This document targets developers working on INCEpTION.
Introduction
This document describes how INCEpTION internally works and how it can be extended to fit your use case or project. It is targeted to software developers. At first, we will give a brief overview of the used technology in INCEpTION, then describe how to setup the working environment including version control, IDE and software requirements. Then, the architecture itself with core services and extension points is presented.
Core technology
INCEpTION is written as a Java application and heavily relies on Spring Boot. Its user interface is a web application that is powered by Apache Wicket. The Natural Language Processing components are mostly based on DKPro Core. This includes the tokenization, import and export to many different standard formats, as well as recommenders i.e. machine learning tools that provide annotation support. The internal data format heavily relies on UIMA and its CAS format.
System Requirements
Browser |
Chrome or Safari (latest versions) |
You should also be able to use INCEpTION with other browsers such as Firefox, Brave, etc. However, those are less regularly tested by the developers. It is recommended to always use the latest version of any browser product you may be using to ensure best compatibility.
Operating System |
Linux (64bit), macOS (64bit), Windows (64bit) |
Java Runtime Environment |
version 17 or higher |
The examples in this guide are based on a recent Debian Linux. Most of them should apply quite directly to Debian-based distributions like e.g. Ubuntu. INCEpTION will run on other distributions as well, but you may have to use different commands for managing users, installing software, etc.
Operating System |
Linux (64bit), macOS (64bit), Windows (64bit) |
Java Runtime Environment |
version 17 or higher |
DB Server |
MariaDB version 10.6 or higher |
You may be able to run INCEpTION on older database server versions but it may require extra configuration that is not included in this documentation. You may consider referring to older versions of this administrators guide included with older versions of INCEpTION.
Docker |
version 24 or higher (arm64 or amd64) |
Setup
This section covers setting up a development environment.
Source code management
We use git as our source code management system and collaborate via the INCEpTION repository on GitHub.
Development workflow
Every feature or bug fix needs to be tracked in an issue on GitHub. Development is done in branches. Based on the milestone (see the issue description on GitHub), the new branch is either created from master (if the code should be in the next major release) or from a bugfix release branch (if the code should be in the next minor release). In order to get the code in production, you need to create a pull request on GitHub of your branch into the target branch (as described before).
In order to contribute to INCEpTION, you need to create a pull request. This section briefly guides you through the best way of doing this:
-
Every feature or bug fix needs to be tracked in an issue on GitHub. If there is no issue for the feature yet, create an issue first.
-
Create a branch based on the branch to which you wish to contribute. Normally, you should create this branch from the master branch of the respective project. In the case that you want to fix a bug in the latest released version, you should consider to branch off the latest maintenance branch (e.g. 0.10.x). If you are not sure, ask via the issue you have just created. Do not make changes directly to the master or maintenance branches. The name of the branch should be e.g.
feature/[ISSUE-NUMBER]-[SHORT-ISSUE-DESCRIPTION]
orbugfix/[ISSUE-NUMBER]-[SHORT-ISSUE-DESCRIPTION]
. -
Now you make changes to your branch. When committing to your branch, use the format shown below for your commit messages. Note that # normally introduces comments in git. You may have to reconfigure git before attempting an interactive rebase and switch it to another comment character.
#[ISSUE NUMBER] - [ISSUE TITLE] [EMPTY LINE] - [CHANGE 1] - [CHANGE 2] - [...]
You can create the pull request any time after your first commit. I.e. you do not have to wait until you are completely finished with your implementation. Creating a pull request early tells other developers that you are actively working on an issue and facilitates asking questions about and discussing implementation details.
Git configuration
Before committing, make sure that you specified your email and name in the git config so that commits can be attributed to you. This can e.g. be done as described in the Git Documentation.
All sources files are stored using UNIX line endings. If you develop on Windows, you have to
set the core.autocrlf
configuration setting to input
to avoid accidentally submitting Windows
line endings to the repository. Using input
is a good strategy in most cases, thus you should
consider setting this as a global (add --global
) or even as a system (--system
) setting.
C:\> git config --global core.autocrlf input
After changing this setting, best do a fresh clone and check-out of the project.
Code style
We use a style for formatting the source code in INCEpTION. Our approach consists of two steps:
-
DKPro code formatting profile - the profile configures your IDE to auto-format the code according to our guidelines as you go.
-
Checkstyle - this tool is used to check if the source code is actually formatted according to our guidelines. It is run as part of a Maven build and the build fails if the code is not formatted properly.
Here is a brief summary of the formatting rules:
-
no tabs, only spaces
-
indenting using 4 spaces in Java files and 2 spaces in XML files
-
maximum 100 characters per line (with a few exceptions)
-
curly braces on the next line for class/method declarations, same line for logic blocks (if/for/…)
-
parameter names start with
a
(e.g.void foo(String aValue)
)
Setting up for the development in Eclipse
This is a guide to setting up a development environment using Eclipse on Mac OS X. The procedure should be similar for other operation systems.
First, you need to follow some steps of the Administrator Installation Guide. It is recommended to configure a MySQL-server.
We recommend you start from a Eclipse IDE for Java Developers package.
Use a JDK
On Linux or OS X, having a full JDK installed on your system is generally sufficient. You can skip on to the next section.
On Windows, you need to edit the eclipse.ini
file and directly before the -vmargs
line, you
have to add the following two lines. Mind to replace C:/Program Files/Java/jdk11
with the actual
location of the JDK/version on your system. Without this, Eclipse will complain that the
jdk.tools:jdk.tools
artifact would be missing.
-vm
C:/Program Files/Java/jdk11/jre/bin/server/jvm.dll
Eclipse Plug-ins
-
Maven Integration: m2e , already comes pre-installed with the Eclipse IDE for Java Developers. If you use another edition of Eclipse which does not have m2e pre-installed, go to Help→Install New Software, select "--All available sites--" and choose Collaboration → m2e - Maven Integration for Eclipse
-
Apache UIMA tools: go to Help→Install New Software, select "Add…" and add the update site:
http://www.apache.org/dist/uima/eclipse-update-site/
as a location. Install the Apache UIMA Eclipse tooling and runtime support. -
Eclipse Web Development Tooling: go to Help→Install New Software, select "--All available sites--" and select the following plug-ins for installation from the section Web, XML, Java EE and OSGi Enterprise Development:
-
Eclipse Java Web Developer Tools
-
Eclipse Web Developer Tools
-
Eclipse XML Editors and Tools: already comes pre-installed in newer Eclipse versions
-
Eclipse Workspace Settings
-
You should check that Text file encoding is
UTF-8
in Preferences → General → Workspace. -
You need to enable Java annotation preprocessors. Go to Preferences → Maven → Annotation Processing and set the Annotation Processing Mode to Automatic.
Importing INCEpTION into the Workspace
Checkout out the INCEpTION git repository with your favorite git client. If you use the command-line client, use the command
$ git clone https://github.com/inception-project/inception.git
In Eclipse, go to File → Import, choose Existing Maven projects, and select the folder to which you have cloned INCEpTION. Eclipse should automatically detect all modules.
Setting up Checkstyle and Formatting
We use a style for formatting the source code in INCEpTION (see Checkstyle and Formatting. The following section describes how to use it with Eclipse.
First, obtain the DKPro code formatting profile from the DKPro website (Section "Code style"). In Eclipse, go to Preferences → Java → Code Style → Formatter to import the file. Apparently, the files can also be used with IntelliJ via the [Eclipse Code Formatter](https://plugins.jetbrains.com/plugin/6546-eclipse-code-formatter) plugin.
