Graphene is an information extraction pipeline which extracts Knowledge Graphs from texts (n-ary relations and rhetorical structures extracted from complex factoid discourse). Given a sentence or a text, Graphene outputs a semantic representation of the text which is a labeled directed graph (a knowledge graph). This knowledge graph can be later used for addressing different AI tasks, such as building Question Answering systems, extracting structured data from text, supporting semantic inference, among other tasks. Differently from existing open relation extraction tools, which focus on the main relation expressed in a sentence, Graphene aims at maximizing the extraction of contextual relations. For example:
Trump withdrew his sponsorship after the second Tour de Trump in 1990 because his business ventures were experiencing financial woes.
In order to capture all the contextual information, Graphene performs the following steps:
- Resolves co-references.
- Transforms complex sentences (for example, containing subordinations, coordinations, appositive phrases, etc), into simple independent sentences (one clause per sentence).
- Identifies rhetorical relations between those sentences
- Extract binary relations (
subject,predicateandobject) from each sentence. - Merge all the extracted relations into a relation graph (knowledge graph).
Graphene’s extracted graphs are represented by our RDFNL format, an simple format that facilitates the representation of complex contextual relations in a way that balances machine representation with human legibility. A description of the RDFNL format can be found here. In order to increase further processability of the extracted relations, Graphene can materialize its relations into a proper RDF graph serialized under the N-Triples specification of the RDF standard. A description of the RDF format can be found here. Alternatively, developers can use the direct output class of the API, which is serializable and deserializable as a JSON object.
Although the Treasury will announce details of the November refunding on Monday, the funding will be delayed if Congress and President Bush fail to increase the Treasury's borrowing capacity.
The serialized class: JSON
The RDFNL format:
# Although the Treasury will announce details of the November refunding on Monday , the funding will be delayed if Congress and President Bush fail to increase the Treasury 's borrowing capacity .
bacf06771e0f4fc5a8e68c30fc77c9c4 0 the Treasury will announce details of the November refunding
S:TEMPORAL on Monday .
L:CONTRAST 948eeebd73564adab7dee5c6f177b3b9
948eeebd73564adab7dee5c6f177b3b9 0 the funding will be delayed
L:CONDITION 006a71e51295440fab7a8e8c697d2ba6
L:CONDITION e4d86228cff443b7a8e9f6d8a5c5987b
L:CONTRAST bacf06771e0f4fc5a8e68c30fc77c9c4
006a71e51295440fab7a8e8c697d2ba6 1 Congress fail to increase the Treasury 's borrowing capacity
L:LIST e4d86228cff443b7a8e9f6d8a5c5987b
e4d86228cff443b7a8e9f6d8a5c5987b 1 president Bush fail to increase the Treasury 's borrowing capacity
L:LIST 006a71e51295440fab7a8e8c697d2ba6
The RDF N-Triples format: NT
Full text extraction of the Barack Obama Wikipedia Page (2017-11-06):
The serialized class: JSON
The RDFNL format: RDFNL
The RDF N-Triples format: RDF
- Andre Freitas
- Bernhard Bermeitinger
- Christina Niklaus
- Leonardo Souza
- Matthias Cetto
- Siegfried Handschuh
- Java 8 (OpenJDK or Oracle)
- Maven 3.3.9
- Docker version 17.03+
- docker-compose version 1.12+
Compiling and packaging requires two additional packages:
cd /tmp
wget https://github.com/Lambda-3/SentenceSimplification/archive/v5.0.0.tar.gz -O SentenceSimplification.tar.gz
tar xfa SentenceSimplification.tar.gz
cd SentenceSimplification
mvn -DskipTests install
cd /tmp
wget https://github.com/Lambda-3/DiscourseSimplification/archive/v8.0.0.tar.gz -O DiscourseSimplification.tar.gz
tar xfa DiscourseSimplification.tar.gz
cd DiscourseSimplification
mvn -DskipTests install
More dependencies (requires docker)
Prior to running Graphene, two additional dependencies must be met:
Both are provided with the docker images:
For using coreference resolution, you must have a PyCobalt instance running, it is provided in the docker-compose-core.yml.
Start it with docker-compose -f docker-compose-core.yml up.
Then create a config file conf/graphene.conf pointing to the PyCobalt service:
graphene {
coreference.url = "http://localhost:5128/resolve"
}
Graphene-Core is build with
mvn clean package -DskipTests
If you want the server part, you have to specify that profile:
mvn -P server clean package -DskipTests
If you want the command line part, you have to specify that profile:
mvn -P cli clean package -DskipTests
To build both interfaces, you can specify both profiles:
mvn -P cli -P server clean package -DskipTests
You can build and start the composed images by running:
docker-compose up
A short video tutorial on the Graphene setup for CLI usage (without coreference resolution) is provided here.
Graphene comes with a Java API which is described here.
In order to use the Graphene API within your own Java application, you can import it as a Maven dependency. For this task, install Graphene-Core into your local repository:
mvn clean install -DskipTests
and add the following lines to your project's pom.xml file:
<dependency>
<groupId>org.lambda3.graphene</groupId>
<artifactId>graphene-core</artifactId>
<version>3.0.0-SNAPSHOT</version>
</dependency>
For simplified access, we wrapped the Graphene-Core library inside a REST-like web-service.
docker-compose upThe usage of the Graphene-Server is described here.
Another way of accessing our service is provided by a command-line interface, which is described here.
@InProceedings{cetto2018graphene,
author = {Matthias Cetto and Christina Niklaus and Andr\'{e} Freitas and Siegfried Handschuh},
title = {Graphene: Semantically-Linked Propositions in Open Information Extraction},
booktitle = {Prooceedings of COLING 2018. To appear.},
year = {2018}
}