Knowledge Graph and Sequence Data Embedding Models

Implementation of translation-based relational learning models (TransE [Bordes et al. 2013], TransH) in Tensorflow.

In addition to original source code: More efficient triple scoring and mini-batch processing (Adagrad SGD).

Data preparation works directly with ontologies (RDF or OWL) For triple processing, rdflib is used.

For the use of sequential data (e.g. text corpora, events, etc.), there is the option to use a joint model (i.e. TransE + Skipgram (TEKE) either pre-trained or joint training)

EKL

More advanced sequential embedding models (event models) can be plugged-in (Convolutional-Autoencoder, Concatenation, ...)

How to run:

Put the path_to_kg and optional path_to_sequence in ekl_experiment.py

Invoke: > python ekl_experiment.py

Bring your own data

• Put an rdf/xml file into your path_to_kg
• Put a sequence.txt file of comma-separated event IDs into path_to_sequence
• Supply a unique_msgs.txt mapping of the form: Event-URI fragment identifier | ID (starting from 0)

Requirements:

• rdflib (4.1.2)
• pandas (0.19.2)
• numpy (1.13.0)
• TensorFlow (1.1.0)