The parameter prefix a needs to be configured manually. In Eclipse go to
Preferences → Java → Code Style set the prefix list column in the parameters row to a .
|
Second, install the Checkstyle plugin for Eclipse as well as the Maven Checkstyle plugin for Eclipse. These plugins make Eclipse automatically pick up the checkstyle configuration from the Maven project and highlight formatting problems directly in the source code editor.
-
Install Checkstyle Eclipse plugin:
https://checkstyle.org/eclipse-cs/#!/install
e.g. via the update site:https://checkstyle.org/eclipse-cs/update
. -
Install the Checkstyle configuration plugin for M2Eclipse: via the update site
http://m2e-code-quality.github.com/m2e-code-quality/site/latest/
-
Select all INCEpTION projects, right click and do a Maven → Update project
Should the steps mentioned above not have been sufficient, close all the INCEpTION projects
in Eclipse, then remove them form the workspace (not from the disk), delete any .checkstyle files
in the INCEpTION modules, and then re-import them into Eclipse again using Import→Existing Maven
projects. During the project import, the Checkstyle configuration plugin for M2Eclipse should
properly set up the .checkstyle files and activate checkstyle.If the Maven project update cannot be completed due to missing .jars, execute a Maven install via right click on the inception project Run as → Maven build…, enter the goal install and check Skip Tests. Alternatively, use the command mvn clean install -DskipTests .
|
Setting up for the development in IntelliJ IDEA
This is a guide to setting up a development environment using IntelliJ IDEA. We assume that the Community Version is used, but this guide should also apply to the Enterprise Version.
After checking out INCEpTION from GitHub, open IntelliJ and import the project. The easiest
way is to go to File → Open
and the select the pom.xml
in the INCEpTION root directory.
IntelliJ IDEA will then guide you through the import process, the defaults work out of the box.
INCEpTION can now be started via running inception-app-webapp/src/main/java/de/tudarmstadt/ukp/inception/INCEpTION.java
.
If you get errors that certain classes are not found, then open a terminal, go to the INCEpTION repository root and run
mvn clean install -DskipTests=true -Dcheckstyle.skip=true
Alternatively, you can run the clean
and install
Maven goals from IntelliJ manually.
If you get an error that the command line is too long, please go to Run → Edit Configurations → Modify Options → Shorten Command Line
in IntelliJ IDEA and select the option @argfile (Java 9+) - java @argfile className [args]
.
Checkstyle and Formatting
We use a style for formatting the source code in INCEpTION (see Checkstyle and Formatting. The following section describes how to use it with IntelliJ IDEA.
First, install the Checkstyle-IDEA plugin.
In File | Settings | Other Settings | Checkstyle
, navigate to the Checkstyle tab. Start to add
a new configuration file by clicking on the +
on the right, navigate to
inception-build/src/main/resources/inception/checkstyle.xml
and apply the changes. Make sure to
check the box next to the newly created configuration and apply it as well.
In order to achieve the same formatting and import order as Eclipse, install the
Eclipse Code Formatter.
Download the DKPro Eclipse Code Style file.
In File | Settings | Other Settings | Eclipse Code Formatter
, create a new profile using this
file.
Also make sure to enable auto import optimization in File | Settings | Editor | General | Auto Import
.
To format your source code on save, we also recommend to install the Save Actions plugin and configure it accordingly.
IntelliJ IDEA Tomcat Integration
This requires IntelliJ IDEA Ultimate. Using Tomcat allows editing HTML,CSS and JavaScript on the fly without restarting
the application. First, download Apache Tomcat from http://tomcat.apache.org/
(we’re using version 8.5).
Then, you need to create a Tomcat server runtime configuration in Run | Edit Configurationsβ¦
. Click on the ` icon,
select `Tomcat Server -> Local`. Click on the `Deployment` tab and then on the `
icon to select an artifact to deploy.
Choose the exploded war version. Select the Server
tab, navigate to the path of your Tomcat server, and update the
on Update
action to Update classes and resources
for both. Make sure that all port settings are different.
You now can start or debug your web application via Tomcat. If starting throws a permission error, make sure that
the mentioned file, e.g. catalina.sh
is marked as executable.
Experimental: If desired, you can also use hot-code replacement via HotswapAgent. This allows you to change code, e.g. adding methods without needing to restart the Tomcat server. For this, follow the excellent HotSwap IntelliJ IDEA plugin guide.
Building documentation
The documentation can be built using a support class in inception-doc/src/test/java/de/tudarmstadt/ukp/inception/doc/GenerateDocumentation.java
.
To make it usable from Intellij IDEA, you need to build the whole project at least once. Run the
class. If it fails, alter the run configuration and add a new environment variable INTELLIJ=true
and check that the working directory is the INCEpTION root directory. The resulting documentation
will be in target/doc-out
.
Running INCEpTION
To run INCEpTION from your IDE, locate the class de.tudarmstadt.ukp.inception.INCEpTION
and run it
as a Java application. This runs INCEpTION as a Spring Boot application using an embedded
web server - similar to running the compiled JAR file from the command line. You may want to define
the following system properties in your launch configuration:
Setting | Value | Description |
---|---|---|
|
|
Location to store the application data |
|
|
Enable the development mode. This e.g. disables caches so that changes to HTML files in the IDE directly reflect in the running application. |
Architecture
INCEpTION uses a standard 3-layer architecture with the presentation layer using Wicket at the top, the business layer heavily relying on Spring Boot and the data layer which is interfaced with Hibernate at the bottom.
Wicket pages
Wicket can only inject components that are interfaces. A pattern for these cases is to create an
ExampleComponent
interface and implement it in an ExampleComponentImpl
class.
Services
Services encode the core logic of INCEpTION. They can be injected into Wicket pages and
other services to interact with the rest of the application. Services can inject Spring
components via autowiring. A good example of a service can e.g. be seen in the
SchedulingService.java
.
Database
The database can be accessed via Hibernate. The schema itself and migrations are managed by Liquibase.
Migration
When changing the database schema, migrations from the current schema to the new one
need to be defined. They describe how the schema needs to be modified. This way,
INCEpTION can be upgraded to newer versions without needing to manually alter the
database schema. The migration process determines the current version of the schema
and only applies transformations from there to the newest one. Each module defines its
own data base tables and migrations in a file called db-changelog.xml
. These are
automatically discovered by Liquibase and used when starting INCEpTION.
Modules
Documents
Annotation Schema
Layers
The layers mechanism allows supporting different types of annotation layers, e.g. span layers, relation layers or chain layers. It consists of the following classes and interfaces:
-
The
LayerSupport
interface provides the API for implementing layer types. -
The
LayerSupportRegistry
interface and its default implementationLayerSupportRegistryImpl
serve as an access point to the different supported layer types. -
The
LayerType
class which represents a short summary of a supported layer type. It is used when selecting the type of a feature in the UI. -
The
TypeAdapter
interface provides methods to create, manipulate or delete annotations on the given type of layer.
To add support for a new type of layer, create a Spring component class which implements the
LayerSupport
interface. Note that a single layer support class can handle multiple layer types.
However, it is generally recommended to implement a separate layer support for every layer type.
Implement the following methods:
-
getId()
to return a unique identifier for the new layer type. Typically the Spring bean name is returned here. -
getSupportedLayerTypes()
to return a list of all the supported layer types handled by the new layer support. This values returned here are used to populate the layer type choice when creating a new layer in the project settings. -
accepts(AnnotationLayer)
to returntrue
for any annotation layer that is handled by the new layer support. I.e.AnnotationLayer.getType()
must return a layer type identifier that was produced by the given layer support. -
generateTypes(TypeSystemDescription, AnnotationLayer)
to generate the UIMA type system for the given annotation layer. This is a partial type system which is merged by the application with the type systems produced by other layer supports as well as with the base type system of the application itself (i.e. the DKPro Core type system and the internal types). -
getRenderer(AnnotationLayer)
to return an early-stage renderer for the annotations on the given layer.
