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Lixi edited this page Apr 30, 2021 · 8 revisions

In this section we'll explain how to configure the generation of a learning problem benchmark.

Ways to Generate a Benchmark

There are 3 main ways to generate a LP benchmark.

  1. The first one is to use a gold standard positive concept, which represents one learning problem and furthermore provide concepts which won't fit the positive concept (we call them negative concepts wrt a positive concept). This negative concept should be close to the positive one but yield other individuals

e.g. Politician and hasParents some Person can have multiple negative concepts such as not Politician and hasParents some Person and Politician and not (hasParents some Person) while the latter obv. wouldn't yield results.

  1. However it might be a lot of work to add for all positive concepts multiple useful negative concepts. Hence LPBenchGen can generate negative concepts out of positives and you only provide positive concepts

  2. You can also just let LPBenchGen generate concepts from the TBox and ABox. It will first generate all possible concepts in the parameters provided by the configuration and a TBox then check if these concepts yield an actual result using the ABox.

Allowing certain types to yield a topical subset of the ABox.

Further on you can add allowed Types.

  • This will be used to only allow Individuals who have a class of this type
  • To generate positive concepts only considering these types.

Methods of generating a Benchmark

General Parameters

These parameters are set for all three methods.

parameter Description default
endpoint The SPARQL endpoint containing the TBox and ABox -
owlFile The ontology (TBox) file -
seed A seed for every random 1
types The allowed types, whereas empty or not set means every class type is allowed
maxIndividualsPerExampleConcept The maximum Individuals which should be added to the final ABox per concept (positive as well as negative) 200
percentageOfPositiveExamples The percentage of positive examples which should be kept from the maxIndividualsPerExampleConcept for the learning problem 0.5
percentageOfNegativeExamples The percentage of negative examples which should be kept from the maxIndividualsPerExampleConcept for the learning problem 0.5
maxNoOfExamples At most this amount of postive (resp. negative) examples should be kept for the learning problem. 30
minNoOfExamples At least this amount of postive (resp. negative) examples should be kept for the learning problem. However if there are less individuals than the value, it will just take the individuals. 5
removeLiterals If the final TBox+ABox should be pruned from Literals and thus making the file smaller. false

use positive and negative concepts

Additionally to the above parameters you can set the concepts. This is basically just a list of positives and negatives concepts.

concepts: 
   - positive: POSITIVE_CONCEPT
     negatives:
       - NEGATIVE_CONCEPT1
       ...
   - positive: ...
     negatives ...

Example

endpoint: http://dbpedia.org/sparql
owlFile: ./dbpedia.owl
types:
  - http://dbpedia.org/ontology/Work
  - http://dbpedia.org/ontology/Band
  - http://dbpedia.org/ontology/MusicalArtist
  - http://dbpedia.org/ontology/Album
  - http://dbpedia.org/ontology/Actor
  - http://dbpedia.org/ontology/MusicGenre
maxIndividualsPerExampleConcept: 30
percentageOfPositiveExamples: 0.5
percentageOfNegativeExamples: 0.5
maxNoOfExamples: 30
minNoOfExamples: 5
concepts:
  - positive: Band and hasAlbum some Album
    negatives: 
      - not Band and hasAlbum some Album
      - Band and not (hasAlbum some Album)
  - positive: Actor and MusicalArtist
    negatives: 
      - Actor and not MusicalArtist
      - not Actor and MusicalArtist

use positive concepts

If you want to generate negative concepts for all or just some positive concepts, just remove the negatives.

concepts: 
   - positive: POSITIVE_CONCEPT #here all negatives concepts will be generated
   - positive: ... #not here though
     negatives ...

Example

endpoint: http://dbpedia.org/sparql
owlFile: ./dbpedia.owl
types:
  - http://dbpedia.org/ontology/Work
  - http://dbpedia.org/ontology/Band
  - http://dbpedia.org/ontology/MusicalArtist
  - http://dbpedia.org/ontology/Album
  - http://dbpedia.org/ontology/Actor
  - http://dbpedia.org/ontology/MusicGenre
maxIndividualsPerExampleConcept: 30
percentageOfPositiveExamples: 0.5
percentageOfNegativeExamples: 0.5
maxNoOfExamples: 30
minNoOfExamples: 5
concepts:
  - positive: Band and hasAlbum some Album
  - positive: Actor and MusicalArtist
    negatives: 
      - Actor and not MusicalArtist
      - not Actor and MusicalArtist

Generate Concepts

Additionally to the general parameters you can set the following parameters which indicate how to generate positive concepts

parameter Description default
maxGenerateConcepts The amount of concepts which should be generated (might be less) 20
maxConceptLength The maximum length a concept is allowed to be. 10
minConceptLength The minimum length a concept is allowed to be. 4
maxDepth The recursive depth on how deep a concept should constructed (ignore in most cases) 2
inferDirectSuperClasses If direct super Classes (e.g. Person for Artist) should be allowed during the construction of a concept. true
endpointInfersRules If the triple store infers rules directly set this to true and you get better concepts and examples. false
namespace The namespace in which a class or rule/property has to reside in. if empty or not set all are allowed. (optional)

Example

endpoint: http://dbpedia.org/sparql
owlFile: ./dbpedia.owl
seed: 123
types:
  - http://dbpedia.org/ontology/Politician
  - http://dbpedia.org/ontology/Company
  - http://dbpedia.org/ontology/RecordLabel
  - http://dbpedia.org/ontology/ArchitecturalStructure
  - http://dbpedia.org/ontology/Architect
  - http://dbpedia.org/ontology/Person
  - http://dbpedia.org/ontology/Mayor
  - http://dbpedia.org/ontology/Work
  - http://dbpedia.org/ontology/Band
  - http://dbpedia.org/ontology/MusicalArtist
  - http://dbpedia.org/ontology/Album
  - http://dbpedia.org/ontology/Actor
  - http://dbpedia.org/ontology/MusicGenre
maxIndividualsPerExampleConcept: 30
percentageOfPositiveExamples: 0.5
percentageOfNegativeExamples: 0.5
maxNoOfExamples: 30
minNoOfExamples: 5
maxGenerateConcepts: 10
maxConceptLength: 8
minConceptLength: 4
maxDepth: 1
inferDirectSuperClasses: true
endpointInfersRules: false
removeLiterals: true
namespace: http://dbpedia.org/ontology/

how to achieve good examples

To achieve good examples for small concepts like Artist you should allow only types which are semantically near that. A negative example with class of Animal wouldn't provide much information, thus it makes sense to allow other super types of the class Person such as Politician. Then the negative examples will be much more informative.

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