VeeAlign

The following repository contains the code and datasets used for the EMNLP 2021 publication "Multifaceted Context Representation using Dual Attention for Ontology Alignment". The system presented in the paper, VeeAlign, is a dual attention based ontology alignment system that aligns ontologies using a novel multi-faceted context representation approach. It was declared the best performing system in the Conference track for OAEI 2020.

Setup

Please run the following commands:

1. conda create --name py37 python=3.7
2. pip3 install -r requirements.txt

Datasets

Five datasets are made available with this repository, in the format required by the code:

1. Conference Dataset
2. Lebensmittel Dataset
3. Freizeit Dataset
4. Web directory Dataset
5. Multifarm Dataset

To add a new dataset:

1. Create a folder <dataset_name> inside the datasets directory.
2. This directory should contain two subdirectories ontologies and alignments, which contain the ontologies being aligned and the alignments respectively.

There are no naming conventions wrt the naming of the ontology/alignment files, but it is assumed that the RDF alignment file contains <onto1> and <onto2> tags denoting the ontologies being aligned.

Training & Testing

To train a model, run python3 train.py.

To test a model, run python3 test.py <ontology1> <ontology2>.

Note that:

1. Both train.py and test.py will run with configurational parameters described in config.ini.
2. train.py will train on alignments specified in config.ini
3. test.py will load pretrained model specified in config.ini

Details on the configurational parameters and how to set them for reproducibility are described here.

Files

There are five main script files:

1. src/train.py: This is the script that needs to be run to train your model.
2. src/test.py: This is the script that needs to be run to test your model.
3. src/data_preprocessing.py: This script contains code to preprocess data for running.
4. src/ontology.py: This script contains code to parse the ontology.
5. src/config.ini: This file contains the configurational values for main.py, and can be adjusted to make the code run for conference, lebensmittel, freizeit and web-directorydatasets. This is explained in further detail below.

We also provide the datasets used for experimentation, in the datasets folder (also included in the data appendix).

Regarding Spelling-check

One of the preprocessing steps, a spelling checker (and corrector) was implemented using the RapidAPI GrammarBot (https://rapidapi.com/grammarbot/api/grammarbot/). If you wish to use it (for better accuracy and/or reproducing the results in our work), please create an API key on this tool and add it to line 12 of data_preprocessing.py. Note that this tool charges for requests beyond a certain limit, so please be mindful of the same.

Using this tool, of course, is optional and currently the script is setup to ignore spell-check.

Configuration

This section details in tabular format, the various configurational fields in config.ini and the best performing values for each of the datasets.

Parameter	Conference	Lebensmittel	Freizeit	Web-directory	Description
Name	conference	lebensmittel	freizeit	web-directory	Name of dataset
K	7	5	5	5	Value of K used in K-fold sliding window
ontology_split	True	False	False	False	Split training data at ontology level (True) or on element level (False)
max_false_examples	150000	150000	150000	150000	Max number of false (dissimilar) examples to take while training
has_spellcheck	True	False	False	False	Whether or not to use an English spelling checker while preprocessing.
max_paths	2	2	16	1	Max number of paths to consider, per node
max_pathlen	26	1	1	3	Max length of the path to consider
bag_of_neighbours	True	False	False	True	Determines whether one-hop neighbours are treated as a bag of nodes, or path of length one
weighted_sum	True	False	False	True	Determines whether unified path representation is computed using weighted sum, or max pooling
lr	0.001	0.001	0.001	0.001	Learning rate
num_epochs	50	50	50	50	Number of epochs
weight_decay	0.001	0.001	0.001	0.001	Weight decay
batch_size	32	32	32	32	Batch size