The concept of layers is not yet fully modularized. Many parts of the application will only know how to deal with specific types of layers. Adding a new layer type should not crash the application, but it may also not necessarily be possible to actually use the new layer. In particular, changes to the TSV format may be required to support new layer types. |
Span layer
A span layer allows to create annotations over spans of text.
If attachType
is set, then an annotation can only be created over the same span on which an
annotation of the specified type also exists. For span layers, setting attachFeature
is mandatory
if a attachType
is defined. The attachFeature
indicates the feature on the annotation of the
attachType
layer which is to be set to the newly created annotation.
For example, the Lemma
layer has the attachType
set to Token
and the attachFeature
set to
lemma
. This means, that a new lemma annotation can only be created where a token already exists
and that the lemma
feature of the token will point to the newly created lemma annotation.
Deleting an annotation that has other annotations attached to it will also cause the attached annotations to be deleted.
This case is currently not implemented because it is currently not allowed to
create spans that attach to other spans. The only span type for which this is relevant
is the Token type which cannot be deleted.
|
Relation layer
A relation layer allows to draw arcs between span annotations. The attachType
is mandatory for
relation types and specifies which type of annotations arcs can be drawn between.
Arcs can only be drawn between annotations of the same layer. It is not possible to draw an arc between two spans of different layers.
Only a single relation layer can attach to any given span layer.
If the annotation_feature
is set, then the arc is not drawn between annotations of the layer
indicated by annotation_type
, but between annotations of the type specified by the feature. E.g.
for a dependency relation layer, annotation_type
would be set to Token
and annotation_feature
to pos
. The Token
type has no visual representation in the UI. However, the pos
feature points
to a POS
annotation, which is rendered and between which the dependency relation arcs are then
drawn.
Deleting an annotation that is the endpoint of a relation will also delete the relation. In the case
that annotation_feature
, this is also the case if the annotation pointed to is deleted. E.g. if
a POS annotation in the above example is deleted, then the attaching relation annotations are also
deleted.
Document Metadata
A document metadata layer can be used to create annotations that apply to an entire document instead of to a specific span of text.
Document metadata types inherit from the UIMA AnnotationBase
type (text annotations inherit from
Annotation
). As such, they do not have begin/end offsets.
Layers Behaviors
Layer behaviors allow to customize the way a layer of a particular span behaves, e.g. whether
a span is allowed to cross sentence boundaries, whether it anchors to characters or tokens,
whether the tree of relations among annotations is valid, etc.
The layer behaviors tie in with the specific LayerSupport
implementations. The mechanism itself
consists of the following classes and interfaces:
-
The
LayerBehavior
interface provides the API necessary for registering new behaviors. There are abstract classes such asSpanLayerBehavior
orRelationLayerBehavior
which provide the APIs for behaviors of specific layer types. -
The
LayerBehaviorRegistry
and its default implementationLayerBehaviorRegistryImpl
serve as an access point to the different supported layer behaviors. Any Spring component implementing theLayerBehavior
interface is loaded, and will be named in the logs when the web app is launched. The classpath scanning used to locate Spring beans is limited to specific Java packages, e.g. any packages starting withde.tudarmstadt.ukp.clarin.webanno
.
A layer behavior may have any of the following responsibilities:
-
Ensure that new annotations that are created conform with the behavior. This is done via the
onCreate
method. If the annotation to be created does not conform with the behavior, the method can cancel the creation of the annotation by throwing anAnnotationException
. -
Highlight annotations not conforming with the behavior. This is relevant when importing pre-annotated files or when changing the behavior configuration of an existing layer. The relevant method is
onRender
. If an annotation does not conform with the behavior, a error marker should be added for problematic annotation. This is done by creating aVComment
which attaches an error message to a specified visual element, then adding that to the responseVDocument
. Note thatonRender
is unlikeonCreate
andonValidate
in that it only has indirect access to the CAS: it is passed a mapping fromAnnotationFS
instances to their corresponding visual elements, and can use.getCAS()
on the FS. The annotation layer can be identified from the visual element with.getLayer().getName()
. -
Ensure that documents being marked as finished conform with the behavior. This is done via the
onValidate
method, which returns a list ofLogMessage, AnnotationFS
pairs to report errors associated with each FS.
Features
The features mechanism allows supporting different types of annotation features, e.g. string features, numeric features, boolean features, link features, etc. It consists of the following classes and interfaces:
-
The
FeatureSupport
interface provides the API for implementing feature types. -
The
FeatureSupportRegistry
interface and its default implementationFeatureSupportRegistryImpl
serve as an access point to the different supported feature types. -
The
FeatureType
class which represents a short summary of a supported feature type. It is used when selecting the type of a feature in the UI. -
The
TypeAdapter
interface provides methods to create, manipulate or delete annotations on the given type of layer.
To add support for a new type of feature, create a Spring component class which implements the
FeatureSupport
interface. Note that a single feature support class can handle multiple feature types.
However, it is generally recommended to implement a separate layer support for every feature type.
Implement the following methods:
-
getId()
to return a unique identifier for the new feature type. Typically the Spring bean name is returned here. -
getSupportedFeatureTypes()
to return a list of all the supported feature types handled by the new feature support. This values returned here are used to populate the feature type choice when creating a new feature in the project settings. -
accepts(AnnotationLayer)
to returntrue
for any annotation layer that is handled by the new layer support. I.e.AnnotationLayer.getType()
must return a layer type identifier that was produced by the given layer support. -
generateFeature(TypeSystemDescription, TypeDescription, AnnotationFeature)
add the UIMA feature definition for the given annotation feature to the given type.
If the new feature has special configuration settings, then implement the following methods:
-
readTraits(AnnotationFeature)
to extract the special settings form the given annotation feature definition. It is expected that the traits are stored as a JSON string in thetraits
field ofAnnotationFeature
. If thetraits
field isnull
, a new traits object must be returned. -
writeTraits(AnnotationFeature, T)
to encode the layer-specific traits object into a JSON string and store it in thetraits
field ofAnnotationFeature
. -
createTraitsEditor(String, IModel<AnnotationFeature>
to create a custom UI for the special feature settings. This UI is shown below the standard settings in the feature detail editor on the Layers tab of the project settings.
Search
The search module contains the basic methods that implement the search service and search functionalities of INCEpTION.
The SearchService
and SearchServiceImpl
classes define and implement the search service as a Spring component, allowing other modules of INCEpTION to create an index for a given project, and to perform queries over that index.
The indexes have two different aspects: the conceptual index, represented by the Index class, and the physical index, represented by a particular physical implementation of an index. This allows different search providers to be used by INCEpTION. Currently, the default search implementation uses Mtas (https://github.com/meertensinstituut/mtas), a Lucene / Solr based index engine that allows to annotate not only raw texts but also different linguistic annotations.
Every search provider is defined by its own index factory, with a general index registry to hold all the available search providers.
Mtas Index
The Mtas index is implemented in the MtasDocumentIndex and MtasDocumentIndexFactory classes. Furthermore, the MtasUimaParser class provides a parser to be used by Lucene when adding a new document to the index.
-
MtasDocumentIndexFactory
The factory allows to build a new MtasDocumentIndex through the getNewIndex method, which is called by the search service.
-
MtasDocumentIndex
This class holds the main functionalities of a Mtas index. Its methods are called by the search service and allow to create, open close and drop a Mtas index. It allows to add or delete a document from an index, as well as to perform queries on the index.
