This repository contains the code along with the datasets of the work that has been submitted as a research paper to the Journal of Web Semantics. The work focuses on how an adaptation of the encoder-decoder framework can be used to generate textual summaries for Semantic Web triples.
For a detailed description of the work presented in this repository, please refer to the preprint version of the submitted paper at: https://arxiv.org/abs/1711.00155.
In order to train our proposed models, we built two datasets of aligned knowledge base triples with text.
- D1: DBpedia triples aligned with Wikipedia biographies
- D2: Wikidata triples aligned with Wikipedia biographies
In a Unix shell environment execute: sh download_datasets.sh
in order to download and uncompress both of them in their corresponding folders (i.e. D1
and D2
). Each dataset folder consists of three different sub-folders:
data
contains each aligned dataset in binary-encodedpickle
files. Each file is a hash table. Each hash table is a Python dictionary of lists.utils
contains each dataset's supporting files, such as hash tables of the frequency with which surface forms in the Wikipedia summaries have been mapped to entity URIs. All the files are binary-encoded inpickle
files.processed
contains the processed version of each aligned dataset after removal of potential outliers (e.g. instances of the datasets with extremely long Wikipedia summaries or very few triples). The files that are contained in theprocessed
folders are the ones that are used for the training and testing of both our neural-network-based systems and the baselines.
Inspect-Dataset.ipynb
is a Python script on iPython Notebook that allows easier inspection of the above aligned datasets. The scripts provides also detailed information regarding the structure of the intermediate parts in D1/data/
and D2/data/
and the functionality of the supporting files in D1/utils/
and D2/utils/
.
The table below presents the distribution of the 10 most common predicates, and entities in our two datasets, D1 and D2 respectively.
Predicates In Triples | % | Entities In Triples | % | Entities In Summaries | % |
dbo:birthDate | 12.43 | dbr:United_States | 0.49 | dbr:United_States | 2.82 |
dbo:birthPlace | 10.67 | dbr:England | 0.19 | dbr:Actor | 2.14 |
dbo:careerStation | 5.47 | dbr:United_Kingdom | 0.14 | dbr:Association_football | 1.02 |
dbo:deathDate | 5.11 | dbr:France | 0.14 | dbr:Politician | 0.97 |
dbo:occupation | 5.06 | dbr:Canada | 0.12 | dbr:Singing | 0.90 |
dbo:team | 4.18 | dbr:India | 0.11 | dbr:United_Kingdom | 0.59 |
dbo:deathPlace | 3.51 | dbr:Actor | 0.10 | dbr:England | 0.58 |
dbo:genre | 3.22 | dbr:Italy | 0.10 | dbr:Writer | 0.53 |
dbo:associatedBand | 2.85 | dbr:London | 0.10 | dbr:Canada | 0.50 |
dbp:associatedMusicalArtist | 2.85 | dbr:Japan | 0.09 | dbr:France | 0.49 |
Predicates In Triples | % | Entities In Triples | % | Entities In Summaries | % |
wikidata:P569 (place of birth) |
14.15 | wikidata:Q5 (human) |
3.96 | wikidata:Q30 (United States of America) |
3.20 |
wikidata:P106 (occupation) |
11.63 | wikidata:Q6581097 (male) |
3.27 | wikidata:Q33999 (actor) |
1.56 |
wikidata:P31 (instance of) |
8.29 | wikidata:Q30 (United States of America) |
1.13 | wikidata:Q82955 (politician) |
1.02 |
wikidata:P21 (sex or gender) |
7.92 | wikidata:Q6581072 (female) |
0.70 | wikidata:Q21 (England) |
0.87 |
wikidata:P570 (date of death) |
7.58 | wikidata:Q145 (United Kingdom) |
0.44 | wikidata:Q145 (United Kingdom) |
0.85 |
wikidata:P27 (country of citizenship) |
6.75 | wikidata:Q82955 (politician) |
0.42 | wikidata:Q27939 (singing) |
0.79 |
wikidata:P735 (given name) |
6.53 | wikidata:Q1860 (English) |
0.39 | wikidata:Q36180 (writer) |
0.71 |
wikidata:P19 (place of birth) |
5.20 | wikidata:Q33999 (actor) |
0.36 | wikidata:Q2736 (association football) |
0.68 |
wikidata:P5 (member of sports team) |
2.64 | wikidata:Q36180 (writer) |
0.24 | wikidata:Q183 (Germany) |
0.61 |
wikidata:P69 (educated at) |
2.58 | wikidata:Q177220 (singer) |
0.20 | wikidata:Q16 (Canada) |
0.58 |
The Systems
directory contains all the code to both train and generate summaries for the sets of triples that are located in the validation and test sets of our datasets. It contains our two models in two separate sub-folders (i.e. Triples2GRU
and Triples2LSTM
). The neural network models are implemented using the Torch package. We conducted our experiments on a single Titan X (Pascal) GPU. Please make sure that Torch along with the torch-hdf5 package and the NVIDIA CUDA drivers are installed in your machine before executing any of the .lua
files in these directories.
