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Morph-CSV

Morph-CSV is an open source tool for querying tabular data sources using SPARQL. It exploits the information from the query, RML+FnO mappings and CSVW metadata to enhance the performance and completness of traditional OBDA systems (SPARQL-to-SQL translators). At this moment can be embebed in the top of any R2RML-compliant system. For detail information, watch the introductory video about Morph-CSV. IF you have any related question on how to create RML+FnO or CSVW annotations, please ask to the W3C Community Group on Knowledge Graph Construction

Morph-csv workflow

Citing Morph-CSV: If you used Morph-CSV in your work, please cite as:

@article{chaves2021enhancing,
  author    = {Chaves{-}Fraga, David and Ruckhaus, Edna and Priyatna, Freddy and Vidal, Maria{-}Esther and Corcho, Oscar},
  title     = {Enhancing Virtual Ontology Based Access over Tabular Data with Morph-CSV},
  journal   = {Semantic Web},
  year      = {2021},
  doi       = {https://doi.org/10.3233/SW-210432},
  publisher = {IOS Press}
}

How to use it?

First of all clone the repository:

git clone https://github.com/oeg-upm/morph-csv.git
cd morph-csv

The best way to run Morph-CSV is using its user interface, deployable with docker*:

 docker-compose up -d

An user interface as we show in the following image will be display in localhost:5000 Morph-csv demo

If you prefer a CLI tool, we provide two ways to run morph-csv: using the created docker image or directly run with Python3:

  • Using docker and docker-compose*:

    docker-compose up -d
    docker exec -it morphcsv python3 /morphcsv/morphcsv.py -c /configs/config-file.json -q /queries/query-file.rq
  • Using python3 (under a UNIX system):

    pip3 install -r requirements.txt
    python3 morphcsv.py -c path-to-config-file.json -q path-to-query-file.rq

*If you have any local resource you want to use copy it to the corresponding shared volume (folders: data, mappings, configs or queries)

Define your config.json file

The path of the data sources in CSVW and YARRRML anotations have to be the same.

{
  "csvw":"PATH OR URL to CSVW annotations",
  "yarrrml": "PATH OR URL TO YARRRML+FnO Mapping"
}

Evaluation

Morph-CSV has ben tested over three use cases: BSBM, Madrid-GTFS-Bench and Bio2RDF project. You can get the resources used and the results obtained in the branch evaluation.

Publications:

  • David Chaves-Fraga, Edna Ruckhaus, Freddy Priyatna, Maria-Esther Vidal, Oscar Corcho: Enhancing Virtual Ontology Based Access over Tabular Data with Morph-CSV. Semantic Web Journal, 2021. Online
  • David Chaves-Fraga, Freddy Priyatna, Idafen Santana-Pérez and Oscar Corcho: Virtual Statistics Knowledge Graph Generation from CSV files. Emerging Topics in Semantic Technologies: ISWC2018 Satellite Events. Vol. 36. Studies on the Semantic Web. IOS Press,2018, pp. 235–244 Online Version
  • Oscar Corcho, Freddy Priyatna, David Chaves-Fraga: Towards a New Generation of Ontology Based Data Access. Semantic Web Journal, 2020. Online version
  • Ana Iglesias-Molina, David Chaves-Fraga, Freddy Priyatna, Oscar Corcho: Enhancing the Maintainability of the Bio2RDF project Using Declarative Mappings. 12th International Semantic Web Applications and Tools for Health Care and Life Sciences Conference, 2019. Online version
  • David Chaves-Fraga, Luis Pozo, Jhon Toledo, Edna Ruckhaus, Oscar Corcho: Morph-CSV: Virtual Knowledge Graph Access for Tabular Data. 19th International Semantic Web Conference P&D, 2020. Online

Authors and Contact

Ontology Engineering Group - Data Integration:

Acknowledgements

The development of Morph-CSV has been supported by the Spanish national project Datos 4.0