Each index is related to only one project, and every project can have only one index from a given search provider.
When adding a document to a Mtas index, the Lucene engine will use the class MtasUimaParser in order to find out which are the tokens and annotations to be indexed.
-
MtasUimaParser
The parser is responsible for creating a new TokenCollection to be used by Lucene, whenever a new document is being indexed. The token collection consists of all the tokens and annotations found in the document, which are transformed into Mtas tokens in order to be added to the Lucene index. The parser scans the document CAS and goes through all its annotations, finding out which ones are related to the annotation layers in the document’s project - those are the annotations to be indexed. Currently, the parser only indexes span type annotations.
Recommenders system
For information on the different recommenders, please refer to user guide.
Recommenders
Recommenders provide the ability to generate annotation suggestions. Optionally, they can be trained based on existing annotations. Also optionally, they can be evaluated.
-
The
RecommendationEngineFactory
interface provides the API for implementing recommender types. -
The
RecommendationEngine
interface provides the API for the actual recommenders produced by the factory. -
The
RecommenderFactoryRegistry
interface and its default implementationRecommenderFactoryRegistryImpl
serve as an access point to the different recommender types.
Suggestion supports
Suggestion supports provide everything necessary to handle annotation suggestions. This includes:
-
extracting suggestions from the predicted annotations that the recommenders
-
rendering these suggestions
-
handling actions like accepting/correting, rejecting, or skipping suggestions
The subsystem is made up of the following main APIs:
-
The
SuggestionSupport
interface provides the API for handling different kinds of suggestions. -
The
SuggestionSupportRegistry
interface and its default implementationSuggestionSupportRegistryImpl
serve as an access point to the different recommender types. -
The
SuggestionRenderer
interface provides the API for rendering suggestions into aVDoc
.
Implementing a custom recommender
This section describes the overall design of internal recommenders in INCEpTION and gives a tutorial on how to implement them. Internal recommenders are created by implementing relevant Java interfaces and are added via Maven dependencies. These are then picked up during application startup by the Spring Framework.
For this tutorial, we will add a recommender for named entities that uses the data majority label for predicting, i.e. it predicts always the label that appears most often in the training data. The full code for this example can be found in the inception-example-imls-data-majority module.
Setting up the environment
To get started, check out the most recent source code of INCEpTION from Github and import it as a Maven project in the IDE of your choice. Add a new module to the INCEpTION project itself, we will call it inception-example-imls-data-majority.
In the root pom.xml
of the INCEpTION project, add your recommender as a dependency. Update
the version of the dependency entry you just created to the version you find in the pom.xml of the
INCEpTION project. It should look like this:
<dependencies>
β¦
<dependency>
<groupId>de.tudarmstadt.ukp.inception.app</groupId>
<artifactId>inception-imls-data-majority</artifactId>
<version>34.2</version>
</dependency>
β¦
</dependencies>
Add the same entry in inception-app-webapp
, but omit the version number. It then uses automatically the
version in the parent POM file. Also add it to usedDependencies
there.
To add a new recommender to INCEpTION, two classes need to be created. These are described in the following.
Implementing the RecommendationEngine
Recommenders give suggestions for possible annotations to the user. In order to do that,
they need to be able be to trained on existing annotations, predict annotations in a document and
be evaluated for a performance estimate. This is what the RecommendationEngine
abstract class is for.
It defines the methods that are used to train, test and evaluate a machine learning algorithm and offers
several helper methods. Instances of this class often wrap external machine learning packages like
OpenNLP or Deeplearning4j.
Recommenders in INCEpTION heavily rely on Apache UIMA types and features.
A recommender is configured for a certain layer and a certain feature. A layer can be seen as the
type of annotation you want to to, e.g. POS
, NER
. Layers correspond to UIMA types. A feature is
one piece of information that should be annotated, e.g. the POS tag. One layer can have many features.
When extending RecommendationEngine
, the predicted layer/type can be obtained by getPredictedType
,
the feature to predict respectively by getPredictedFeature
.
Annotations are given to a recommender in the form of a UIMA CAS. One CAS corresponds to one document in INCEpTION. Annotations from a CAS can be read and manipulated via the CasUtil.
We start by creating a new class de.tudarmstadt.ukp.inception.recommendation.imls.datamajority.DataMajorityNerRecommender
that implements RecommendationEngine
.
Please see the JavaDoc of the respective methods for their semantics.
public class DataMajorityNerRecommender
extends RecommendationEngine
{
public static final Key<DataMajorityModel> KEY_MODEL = new Key<>("model");
private static final Class<Token> DATAPOINT_UNIT = Token.class;
private final Logger log = LoggerFactory.getLogger(getClass());
public DataMajorityNerRecommender(Recommender aRecommender)
{
super(aRecommender);
}
/**
* Given training data in {@code aCasses}, train a model. In order to save data between runs,
* the {@code aContext} can be used. This method must not mutate {@code aCasses} in any way.
*
* @param aContext
* The context of the recommender
* @param aCasses
* The training data
* @throws RecommendationException
* if there was a problem during training
*/
public abstract void train(RecommenderContext aContext, List<CAS> aCasses)
throws RecommendationException;
/**
* Given text in a {@link CAS}, predict target annotations. These should be written into
* {@link CAS}. In order to restore data from e.g. previous training, the
* {@link RecommenderContext} can be used.
*
* @param aContext
* The context of the recommender
* @param aCas
* The training data
* @return Range in which the recommender generated predictions. No suggestions in this range
* should be inherited.
* @throws RecommendationException
* if there was a problem during prediction
*/
public Range predict(PredictionContext aContext, CAS aCas) throws RecommendationException
{
return predict(aContext, aCas, 0, aCas.getDocumentText().length());
}
/**
* Given text in a {@link CAS}, predict target annotations. These should be written into
* {@link CAS}. In order to restore data from e.g. previous training, the
* {@link RecommenderContext} can be used.
* <p>
* Depending on the recommender, it may be necessary to internally extend the range in which
* recommendations are generated so that recommendations that partially overlap the prediction
* range may also be generated.
*
* @param aContext
* The context of the recommender
* @param aCas
* The training data
* @param aBegin
* Begin of the range in which predictions should be generated.
* @param aEnd
* End of the range in which predictions should be generated.
* @return Range in which the recommender generated predictions. No suggestions in this range
* should be inherited.
* @throws RecommendationException
* if there was a problem during prediction
*/
public abstract Range predict(PredictionContext aContext, CAS aCas, int aBegin, int aEnd)
throws RecommendationException;
/**
* Evaluates the performance of a recommender by splitting the data given in {@code aCasses} in
* training and test sets by using {@code aDataSplitter}, training on the training set and
* measuring performance on unseen data on the training set. This method must not mutate
* {@code aCasses} in any way.
*
* @param aCasses
* The CASses containing target annotations
* @param aDataSplitter
* The splitter which determines which annotations belong to which set
* @return Scores available through an EvaluationResult object measuring the performance of
* predicting on the test set
* @throws RecommendationException
* if there was a problem during evaluation
*/
public abstract EvaluationResult evaluate(List<CAS> aCasses, DataSplitter aDataSplitter)
throws RecommendationException;
private static class DataMajorityModel
{
private final String majorityLabel;
private final double score;
private final int numberOfAnnotations;
private DataMajorityModel(String aMajorityLabel, double aScore, int aNumberOfAnnotations)
{
majorityLabel = aMajorityLabel;
score = aScore;
numberOfAnnotations = aNumberOfAnnotations;
}
}
private static class Annotation
{
private final String label;
private final double score;
private final String explanation;
private final int begin;
private final int end;
private Annotation(String aLabel, int aBegin, int aEnd)
{
this(aLabel, 0, 0, aBegin, aEnd);
}
private Annotation(String aLabel, double aScore, int aNumberOfAnnotations, int aBegin,
int aEnd)
{
label = aLabel;
score = aScore;
explanation = "Based on " + aNumberOfAnnotations + " annotations";
begin = aBegin;
end = aEnd;
}
}
}
For the constructor, we take the Recommender
object which contains the recommender configuration,
e.g. the layer and the name of the feature to recommend. The next step is to implement the required
methods.
