RDF-star2Vec

RDF-star2Vec is a knowledge graph embedding model for RDF-star graphs. This repository is an extended implementation of jRDF2Vec. You can generate embeddings for N-Triples file.

Walk strategies

RDF-star2Vec introduces graph walk methods that allow probabilistic transition between a quoted triple (QT) and its compositional entities.

• qs-walk (from QT to subject): walks from the QT to its compositional entity that is in the role of subject
• oq-wal (from object to QT): walks from the compositional entity that is in the role of object to the QT.

How to use

1. Generate walk file
2. Representation learning

Generate walk file

ex) The source dataset is rdf-star_ext_ikgrc2023.nt, depth is 8, probability of qs-walk is 0.5, probability of oq-walk is 0.5.

You can download the packaged JAR of the latest successful: here

java -jar rdf-star2vec_1.0.0-SNAPSHOT.jar -graph rdf-star_ext_ikgrc2023.nt -onlyWalks -walkDir experiment/ -walkGenerationMode STAR_MID_WALKS_DUPLICATE_FREE -depth 8 -qt2subject 0.5 -object2qt 0.5

Parameters for the Walk Configuration

• -onlyWalks (Required)
  • If added to the call, this switch will deactivate the training part so that only walks are generated. If training parameters are specified, they are ignored.
• -qt2subject <number> (Required, Range: 0 to 1)
  • The transition probability from a QT node to its compositional subject.
• -object2qt <number> (Required, Range: 0 to 1)
  • The transition probability from an object to a QT node.
• -numberOfWalks <number> (default: 100)
  • The number of walks to be performed per entity.
• -depth <depth> (default: 4)
  • This parameter controls the depth of each walk. Depth is defined as the number of hops. Hence, you can also set an odd number.
• -walkGenerationMode <STAR_MID_WALKS | STAR_MID_WALKS_DUPLICATE_FREE | STAR_RANDOM_WALKS | STAR_RANDOM_WALKS_DUPLICATE_FREE> (default: STAR_MID_WALKS_DUPLICATE_FREE)
  • This parameter determines the mode for the walk generation (multiple walk generation algorithms are available).
• -threads <number_of_threads> (default: (# of available processors) / 2)
  • This parameter allows you to set the number of threads that shall be used for the walk generation as well as for the training.
• -walkDirectory <directory where walk files shall be generated/reside>
  • The directory where the walks shall be generated into.

System Requirements

• Java 8 or later.

Representation learning

The following steps are necessary to obtain RDF-star2Vec embeddings.

Step 1: Compile wang2vec
The RDF-star2Vec uses structured word2vec (wang2vec).

Download the C implementation of wang2vec from GitHub.

Edit word2vec.c to support QT (long strings) in RDF-star as follows:

vi word2vec.c

#include <math.h>
#include <pthread.h>

- #define MAX_STRING 100
+ #define MAX_STRING 10000
#define MAX_EXP 6
#define MAX_SENTENCE_LENGTH 1000

Compile the files with make.

Step 2: Run word2vec
Run the compiled wang2vec implementation on the generated walk file using the walk strategies. In case you receive a segfault error, set the capping parameter to 1 (-cap 1).

Call Syntax

./word2vec -train <your walk file> -output <desired file to be written> - type <1 (skip-gram) or 3 (structured 
skip-gram>) -size <vector size> -threads <number of threads> -min-count 0 -cap 1  

Exemplary Call

./word2vec -train walks.txt -output v100.txt -type 3 -size 100 -threads 4 -min-count 0 -cap 1  

Evaluation

Please see here for evaluating the embeddings generated using RDF-star2Vec

Publication

Egami, S., Ugai, T., Oota, M., Matsushita, K., Kawamura, T., Kozaki, K., Fukuda, K.: RDF-star2Vec: RDF-star Graph Embeddings for Data Mining, IEEE Access, EarlyAccess