Learning Relational Latent Representation with Clustering

An implementation of the approach introduced in the paper Clustering-based Unsupervised Relational Representation Learning with an Explicit Distributed Representation, check the original paper for details. Still under development.

Installation

get SBT
clone this repository
download the ReCeNT and follow the instruction how to compile it
add the jar in the lib folder of this project
position in the root folder of this project and
- build a jar with dependencies sbt assembly
- build a jar without dependencies sbt package

Usage

Arguments

Usage: LearnNewRepresentation.jar [OPTIONS]

OPTIONS

--db knowledgeBase                                     database(s) with data to cluster (May be specified multiple times.)                                                   
--declarations file path                               file containing declarations of predicates

--domain file file 		                               header for the knowledge base(s); specification of logical predicates

--algorithm [Spectral|Hierarchical]                    clustering algorithm to use

--bagCombination [union|intersection]                  bag combination method

--bagSimilarity [chiSquared|maximum|minimum|union]     bag similarity measure

--clusterValidation silhouette|intraClusterSimilarity  clustering validation method

--depth n                                              depth of the neighbourhood tree
                                                     
--kDomain comma-separated list of domain:numClusters   number of clusters per domain (if precise number needed, otherwise leave it to the clustering selection)
                                                       
--linkage [average|complete|ward]                      linkage for hierarchical clustering (use complete for now!)

--maxClusters n                                        maximal number of clusters to create, per domain

--minimalCoverage double                               minimal coverage for a definition to be considered as large-coverage (percentage)

--output string                                        name of the file to save new layer [default:newLayer.*]
                                                       
--overlapThreshold Double [0.3]                        if overlap measure smaller than this threshold, a cluster is accepted as a new predicate                                                      

--query comma-separated list                           list of domains to cluster; if not set, all domains are clustered

--root filePath                                        folder to place files in

--saturationTradeOff Double                            saturation trade off parameter: sim(i) >= w * sim(i+1) [default: 0.9]
                                                     
--selection predefined|model|saturation|definition     how to select the number of clusters per domain
                                                       
--similarity [RCNT|RCNTv2]                similarity measure

--weights Array[Double]                                semi-colon separated list of parameter sets; each set is a
                                                       comma-separated list of 5 doubles [attributes,attribute
                                                       distribution,connections,vertex neighbourhood, edge distribution]

--clusterHyperedges                                    should hyperedges be clusters as well (between the specified domains)
                                                     
--extractDefs                                          extract the definitions of new predicates
                                                       
-k n                                                   desired number of clusters in 'predefined' selection method is used

Input files

Knowledge base/graph containing the facts in a domain (*.db)

Movie(Aoceanstwelve,Anelsonpeltz)
Movie(Aplayerthe,Awhoopigoldberg)
Movie(Apelicanbriefthe,Ajuliaroberts)
Movie(Aoceanstwelve,Ajuliaroberts)
...
Gender_male(Adavidsontag)
Gender_male(Arobertculp)
Gender_female(Acynthiastevenson)
Gender_male(Afredward)
Gender_female(Adinamerrill)
...
Genre(Asoderberghsteven,Acrime)
Genre(Apakulaalanj,Adrama)
Genre(Apakulaalanj,Amystery)
Genre(Aaltmanroberti,Adrama)
Workedunder(Aminianden,Asoderberghsteven)
Workedunder(Acaseyaffleck,Asoderberghsteven)
Workedunder(Aelliottgould,Asoderberghsteven)
Workedunder(Adenzelwashington,Apakulaalanj)
...

Definitions file specifying the domains of objects (*.def)

Gender_male(person)
Gender_female(person)
Genre(person,genre)
Movie(movie,person)
Workedunder(person,person)

Declarations file specifying the meaning of the arguments of predicates (*.dcl)

Gender_male(name)
Gender_female(name)
Genre(name,attr)
Movie(name,name)
Workedunder(name,name)

The arguments can have the following roles:

name - identifier of an objects/instance/example; this is essentially treated as the name of an instance
attr - identifies a discrete attribute value. Attribute name is given by the name of a predicate. It needs to have exactly one name argument.
number - identifies a continuous attribute value. Attribute name is given by the name of a predicate. It needs to have exactly one name argument.

These roles influence the way a neighbourhood tree is constructed.

Contributing

Fork it!
Create your feature branch: git checkout -b my-new-feature
Commit your changes: git commit -am 'Add some feature'
Push to the branch: git push origin my-new-feature
Submit a pull request

Contact

If you have any question, feel free to send then at sebastijan.dumancic@cs.kuleuven.be

Citing

Please cite the following paper if you are using the code

@inproceedings{ijcai2017-dumancic,
  author    = {Sebastijan Dumancic, Hendrik Blockeel},
  title     = {Clustering-Based Relational Unsupervised Representation Learning with an Explicit Distributed Representation},
  booktitle = {Proceedings of the Twenty-Sixth International Joint Conference on
               Artificial Intelligence, {IJCAI-17}},
  pages     = {1631--1637},
  year      = {2017},
  doi       = {10.24963/ijcai.2017/226},
  url       = {https://doi.org/10.24963/ijcai.2017/226}
}

License

Release under Apache License, version 2.

sebdumancic/CURLED