DataMajorityModel
and Annotation
are internal data classes to simplify the code.
RecommenderContext
Instances of RecommendationEngine itself are stateless. If data like trained models need to be
saved and loaded, it can be saved in the RecommenderContext
that is given in the interface methods.
When needed again, e.g. for prediction, it then can be loaded again. The Key
class is used in order
to ensure type safety.
Training
Training consists of extracting annotations followed by training and saving the model. The
platform needs to know whether the recommender is ready for prediction, this is done by
overriding RecommendationEngine::isReadyForPrediction
.
@Override
public TrainingCapability getTrainingCapability()
{
return TRAINING_REQUIRED;
}
@Override
public void train(RecommenderContext aContext, List<CAS> aCasses) throws RecommendationException
{
List<Annotation> annotations = extractAnnotations(aCasses);
DataMajorityModel model = trainModel(annotations);
aContext.put(KEY_MODEL, model);
}
@Override
public boolean isReadyForPrediction(RecommenderContext aContext)
{
return aContext.get(KEY_MODEL).map(Objects::nonNull).orElse(false);
}
Extracting annotations itself is done by iterating over all documents and selecting all annotations for each. Here, we need to use the layer name and feature for which the recommender is configured to extract the correct annotations.
private List<Annotation> extractAnnotations(List<CAS> aCasses)
{
List<Annotation> annotations = new ArrayList<>();
for (CAS cas : aCasses) {
Type annotationType = CasUtil.getType(cas, layerName);
Feature predictedFeature = annotationType.getFeatureByBaseName(featureName);
for (AnnotationFS ann : CasUtil.select(cas, annotationType)) {
String label = ann.getFeatureValueAsString(predictedFeature);
if (isNotEmpty(label)) {
annotations.add(new Annotation(label, ann.getBegin(), ann.getEnd()));
}
}
}
return annotations;
}
The training itself is done by counting the number of occurrences for each label that was seen in the documents. The label is then the one which occurred the most in the training documents.
private DataMajorityModel trainModel(List<Annotation> aAnnotations)
throws RecommendationException
{
Map<String, Integer> model = new HashMap<>();
for (Annotation ann : aAnnotations) {
int count = model.getOrDefault(ann.label, 0);
model.put(ann.label, count + 1);
}
Map.Entry<String, Integer> entry = model.entrySet().stream()
.max(Map.Entry.comparingByValue()).orElseThrow(
() -> new RecommendationException("Could not obtain data majority label"));
String majorityLabel = entry.getKey();
int numberOfAnnotations = model.values().stream().reduce(Integer::sum).get();
double score = (float) entry.getValue() / numberOfAnnotations;
return new DataMajorityModel(majorityLabel, score, numberOfAnnotations);
}
We also compute a dummy score here which is displayed in the UI and used for e.g. active learning.
Predicting
The first thing we do when predicting is to load the model we saved during training. For every
candidate in the document, we assign the majority label, create a new annotation and add it to the CAS
.
From there, it will be read by INCEpTION and displayed to the user.
@Override
public Range predict(PredictionContext aContext, CAS aCas, int aBegin, int aEnd)
throws RecommendationException
{
DataMajorityModel model = aContext.get(KEY_MODEL).orElseThrow(
() -> new RecommendationException("Key [" + KEY_MODEL + "] not found in context"));
// Make the predictions
Type tokenType = CasUtil.getAnnotationType(aCas, DATAPOINT_UNIT);
Collection<AnnotationFS> candidates = selectOverlapping(aCas, tokenType, aBegin, aEnd);
List<Annotation> predictions = predict(candidates, model);
// Add predictions to the CAS
Type predictedType = getPredictedType(aCas);
Feature scoreFeature = getScoreFeature(aCas);
Feature scoreExplanationFeature = getScoreExplanationFeature(aCas);
Feature predictedFeature = getPredictedFeature(aCas);
Feature isPredictionFeature = getIsPredictionFeature(aCas);
for (Annotation ann : predictions) {
AnnotationFS annotation = aCas.createAnnotation(predictedType, ann.begin, ann.end);
annotation.setStringValue(predictedFeature, ann.label);
annotation.setDoubleValue(scoreFeature, ann.score);
annotation.setStringValue(scoreExplanationFeature, ann.explanation);
annotation.setBooleanValue(isPredictionFeature, true);
aCas.addFsToIndexes(annotation);
}
return new Range(candidates);
}
For a document, we consider possible candidates for a named entity to be tokens that are upper case. In a real recommender, the step of candidate extraction should be more elaborate than that, but for this tutorial, it is sufficient.
When making predictions, we also set the score feature to put a number on the quality of the annotation.
The UIMA score feature to set can be obtained by calling getScoreFeature
inside a RecommendationEngine
.
When creating predictions, make sure to call annotation.setBooleanValue(isPredictionFeature, true);
so
that INCEpTION knows it is a prediction, not a real annotation. In addition, we provide an explanation for
the score through the UIMA feature obtained by calling getScoreExplanationFeature
inside a RecommendationEngine
.
private List<Annotation> predict(Collection<AnnotationFS> candidates, DataMajorityModel aModel)
{
List<Annotation> result = new ArrayList<>();
for (AnnotationFS token : candidates) {
String tokenText = token.getCoveredText();
if (tokenText.length() > 0 && !Character.isUpperCase(tokenText.codePointAt(0))) {
continue;
}
int begin = token.getBegin();
int end = token.getEnd();
Annotation annotation = new Annotation(aModel.majorityLabel, aModel.score,
aModel.numberOfAnnotations, begin, end);
result.add(annotation);
}
return result;
}
We use the dummy score here from the training as the recommender score.
Evaluating
When configuring a recommender, it can be specified that it needs to achieve a certain score
before the recommendations are shown to the user. For that, the platform regularly evaluates
recommenders in the background. We use macro-averaged F1-score as an evaluation score.
In code, the evaluation is implemented in the evaluate
method.
Evaluation is done on a set of documents. In order to properly divide the annotations into training
and test set, a DataSplitter
is given which tells you to which data set an annotation belongs.
For the actual evaluation, we collect the true label and the predicted majority label in a
LabelPair
for each true label. A stream of these instances can then be collected with
the use of an EvaluationResultCollector
as an EvaluationResult
object - the result of the
evaluation. This object provides access to calculations for token-based accuracy, macro-averaged
precision, recall and F1-score. This F1-score is later used for
comparison with the user-defined threshold to activate the recommender.
