Plugins and Integrations based on Corese

Table of contents

  1. Semantic Web Import Plugin For Gephi
  2. KGstat: Knowledge Graphs and Statistical Analysis
  3. Morph-xR2RML: MongoDB-to-RDF Translation
  4. SPARQL Micro-Services
  5. Corese-library with Python

1. Semantic Web Import Plugin For Gephi

1.1. Description

Gephi is open-source software designed for visualizing and analyzing large network graphs. Ideal for data analysts and scientists interested in exploring graph structures.

Users can interact with visual representations to manipulate structures, shapes, and colors, unveiling hidden properties in the data. The platform aims to assist data analysts in formulating hypotheses, intuitively identifying patterns, and isolating anomalies or issues in data sourcing. As a complement to traditional statistical methods, Gephi employs interactive interfaces to enhance visual reasoning, a key component of Exploratory Data Analysis within the field of Visual Analytics.

The SemanticWebImport plugin enables the import of semantic data into Gephi. This data is acquired by executing a SPARQL query on the semantic dataset and is built on the Corese framework. Developed by INRIA’s Wimmics team, this plugin serves as a valuable extension to Gephi’s capabilities.

1.2. Prerequisites

  • Basic understanding of graph theory and network analysis.
  • Familiarity with SPARQL queries and semantic web technologies is recommended.
  • Gephi software should be installed and running on your system.

1.3. Installation Guide

Follow the steps described on the Github page:

1.4 Additional Resources

1.5. Feedback and Support

For any questions, issues, or feature requests, please visit the GitHub repository’s issues section.

2. KGstat: Knowledge Graphs and Statistical Analysis

2.1. Description

KGstat is a repository that offers tutorial notebooks aimed at combining the querying of Knowledge Graphs via SPARQL with statistical analysis and modeling using R as a programming language. Developed by Anna Bobasheva, this resource is an intersection of data science, Semantic Web technology, and statistics.

The tutorials cover a range of topics including:

  • Accessing and querying knowledge graphs
  • Visualizing descriptive statistics
  • Regression methods
  • Classification methods
  • Time series analysis

The intended audience for KGstat encompasses data science students, professionals in the Semantic Web community, and statisticians. The tutorials serve as a bridge to help these diverse groups discover how they can benefit from each other’s work.

2.2. Prerequisites

  • Basic understanding of SPARQL and Knowledge Graphs.
  • Familiarity with R programming language.
  • An interest in applying statistics to Semantic Web data.

2.3. Installation Guide

  1. Visit the KGstat GitHub repository.
  2. Clone the repository or download the ZIP file to your local machine.
  3. Open the tutorial notebooks in an R environment to get started.

2.4. Additional Resources

2.5. Feedback and Support

For any further questions or to provide feedback, you can reach out to the developer Anna Bobasheva through the GitHub repository’s issues section.

3. Morph-xR2RML: MongoDB-to-RDF Translation

3.1. Description

Morph-xR2RML is an implementation of the xR2RML mapping language that enables the description of mappings from relational or non relational databases to RDF. xR2RML is an extension of R2RML and RML.

Morph-xR2RML was developed by the I3S laboratory as an extension of the Morph-RDB project which is an implementation of R2RML. It is made available under the Apache 2.0 License.

3.2. Prerequisites

  • Basic understanding of MongoDB and RDF.
  • Familiarity with SPARQL queries is recommended.

3.3. Installation Guide

The easiest way to get started with Morph-xR2RML is by using Docker. Detailed installation instructions can be found here.

3.4. Additional Resources

3.5. Feedback and Support

For any questions or issues, please visit the GitHub repository’s issues section.

4. SPARQL Micro-Services

4.1. Description

SPARQL Micro-Services offers an architecture for querying Web APIs with SPARQL. Each SPARQL micro-service acts as a lightweight SPARQL endpoint, providing access to a small, resource-centric graph that queries Web APIs and builds a custom graph.

4.2. Prerequisites

  • Understanding of SPARQL queries and Web APIs.
  • Familiarity with Docker is beneficial but not mandatory.

4.3. Installation Guide

The most straightforward way to deploy SPARQL Micro-Services is using Docker. Detailed instructions can be found here.

4.4. Additional Resources

4.5. Feedback and Support

For any questions or issues, please visit the GitHub repository’s issues section.

5. Corese-library with Python

6.1. Description

Corese-library with Python provides a Python interface to work with Corese, which is a semantic graph database. The guide shows you how to set up your Python environment, run a Corese server, and interact with it using Python code.

5.2. Prerequisites

  • Basic knowledge of Python and Java
  • Python installed
  • Java installed

5.3. Installation Guide

  1. Install Java and Python.
  2. Install Python dependencies: pip install --user py4j.
  3. Download Corese-library-python.
  4. Place corese-library-python-4.4.1.jar in the same directory as your Python code, say
  5. Run with python

5.4. Additional Resources

5.5. Feedback and Support

For questions or issues, consult the GitHub repository’s issues section.

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