DBpedia Spotlight Wrapper app

This repository provides a wrapper for using the DBpedia Spotlight service to perform named entity recognition and linking.

User instruction

General user instruction for CLAMS apps is available at CLAMS Apps documentation.

Below is a list of additional information specific to this app.

System requirements

• Python3 with the clams-python module installed, to run the app locally.
• Maven 3.1, or later.
• JDK 1.8
• The English language model from the DBpedia Databus.
• An HTTP client utility (such as curl) to invoke and execute analysis.
• 10GB of RAM to run the DBpedia Spotlight service.

  To run in a container

• docker to run the app in a Docker container (as a HTTP server).

Running the app locally

Start by compiling the DBpedia Spotlight Project:

git clone https://github.com/dbpedia-spotlight/dbpedia-spotlight-model.git

In the local repository, run mvn package. This will take some time. Then, download and uncompress the English ('en') language model from the DBpedia Databus and run the following command:

java -Dfile.encoding=UTF-8 -Xmx10G -jar rest/target/rest-1.1-jar-with-dependencies.jar /path/to/the/uncompress/language/model http://0.0.0.0:2222/rest

Where /path/to/the/uncompress/language/model is the location of the language model.

To invoke the app, run python3 app.py

Building and running the Docker image

From the project directory, run the following in your terminal to build the Docker image from the included Dockerfile:

docker build -t dbpedia-spotlight-wrapper -f Containerfile .

Then to create a Docker container using that image, run:

docker run -v /path/to/data/directory:/data -p <port>:5000 <app_name>

where /path/to/data/directory is the location of your media files or MMIF objects and <port> is the host port number you want your container to be listening to. The HTTP inside the container will be listening to 5000 by default.

Note: on newer Mac computers using ARM architecture, it may be necessary to use 5001 instead.

Once the app is running as an HTTP server, to invoke the app and get automatic annotations, simply send a POST request to the app with a MMIF input as request body.

MMIF input files can be obtained from outputs of other CLAMS apps, or you can create an empty MMIF that only contains source media locations using the clams source command. See the help message for a more detailed instructions.

clams source --help

(Make sure you installed the same clams-python package version specified in the requirements.txt.)

Configurable runtime parameter

(Parameters should be already well-described in the app metadata. But you can use this space to show examples, for instance.)