@Override
public EvaluationResult evaluate(List<CAS> aCasses, DataSplitter aDataSplitter)
throws RecommendationException
{
List<Annotation> data = extractAnnotations(aCasses);
List<Annotation> trainingData = new ArrayList<>();
List<Annotation> testData = new ArrayList<>();
for (Annotation ann : data) {
switch (aDataSplitter.getTargetSet(ann)) {
case TRAIN:
trainingData.add(ann);
break;
case TEST:
testData.add(ann);
break;
case IGNORE:
break;
}
}
int trainingSetSize = trainingData.size();
int testSetSize = testData.size();
double overallTrainingSize = data.size() - testSetSize;
double trainRatio = (overallTrainingSize > 0) ? trainingSetSize / overallTrainingSize : 0.0;
if (trainingData.size() < 1 || testData.size() < 1) {
log.info("Not enough data to evaluate, skipping!");
EvaluationResult result = new EvaluationResult(DATAPOINT_UNIT.getSimpleName(),
getRecommender().getLayer().getUiName(), trainingSetSize, testSetSize,
trainRatio);
result.setEvaluationSkipped(true);
return result;
}
DataMajorityModel model = trainModel(trainingData);
// evaluation: collect predicted and gold labels for evaluation
EvaluationResult result = testData.stream()
.map(anno -> new LabelPair(anno.label, model.majorityLabel))
.collect(toEvaluationResult(DATAPOINT_UNIT.getSimpleName(),
getRecommender().getLayer().getUiName(), trainingSetSize, testSetSize,
trainRatio));
return result;
}
RecommendationFactory
The RecommendationFactory
is used to create a new recommender instance. It also defines for which
types of layers and features the recommender itself can be used. Here, we decided to only support
token span layers without cross sentence annotations.
@ExportedComponent
@Component
public class DataMajorityRecommenderFactory
extends RecommendationEngineFactoryImplBase<Void>
{
// This is a string literal so we can rename/refactor the class without it changing its ID
// and without the database starting to refer to non-existing recommendation tools.
public static final String ID = "de.tudarmstadt.ukp.inception.recommendation.imls.datamajority.de.tudarmstadt.ukp.inception.recommendation.imls.datamajority.DataMajorityNerRecommender";
@Override
public String getId()
{
return ID;
}
@Override
public RecommendationEngine build(Recommender aRecommender)
{
return new DataMajorityNerRecommender(aRecommender);
}
@Override
public String getName()
{
return "Data Majority Recommender";
}
@Override
public boolean accepts(AnnotationLayer aLayer, AnnotationFeature aFeature)
{
if (aLayer == null || aFeature == null) {
return false;
}
return (asList(SINGLE_TOKEN, TOKENS).contains(aLayer.getAnchoringMode()))
&& !aLayer.isCrossSentence() && SpanLayerSupport.TYPE.equals(aLayer.getType())
&& CAS.TYPE_NAME_STRING.equals(aFeature.getType()) || aFeature.isVirtualFeature();
}
}
External recommender
Overview
This section describes the External Recommender API for INCEpTION. An external recommender is a classifier whose functionality is exposed via a HTTP web service. It can predict annotations for given documents and optionally be trained on new data. This document describes the endpoints a web service needs to expose so it can be used with INCEpTION. The documents that are exchanged are in form of a UIMA CAS. For sending, they have to be serialized to CAS XMI. For receiving, it has to be deserialized back. There are two main libraries available that manage CAS handling, one is the Apache UIMA Java SDK, the other one dkpro-cassis (Python).
API Endpoints
Predict annotations for a single document
POST /predict
Description
Sends a CAS together with information about the layer and feature to predict to the external recommender. The external recommender then returns the CAS annotated with predictions.
Parameters
Type | Name | Description | Schema |
---|---|---|---|
Body |
body |
Document CAS for which annotations will be predicted |
Example HTTP request
{
"metadata" : {
"layer" : "de.tudarmstadt.ukp.dkpro.core.api.ner.type.NamedEntity",
"feature" : "value",
"projectId" : 1337,
"anchoringMode" : "tokens",
"crossSentence" : false
},
"document" : {
"xmi" : "<?xml version=\"1.0\" encoding=\"UTF-8\"?> <xmi:XMI xmlns:tcas=\"http:///uima/tcas.ecore\" xmlns:xmi=\"http://www.omg.org/XMI\" xmlns:cas=\"http:///uima/cas.ecore\" xmlns:cassis=\"http:///cassis.ecore\" xmi:version=\"2.0\"> <cas:NULL xmi:id=\"0\"/> <tcas:DocumentAnnotation xmi:id=\"8\" sofa=\"1\" begin=\"0\" end=\"47\" language=\"x-unspecified\"/> <cas:Sofa xmi:id=\"1\" sofaNum=\"1\" sofaID=\"mySofa\" mimeType=\"text/plain\" sofaString=\"Joe waited for the train . The train was late .\"/> <cas:View sofa=\"1\" members=\"8\"/> </xmi:XMI>",
"documentId" : 42,
"userId" : "testuser"
},
"typeSystem" : "<?xml version=\"1.0\" encoding=\"UTF-8\"?> <typeSystemDescription xmlns=\"http://uima.apache.org/resourceSpecifier\"> <types> <typeDescription> <name>uima.tcas.DocumentAnnotation</name> <description/> <supertypeName>uima.tcas.Annotation</supertypeName> <features> <featureDescription> <name>language</name> <description/> <rangeTypeName>uima.cas.String</rangeTypeName> </featureDescription> </features> </typeDescription> </types> </typeSystemDescription>"
}
Example HTTP response
{
"document" : "<?xml version=\"1.0\" encoding=\"UTF-8\"?> <xmi:XMI xmlns:tcas=\"http:///uima/tcas.ecore\" xmlns:xmi=\"http://www.omg.org/XMI\" xmlns:cas=\"http:///uima/cas.ecore\" xmlns:cassis=\"http:///cassis.ecore\" xmi:version=\"2.0\"> <cas:NULL xmi:id=\"0\"/> <tcas:DocumentAnnotation xmi:id=\"8\" sofa=\"1\" begin=\"0\" end=\"47\" language=\"x-unspecified\"/> <cas:Sofa xmi:id=\"1\" sofaNum=\"1\" sofaID=\"mySofa\" mimeType=\"text/plain\" sofaString=\"Joe waited for the train . The train was late .\"/> <cas:View sofa=\"1\" members=\"8\"/> </xmi:XMI>"
}
Train recommender on a set of documents
POST /train
Description
Sends a list of CASses to the external recommender for training. No response body is expected.
Parameters
Type | Name | Description | Schema |
---|---|---|---|
Body |
body |
List of documents CAS whose annotations will be used for training |
Responses
HTTP Code | Description | Schema |
---|---|---|
204 |
Successful training |
No Content |
429 |
Too many training requests have been sent, the sender should wait a while until the next request |
No Content |
Example HTTP request
{
"metadata" : {
"layer" : "de.tudarmstadt.ukp.dkpro.core.api.ner.type.NamedEntity",
"feature" : "value",
"projectId" : 1337,
"anchoringMode" : "tokens",
"crossSentence" : false
},
"documents" : [ {
"xmi" : "<?xml version=\"1.0\" encoding=\"UTF-8\"?> <xmi:XMI xmlns:tcas=\"http:///uima/tcas.ecore\" xmlns:xmi=\"http://www.omg.org/XMI\" xmlns:cas=\"http:///uima/cas.ecore\" xmlns:cassis=\"http:///cassis.ecore\" xmi:version=\"2.0\"> <cas:NULL xmi:id=\"0\"/> <tcas:DocumentAnnotation xmi:id=\"8\" sofa=\"1\" begin=\"0\" end=\"47\" language=\"x-unspecified\"/> <cas:Sofa xmi:id=\"1\" sofaNum=\"1\" sofaID=\"mySofa\" mimeType=\"text/plain\" sofaString=\"Joe waited for the train . The train was late .\"/> <cas:View sofa=\"1\" members=\"8\"/> </xmi:XMI>",
"documentId" : 42,
"userId" : "testuser"
} ],
"typeSystem" : "<?xml version=\"1.0\" encoding=\"UTF-8\"?> <typeSystemDescription xmlns=\"http://uima.apache.org/resourceSpecifier\"> <types> <typeDescription> <name>uima.tcas.DocumentAnnotation</name> <description/> <supertypeName>uima.tcas.Annotation</supertypeName> <features> <featureDescription> <name>language</name> <description/> <rangeTypeName>uima.cas.String</rangeTypeName> </featureDescription> </features> </typeDescription> </types> </typeSystemDescription>"
}
Definitions
Document
Name | Description | Schema |
---|---|---|
documentId |
Identifier for this document. It is unique in the context of the project. |
integer |
userId |
Identifier for the user for which recommendations should be made. |
string |
xmi |
CAS as XMI |
string |
Metadata
Name | Description | Schema |
---|---|---|
anchoringMode |
Describes how annotations are anchored to tokens. Is one of 'characters', 'singleToken', 'tokens', 'sentences'. |
string |
crossSentence |
True if the project supports cross-sentence annotations, else False |
boolean |
feature |
Feature of the layer which should be predicted |
string |
layer |
Layer which should be predicted |
string |
projectId |
The id of the project to which the document(s) belong. |
integer |
PredictRequest
Name | Description | Schema |
---|---|---|
document |
Example : |
|
metadata |
Example : |
|
typeSystem |
Type system XML of the CAS |
string |
PredictResponse
Name | Description | Schema |
---|---|---|
document |
CAS with annotations from the external recommender as XMI |
string |
Train
Name | Description | Schema |
---|---|---|
documents |
CAS as XMI |
< Document > array |
metadata |
Example : |
|
typeSystem |
Type system XML of the CAS |
string |
Encoding annotation suggestions
This section explains how annotation suggestions can be encoded in the response to a predict
call.
