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*TL;DR: The Qanary framework takes a best-of-breed approach to the integration of Question Answering (QA) components. It enables rapid development of QA systems that are flexible, extensible, reusable and provide built-in features to measure the quality of the QA quality.*

Qanary is a methodology for creating Question Answering Systems it is part of the [WDAqua project](http://wdaqua.eu) where question answering systems are researched and developed. For all the publications related to Qanary please see the section [publications](#qanarypublications). W.r.t. questions, ideas or any feedback related to Qanary please do not hesitate to [contact the core developers](https://github.com/WDAqua/Qanary/wiki/Who-do-I-talk-to%3F). However, if you like to see a QA system built using the Qanary framework, one of our core developers has build a complete end-to-end QA system which allows to query several RDF data stores: http://wdaqua.eu/qa.
Qanary is a methodology for creating Question Answering Systems it is part of the [WDAqua project](http://wdaqua.eu) where question answering systems are researched and developed. For all the publications related to Qanary please see the section [publications](#qanarypublications). W.r.t. questions, ideas or any feedback related to Qanary please do not hesitate to [contact the core developers](https://github.com/WDAqua/Qanary/wiki/Who-do-I-talk-to%3F). However, if you like to see a QA system built using the Qanary framework, one of our core developers has built a complete end-to-end QA system that allows to query several RDF data stores: http://wdaqua.eu/qa.


Please go to the [GitHub Wiki page](https://github.com/WDAqua/Qanary/wiki) of this repository to get more insights on how to use this framework, how to add new component etc.
Please go to the [GitHub Wiki page](https://github.com/WDAqua/Qanary/wiki) of this repository to get more insights on how to use this framework, how to add new components etc.

Here, we provide our key contributions on-top of the [RDF vocabulary qa](https://github.com/WDAqua/QAOntology): the reference implementation of the Qanary methodology. This repository contributes several sub-resources for the Question Answering Community to build knowledge-driven QA systems incorporating a standard [RDF vocabulary named "qa"](https://github.com/WDAqua/QAOntology).
Here, we provide our key contributions on top of the [RDF vocabulary qa](https://github.com/WDAqua/QAOntology): the reference implementation of the Qanary methodology. This repository contributes several sub-resources for the Question Answering Community to build knowledge-driven QA systems incorporating a standard [RDF vocabulary named "qa"](https://github.com/WDAqua/QAOntology).

All the resources are reusable. For detailed description of individual resources, we kindly refer to the Wiki section of this repository. The [Qanary Question Answering components](https://github.com/WDAqua/Qanary-question-answering-components) maintained by the core developers are available [here](https://github.com/WDAqua/Qanary-question-answering-components) (in a separated Git repository); it contains typical components that where provided from and for the community. They require to first clone the Qanary framework repository and execute ``mvn install``.
All the resources are reusable. For a detailed description of individual resources, we kindly refer to the Wiki section of this repository. The [Qanary Question Answering components](https://github.com/WDAqua/Qanary-question-answering-components) maintained by the core developers are available [here](https://github.com/WDAqua/Qanary-question-answering-components) (in a separated Git repository); it contains typical components that where provided from and for the community. They are required to first clone the Qanary framework repository and execute ``mvn install``.
Although many components are available already, this is still just the beginning, many more components will be published soon.

The following sub-project are part of the Qanary core frameworks:
The following sub-projects are part of the Qanary core frameworks:

* [**Qanary Pipeline**](#qanarypipeline) implementation: a central component where components for question answering systems are connected automatically and can be called by Web UIs
* Qanary component implementations: components providing wrappers to existing functionality or implement new question answering approaches
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If you want to inform yourself about the Qanary methodology in general, please use this publication: *Andreas Both, Dennis Diefenbach, Kuldeep Singh, Saedeeh Shekarpour, Didier Cherix and Christoph Lange: Qanary - A Methodology for Vocabulary-driven Open Question Answering Systems* appearing in [13th Extended Semantic Web Conference](http://2016.eswc-conferences.org), 2016.

For additional publication we suggest to [follow this list of publications](https://scholar.google.de/scholar?q=%22qanary%22+question+%22answering%22)
For additional publications, we suggest to [follow this list of publications](https://scholar.google.de/scholar?q=%22qanary%22+question+%22answering%22)


## Stuff used to make this:
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3. Install Maven 3.5.2 or higher (see <https://maven.apache.org/install.html> for details)

4. Install Stardog Triplestore (<http://stardog.com/>) and start it in background. Create a database with the name _qanary_. All the triples generated by the components will be stored in the _qanary_ database. We use version 7 of Stardog.
4. Install Stardog Triplestore (<http://stardog.com/>) and start it in the background. Create a database with the name _qanary_. All the triples generated by the components will be stored in the _qanary_ database. We use version 7 of Stardog.

5. Switch to Qanary directoy: `cd Qanary`
5. Switch to Qanary directory: `cd Qanary`

6. [Configure your pipeline appropriately](https://github.com/WDAqua/Qanary/wiki/Configuration-Parameters-of-a-Qanary-Pipeline). A common pitfall is the Stardog Triplestore. If you're using a regular Stardog Triplestore running locally on port `5280` with the database `qanary` and the credentials `admin`/`admin`, simply use the following configuration:
```
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stardog.password=admin
```

7. Build the components (creating Docker images is skipped):
7. Build the components (creating Docker images and deployment to Maven central are skipped):
```
mvn clean install -Ddockerfile.skip=true
mvn clean install -Ddockerfile.skip=true -Dgpg.skip
```

8. Run the pipeline component:
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java -jar target/qa.pipeline-<version>.jar
```

9. After running corresponding component JAR files, you can see Spring Boot application running on <http://localhost:8080/#/overview> that will tell the status of currently running components.
9. After running the corresponding component JAR files, you can see the Spring Boot application running on <http://localhost:8080/#/overview> that will tell the status of currently running components.
* To run components you need to build and run Qanary components, see the components repository for details: [Qanary Question Answering components](https://github.com/WDAqua/Qanary-question-answering-components)

10. Now your pipeline is ready to use. Go to <http://localhost:8080/startquestionansweringwithtextquestion>. Here you can find a User Interface to interact for adding question via web interface, and then select the components you need to include in the pipeline via checking a checkbox for each component. Press the start button and you are ready to go!
10. Now your pipeline is ready to use. Go to <http://localhost:8080/startquestionansweringwithtextquestion>. Here you can find a User Interface to interact for adding a question via web interface, and then select the components you need to include in the pipeline by checking a checkbox for each component. Press the start button and you are ready to go!


### Including the creation of Docker instances
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* Start the Qanary Docker containers.

* Now your pipeline is ready to use. Go to <http://localhost:8080/startquestionansweringwithtextquestion>. Here you can find a User Interface to interact for adding question via web interface, and then select the components you need to include in the pipeline via checking a checkbox for each component. Press the start button and you are ready to go!
* Now your pipeline is ready to use. Go to <http://localhost:8080/startquestionansweringwithtextquestion>. Here you can find a User Interface to interact for adding a question via web interface, and then select the components you need to include in the pipeline by checking a checkbox for each component. Press the start button and you are ready to go!


### Using Qanary for your work

* Our Wiki page contains all the information about how to integrate a new component and also about the easy usability of Qanary framework. We have illustrated inclusion of a new component with example in Wiki page. Please refer to it.
* Our Wiki page contains all the information about how to integrate a new component and describes also the easy usability of the Qanary framework. We have illustrated the inclusion of a new component with an example on the Wiki page. Please take a look at it.

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