-
You can train your own Triples2LSTM or Triples2GRU models, by executing
th train.lua
inside each system's directory. You need to have access to a GPU with at least 11 GB of memory in order to train the models with the same hyperparameters that we used in the paper. However, by lowering theparams.batch_size
andparams.rnn_size
variables you can train on NVIDIA GPUs will less amount of dedicated memory. By altering thedataset_path
andcheckpoint_path
variables in eachtrain.lua
file, you can select the dataset (i.e. D1 or D2) on which you will be training your model, and whether you will using the surface form tuples or URIs setup. The checkpoint files of the trained models will be saved in the correspondingcheckpoints
directory. -
You can use a checkpoint of a trained model to start generating summaries given input sets of triples from the validation and test sets of the aligned datasets by executing
th beam-sample.lua
. Please make sure that the pre-trained model (i.e. on D1 or D2, with URIs or surface form tuples) matches the dataset that will be loaded in thebeam_sampling_params.dataset_path
variable. You can download all our trained models and generate summaries from them by running the shell scripts located at: -
Systems/Triples2LSTM/download_trained_models.sh
-
Systems/Triples2GRU/download_trained_models.sh
The generated summaries will be saved as HDF5 files in the directory of the pre-trained model. Our trained models use CUDA Tensors. Consequently, the NVIDIA CUDA drivers along with the cutorch
and cunn
Lua packages should be installed in your machine. The latter can be installed by running:
luarocks install cutorch
luarocks install cunn
- Execute the Python script
beam-sample.py
in order to create a.csv
file with the sampled summaries. The following Python packages: (i)h5py
, (ii)pandas
, and (iii)numpy
should be installed in your machine. The script replaces the<item>
tokens along with the property-type placeholders, and presents the generated summaries along with the input sets of triples and the actual Wikipedia summaries in the resultant.csv
file. The.csv
file will by default be saved in the location of the pre-trained model.
For all possible alteration in the parameters of the above files, please consult their corresponding comment sections.
The KenLM
directory contains all the required code in order to train an n-gram Kneser-Ney language model. The code is based on the KenLM Language Model Toolkit. The binary files that reside in the ./kenlm/build/
directory have been compiled using Boost on a machine running Ubuntu 16.04 (x86_64 Linux 4.4.0-98-generic). In case you wish to experiment with this baseline on a different OS, you need to download and compile the original package according to the instructions at https://kheafield.com/code/kenlm/.
The following Python packages should also be installed in your machine: (i) numpy
, (ii) pandas
, and (iii) kenlm
. The latter can be installed by running: pip install https://github.com/kpu/kenlm/archive/master.zip
(i.e. https://github.com/kpu/kenlm).
- In a Unix shell environment, run:
sh train.sh
in order to train a 5-gram Kneser-Ney language model. The trained model will be saved in the./KenLM/
directory with the.klm
extension (e.g.D1.surf_form_tuples.model.klm
orD2.surf_form_tuples.model.klm
). - Execute the Python script
sample.py
in order to sample the most probable summary templates. The summaries are sampled using beam-search. The most probable templates will be saved in apickle
file (e.g.D1.surf_form_tuples.templates.p
orD2.surf_form_tuples.templates.p
) in the./KenLM/templates/
directory. - Run the Python script
process-templates.py
in order to post-process the templates according to each input set of triples from the test or validation set of the selected dataset. The script replaces the<item>
tokens along with any potential property-type placeholders according to the triples of the input set. The generated.csv
file with all the generated summaries along with their input sets of triples is saved in the./KenLM/templates/
directory.
In the default scenario, the model trains on D1 and samples summaries for the sets of triples that have been allocated to the test set. In case you wish to run the files (i.e. train.sh
, sample.py
and process-templates.py
) in a different setup, you can alter them following the guidelines in each file's comment sections.
This project is licensed under the terms of the Apache 2.0 License.