Note that a recommender can only produce suggestions for one feature on one layer. The name of the layer and feature are contained in the request to the predict
call and only suggestions generated for that specific layer and feature will be processed by INCEpTION when the call returns.
For the purpose of producing annotation suggestions, this specific layer is extended with additional features that can be set. Some of these features start with the name of the feature (we use <FEATURE_NAME>
as a placeholder for the actual feature name below) to be predicted and then add a suffix:
-
inception_internal_predicted
: this boolean feature indicates that an annotation was added by the external recommender. It allows the system to distinguish between annotations that already existed in the document and annotations that the recommender has created. Only annotations where this flag is set totrue
will be processed by INCEpTION. -
<FEATURE_NAME>
: this feature takes the label that the external recommender assigns. -
<FEATURE_NAME>_score
(optional): this floating-point (double) feature can be used to indicate the score assigned to a predicted label. -
<FEATURE_NAME>_score_explanation
(optional): this string feature can be used to provide an explanation for the score. This explanation is shown on the annotation page when the user inspects a particular suggestion (note that not all editors may support displaying explanations). -
<FEATURE_NAME>_auto_accept
(optional): this feature can be set toon-first-access
to force-accept an annotation into a document when an annotator accesses a document for the first time. This should only be used in conjunction with non-trainable recommenders and with the option Wait for suggestions from non-trainable recommenders when opening document in the recommender project settings. Thus, when an annotator opens a document for the first time, the system would wait for recommendations by non-trainable (pre-trained) recommenders and then directly accept any of the suggestions that the recommender has marked to uto-accept on-first-access. When the annotator resets a document via the action bar, this procedure is also followed. This provides a convenient way of "pre-annotating" documents with the help of external recommenders. Note though that an annotator has to actually open a document in order for this process to trigger.
Active Learning
The active learning module aims to guide the user through recommendations in such a way that the the judgements made by the user are most informative to the recommenders. The goal is to reduce the required user interactions to a minimum. The module consists of the following classes and interfaces:
-
The
ActiveLearningService
interface and its default implementationActiveLearningServiceImpl
which provide access to the ranked suggestions. -
The
ActiveLearningStrategy
interface which allows plugging in different sampling strategies. -
The
UncertaintySamplingStrategy
class which is currently the only sampling strategy available. -
The
ActiveLearningSidebar
class which provides the active learning sidebar for the annotation page. Here the user can accept/reject/correct/skip suggestions.
The active learning module relies on the recommendation module for the actual annotation recommendations. This means that the active learning module does not directly make use of the user feedback. If suggestions are accepted, they are used in the next train/predict run of the recommendation module as training data. The active learning module then samples the new annotation suggestions from this run and updates the order in which it offers the suggestions to the user.
-
ActiveLearningSuggestionOfferedEvent
- active learning has pointed the user at a recommentation -
ActiveLearningRecommendationEvent
- user has accepted/rejected a recommendation -
ActiveLearningSessionStartedEvent
- user has opened an active learning session -
ActiveLearningSessionCompletedEvent
- user has closed the active learning session
Sampling strategies
Uncertainty sampling
Currently, there is only a single sampling strategy, namely the UncertaintySamplingStrategy
. It
it compares the scores of the annotation suggestion. The smaller the difference between
the best and the second best score, the earlier the suggestion is proposed to the user. The
scores produced by different recommenders can be on different scales and are therefore
not really comparable. Thus, the strategy only compares suggestions from the same recommender to
each other. So if recommender A produces two suggestions X and Y, they are compared to each other.
However, if there are two recommenders A and B producing each one suggestion X and Y, then X and Y
are not compared to each other.
Event Log
The event logging module allows catching Spring events and logging them them to the database. It consists of the following classes and interfaces:
-
The
EventRepository
interface and its default implementationEventRepositoryImpl
which serve as the data access layer for logged events. -
The
EventLoggingListener
which hooks into Spring, captures events, and then uses theEventRepository
to log them. -
The
EventLoggingAdapter
interface. Spring components implementing this interface are used to extract information from Spring events and to convert them into a format suitable to be logged. -
The
LoggedEvent
entity class which maps the logged events to the database. -
The
LoggedEventExporter
andExportedLoggedEvent
which are used to export/import the event log as part of a project export/import.
The log module comes with a number of adapters for common events such as annotation
manipulation, changes to the project configuration, etc. Any event for which no specific
adapter exists is handled using the GenericEventAdapter
which logs only general
information (e.g. the timestamp, current user, type of event) but no event-specific
details (e.g. current project, current document, or even more specific details). Note that
even the GenericEventAdapter
skips logging certain Spring events related to session
management, authorization, and the Spring context life-cycle.
Event Logging Adapters
New logging adapters should be created in the module which provides the event they are logging. Logging adapters for events generated outside INCEpTION (i.e. in upstream code) are usually added to the log module itself.
To add support for a logging a new event, create a Spring component class which implements the
EventLoggingAdapter
interface. Implement the following methods depending on the context in which
the event is triggered:
-
getProject(Event)
if the event is triggered in the context of a specific project (applies to most events); -
getDocument(Event)
if the event is related to a specific source document (e.g. applies to events triggered during annotation). -
getDocument(Event)
if the event is related to a specific annotator (e.g. applies to events triggered during annotation).
The methods getEvent
, getUser
and getCreated
normally do not need to be implemented.
Most event adapters implement the getDetails
method. This method must return a JSON string which
contains any relevant information about the event not covered by the methods above. E.g. for an
annotation manipulation event, it would contain information helping to identify the annotation and
the state before and after the manipulation. In order to generate this JSON string, the adapter
typically contains an inner class called Details
to which the detail information from the event
is copied and which is then serialized to JSON using JSONUtil.toJsonString(…)
.
Knowledge base
Schema mapping
An IRI Schema defines the following attributes that are used for making queries in a knowledge base.
