
Introduction
Design Issues
Controlled Natural Languages
Implementation
Conclusion
Demo
Outline
1 Introduction
Context
Problem
2 Controlled Natural Languages
Definition
Design Issues
Implementation
Demo
3 Conclusion
Denaux Controlled Natural Languages
43. Definition
Introduction
Design Issues
Controlled Natural Languages
Implementation
Conclusion
Demo
Denaux Controlled Natural Languages
44. Introduction
Controlled Natural Languages
Conclusion
Typical Usage of Novice User
See example sentences to get a feeling for the language
Write sentence
Get feedback and improve sentence until correctly parsed
Denaux Controlled Natural Languages
45. Introduction
Controlled Natural Languages
Conclusion
How to Evaluate a CNL
Use a made up world with made up concepts
Use a “Controlled Visual Language”
Denaux Controlled Natural Languages
46. Introduction
Controlled Natural Languages
Conclusion
Knowledge Capture with CNLs
Conceptual Knowledge: Yes
Factual Knowledge: Yes
However:
No guarantee that conceptual knowledge is directly usable
Is OWL(or 1st Order Logics) correctly understood?
Denaux Controlled Natural Languages
47. Introduction
Controlled Natural Languages
Conclusion
ROO: Rabbit to OWL Ontology Authoring.
Example of adapting to ontology contributors
Domain experts:
Good knowledge of the domain to be represented
Limited or no Ontology Engineering experience
Limited or no knowledge of OWL, Protégé, etc.
ROO provides tool support for domain experts:
Guidance through ontology construction methodology
Controlled Natural Language interface
No OWL specific terminology
Adaptation at design time, not at runtime
Re-use techniques from User Modelling and
Personalisation
Denaux Controlled Natural Languages
48. Introduction
Controlled Natural Languages
Conclusion
ROO: Rabbit to OWL Ontology Authoring.
Example of adapting to ontology contributors
Denaux Controlled Natural Languages
49. Introduction
Controlled Natural Languages
Conclusion
ROO: Rabbit to OWL Ontology Authoring.
Example of adapting to ontology contributors
Denaux Controlled Natural Languages
50. Introduction
Controlled Natural Languages
Conclusion
ROO: Rabbit to OWL Ontology Authoring.
Example of adapting to ontology contributors
Denaux Controlled Natural Languages
51. Introduction
Controlled Natural Languages
Conclusion
Acknowledgements
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Denaux Controlled Natural Languages