Building a rich ontology from agrovoc
Download
1 / 35

Building a rich ontology from AGROVOC - PowerPoint PPT Presentation


  • 285 Views
  • Updated On :

Building a rich ontology from AGROVOC. Dagobert Soergel College of Information Studies, University of Maryland [email protected] , www.dsoergel.com. FAO Agricultural Ontology Server Workshop Beijing, April 27 - 29, 2004. The problem.

Related searches for Building a rich ontology from AGROVOC

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Building a rich ontology from AGROVOC' - LionelDale


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
Building a rich ontology from agrovoc l.jpg

Building a rich ontology from AGROVOC

Dagobert Soergel

College of Information Studies, University of Maryland

[email protected], www.dsoergel.com

FAO Agricultural Ontology Server Workshop

Beijing, April 27 - 29, 2004


The problem l.jpg
The problem

  • AI and Semantic Web applications need full-fledged ontologies that support reasoning

  • Constructing such ontologies is expensive

  • While existing KOS do not provide the full set of precise concept relationships needed for reasoning,existing KOS, both large and small, represent much intellectual capital KOS = Knowledge Organization System

  • How can this intellectual capital be put to use in constructing full-fledged ontologies

  • Specifically: From AGROVOC to a full-fledged Food and Agriculture Ontology


Some applications of a food and agriculture ontology l.jpg
Some applications of a Food and Agriculture Ontology

  • Advice on crops and crop management (fertilization, irrigation)

  • Advice on pest management

  • Tracking contaminants through the food chain

  • Advice on safe food processing

  • Computing nutrition labels

  • Advice on healthy eating

  • Improved searching


Slide4 l.jpg
AGROVOC relationships compared with more differentiated relationships of a Food and Agriculture Ontology


From agrovoc to fa ontology l.jpg
From AGROVOC to FA Ontology

  • Define the FA Ontology structure

  • Fill in values from AGROVOC to the extent possible

  • Edit manually with computer assistanceusing the rules-as-you go approach andan ontology editor:

    • make existing information more precise

    • add new information



Slide8 l.jpg

Note

Relationships

between

Relationships

Relationships

between

concepts

Concept

Relationship

annotation relationship

designated by

Relationships

between

terms

Lexicalization/

Term

Other information:

language/culture

subvocabulary/scope

audience

type, etc.

manifested as

Relationships

between

strings

String


Define ontology structure relationship types l.jpg
Define ontology structureRelationship types


Fill in values from agrovoc l.jpg
Fill in values from AGROVOC

  • Fill in values from AGROVOC to the extent possible

  • Arrange in structured sequence (to the extent possible based on the information in AGROVOC) to facilitate editing(The editor can deal with similar problems at the same time.)


Edit manually with computer assistance l.jpg
Edit manually with computer assistance

  • Use the rules-as-you-go approach andgood ontology editing software that handles large ontologies efficiently

  • make existing information more precise

  • add new information

    Assumption:

    Entity types of concepts are known from AGROVOC or other sources (Langual, UMLS, WordNet); for example

    milk fat is a Substance

    Asteraceae is a taxon

    The editor may need to determine the entity type


The rules as you go approach exploit patterns to automate the conversion process example l.jpg
The rules-as-you-go approachExploitpatternsto automate the conversion processExample

1.   An editor has determined that

milk NT cow milk should become milk <includesSpecific> cow milk

  • She recognizes that this is an example of the general pattern milk NT * milk  milk <includesSpecific> * milk (where * is the wildcard character)

  • Given this pattern, the system can derive automatically

    milk NT goat milk should become milk <includesSpecific> goat milk

    Result:


The rules as you go approach exploit patterns to automate the conversion process l.jpg
The rules as you go approachExploitpatternsto automate the conversion process

1.  Editor: milk NT milk fat  milk <containsSubstance> milk fat

  • Pattern:Substance NT/RT Substance Substance <containsSubstance> Substance

  • Thereforemilk RT milk protein milk <containsSubstance> milk protein

    Result:


The rules as you go approach exploit patterns to automate the conversion process20 l.jpg
The rules as you go approachExploitpatternsto automate the conversion process

1.   Editor:

cows RT cow milk  cows <hasComponent> cow milk

  • PatternAnimal RT BodyPart Animal <hasComponent> BodyPart

  • Therefore:

    goats NT goat milk goat <hasComponent> goat milk

    Result:


The rules as you go approach exploit patterns to automate the conversion process22 l.jpg
The rules as you go approachExploitpatternsto automate the conversion process

1.   Editor:

acid soils BT chemical soil types  acid soils <isa> chemical soil types

  • Pattern:X BT * type* X <isa> * type*

  • Therefore:

    acrisols BT genetic soil types acrisols <isa> genetic soil types

    Result:


The rules as you go approach exploit patterns to automate the conversion process24 l.jpg
The rules as you go approachExploitpatternsto automate the conversion process

1.   Editor:Cichorium BT Asteraceae  Cichorium <isa> Asteraceae

  • Pattern:Taxon BT Taxon Taxon <isa> Taxon

  • Therefore:

    Cichorium endivia BT Cichorium Cichorium endivia <isa> Cichorium

    Result:


The rules as you go approach exploit patterns to automate the conversion process26 l.jpg
The rules as you go approachExploitpatternsto automate the conversion process

1.   Editor:Cichorium intybus RT coffee substitutes Cichorium intybus <usedToMake> coffee substitutes

  • Pattern:Taxon RT FoodProduct Taxon <usedToMake> FoodProduct

  • Therefore:Cichorium intybus RT root vegetables

    Cichorium intybus <usedToMake> root vegetables

    Result:


The rules as you go approach discussion l.jpg
The rules as you go approachDiscussion

Main idea: Formulate constraints to assist the editor

  • Ontology may have many relationship types, perhaps > 100

  • Constraints limit the relationship types that are possible in a specific case; show the editor only these

  • If the constraints limit possible relationship types to 1, conversion is automatic

  • Constraints may depend on Thesaurus to be converted





Checking by editor l.jpg
Checking by editor

  • Relationship instances created by editor by selecting from a constraint-generated menuare final

  • Relationship instances created automatically must be presented to the editor

  • If the editor determines that the relationship instances are almost always correct, she checks a box accept without checking


Overall conversion process l.jpg
Overall conversion process

  • One master editor must go through the file from start to finish,processing the relationship instances and creating patterns,creating new relationship types as needed

  • Assistant editors can apply the patterns.

  • In the first pass, the master editor should deal with the easy cases.

  • Deal with the remaining cases later.Groups of similar relationship instances can be seen more easily in a smaller set


Adding new relationship types and new relationship instances l.jpg
Adding new relationship types and new relationship instances

  • AGROVOC does not contain all relationship types or relationship instances for AI applications

  • Need to add data. For example

    Organism X  <hasPest> Organism Y

    ChemSubstance X <actsAgainst> Organism Y

    Organism X <actsAgainst> Organism Y

    Plant X  <growsIn>  Environment Y

    FoodProduct X <suitableFor> Diet Y


Conclusion l.jpg
Conclusion

The rules-as-you-go approach is a realistic method for developing a rich ontology from an existing thesaurus


ad