drepr 2.10.0.dev3

Data Representation Language for Reading Heterogeneous Datasets

D-REPR

Table of Contents

• Features
• Installation
• Example
• Contributing

Features

• Supporting reading datasets in heterogeneous formats (JSON, CSV, Spreadsheets, etc) and layouts (relational tables, matrix tables, etc) to the RDF format

Installation

From PyPi: pip install drepr

If you want to install from source or have trouble during installation, please look in the Wiki Installation

How D-REPR works

There are four steps in D-REPR to model a dataset:

1. Define resources: a resource can be a physical file in CSV, JSON format. A dataset may have multiple resources such as one main CSV file and a data-definition dictionary in a JSON file.
2. Define attributes: each attribute denotes values that belong to a group. For example, in a relational table, each column is an attribute.
3. Define alignments between attributes: a method to get a value of an attribute from a value of a corresponding attribute. The common methods are accessing by index and by value. For example, in a relational table of products, given a product id, we can retrieve the corresponding product name in the same row (by index). This step essentially defines the layout of the dataset.
4. Define a semantic model: given each attribute a type and relationships between attributes.

Docs and Examples

Please see the paper D-REPR: A Language for Describing and Mapping Diversely-Structured Data Sources to RDF and the slides.

The example datasets can be found in the example folder.

Testing

Testing rust package: cargo test --no-default-features --features pyo3/auto-initialize

Contributing

Please read the Wiki Contributing for details on our code of conduct, how to setup the development environment and the process for submitting pull requests to us.