Attribute | Description | Example Value |
---|---|---|
Class IRI |
Class of resources that are classes. |
rdfs:Class |
Subclass IRI |
Property that defines a subclass of relation between classes. |
rdfs:subClassOf |
Type IRI |
Property that defines which class a resource belongs to |
rdf:type |
Label IRI |
Property that defines a human readable label for a class or instance |
rdfs:label |
Description IRI |
Property that defines a description for a class or instance |
rdfs:comment |
Property IRI |
Class of resources that are properties |
rdf:Property |
Subproperty IRI |
Property that defines a subproperty of relation between properties. |
rdfs:subPropertyOf |
Property Label IRI |
Property that defines a human readable label for a property |
rdfs:label |
Property Description IRI |
Property that defines a description for a property |
rdfs:comment |
There are multiple classes in the knowledge base module that model the IRI Schema of a knowledge base. All the classes share that they have a single class-attribute for each IRI in the IRI Schema. However each class has a different use case. The relevant classes are shown here.
If the structure of the general IRI Schema is changed (e.g. a new attribute is added) all the classes need to be adjusted.* |
Class | Usage |
---|---|
|
General model for a knowledge base in frontend and backend components. |
|
Read pre-configured knowledge base profiles from a yaml file.
The actual IRI Schema is modeled in KnowledgeBaseMapping.java.
The yaml file is located at: |
|
Defines some specific IRI Schemas (e.g RDF, WIKIDATA, SKOS). |
|
Export a knowledge base configuration when a project is exported. |
Concept Linking
The concept linking module is used to find items from a knowledge base that match a certain query
and context. It is used e.g. by the ConceptFeatureEditor
to display items which match a concept
mention and it can use the mention’s context to rank (and optimally disambiguate) the candidate
items. It can also be used for non-contextualized queries, e.g. via the search field on the
knowledge base browsing page. The module consists of the following classes and interfaces:
-
The
ConceptLinkingService
interface and its default implementationConceptLinkingServiceImpl
which is the main entry point for locating KB items. -
The
EntityRankingFeatureGenerator
interface. Spring beans which implement this interface are automatically picked up by theConceptLinkingServiceImpl
and used to rank candidates.
Ranking
Feature generators
The module currently uses primarily the LevenshteinFeatureGenerator
which calculate the Levenshtein
distance between the mention text and the KB item label as well as between the query text (e.g.
entered into the auto-complete field of the ConceptFeatureEditor
) and the KB item label.
External Editors
This section introduces the mechanism for registering external editors in INCEpTION. An external editor is an editor plugin implemented in JavaScript / TypeScript.
In order to use an external editor, create a folder editors
in the INCEpTION home folder, then
within that folder create another folder for the editor plugin. The name of the folder will be the
identifier of the editor plugin (e.g. if you would later rename the folder, the editor ID saved
in the editor user preference would become invalid).
Within the editor folder, create a plugin descriptor file named plugin.json
. This file contains all
important information required by INCEpTION to use the editor.
The way the plugin descriptor needs to be set up depends mainly on whether the editor plugin takes care of rendering the full document or only the annotations. However, some settings are generic for any type of editor plugin:
-
name
: the human-readable name for the editor -
factory
: the JavaScript expression to access the annotation editor factory provided by the plugin
plugin.json
for external editor{
"name": "My Editor (external)",
"factory": "MyEditor.factory()",
"view": "iframe:cas+xhtml+xml",
"scripts": [
"dist/MyEditor.min.js"
],
"stylesheets": [
"dist/MyEditor.min.css"
]
}
Document-rendering editors
A document-rendering editor loads the document and annotation data from the backend and then renders the document including the annotations. This is typically the case for editors using SVG to display the document. The editor has the ability to define the layout of the document in such a ways that the annotations fit nicely.
For document-rendering editors, the plugin.json
file offers the following settings:
-
view
: the HTML file used as a template for the editor plugin. The value must start withplugin:
followed by a path to the HTML file within the plugin. E.g. if a template fileeditor.html
is next to theplugin.json
file in the same folder, useplugin:editor.html
.
plugin.json
for document-rendering editor{
"name": "My Editor (external)",
"factory": "MyEditor.factory()"
"view": "plugin:editor.html"
}
The external editor mechanism loads the template file within an IFrame that is embedded in the
annotation page. Any CSS or JavaScript files needed by the plugin must be referenced by the template
file using a relative location. For example let’s assume a file editor.html
which needs to load
a editor.css
style sheet and an editor.js
JavaScript file:
<meta charset="utf-8">
<title>DoccanoSequenceEditor demo</title>
<script src="./editor.js"></script>
<link rel="stylesheet" href="./editor.css">
<div id="editor"/>
Editors using server-side document views
Some annotation editors overlay their annotations on an already existing document view. For example,
annotations could be overlaid on a HTML or PDF document. In this case, the external editor mechanism
can be configured to use a particular DocumentView
plugin on the server to render the document and
to display it within an IFrame that is embedded in the annotation page. The editor plugin JavaScript
and CSS stylesheet files required are then injected into this IFrame as well.
{
"name": "My editor",
"factory": "MyEditor.factory()",
"view": "iframe:cas+html,
"scripts": [ "editor.js" ],
"stylesheets": [ "editor.css" ],
}
Views
Currently supported views are:
-
iframe:cas+xml
: Renders XML contained in the CAS into a generic XML IFrame in the editor area. -
iframe:cas+xhtml+xml
: Renders XML contained in the CAS into an XHTML+XML IFrame in the editor area. HTML head and body elements are added automatically. The XML is rendered into the body.
Policies
Every editor should provide a policy.yaml
right next to the plugin.json
. The policy.yaml
declares
all elements and attributes that are supported by the editor. This policy file should be written as
a safelist, i.e. it should say exactly what is permitted instead of saying what is not allowed.
Allowing the wrong elements and attributes may cause security problems, e.g. if they can contain
executable JavaScript or load data from remote locations.
There are several elements like script
, meta
, applet
, link
, iframe
as well as a
which are
and JavaScript event attributes always filtered out.
If an editor does not provide a policy.yaml
file, a default built-in policy is used which allows
most HTML formatting elements.
policy.yaml
filename: Example policy
version: 1.0
case_sensitive: false
default_attribute_action: DROP
default_element_action: DROP
debug: false
policies:
- { elements: ["html"], action: "PASS" }
- { elements: ["p", "div"], action: "PASS" }
- { elements: ["tr", "th"], action: "PASS" }
- { attributes: ["class"], action: "PASS" }
- { attributes: ["style"], action: "DROP" }
- {
attributes: ["title"],
matching: "[a-zA-Z0-9]*",
on_elements: ["div"],
action: "PASS",
}
There are two types of policies: element policies, and attribute policies.
Element policies
An element policy must contain the key elements
which takes a list of element names and the key
action
which can be either PASS
or DROP
. If an element is dropped, all child elements are
also dropped. Text within the child elements is replaced by an equivalent amount of space such that
offsets are not affected.
Note that the root element of your documents should always be allowed to PASS
, otherwise the
document may fail to render.
It is possible to preserve elements within dropped elements by explicitly allowing the nested
elements to PASS
.
policies:
- { elements: ["root", "child2"], action: "PASS" }
- { elements: ["child1"], action: "DROP" }
Using this policy, a document <root><child1><child2>text</child2></child1/></root>
will be transformed
to <root><child2>text</child2></root>
.
Attribute policies
An attribute policy must contain the key attributes
which takes a list of attribute names,
and the key action
which can be either PASS
or DROP
. Optionally it may contain the the key
onElements
which takes a list of element names. If this key is present, the policy only affects
the attributes on the given elements, otherwise the policy affects all elements globally. Also, the
key matching
can be optionally included to affect only attributes whose value matches the regular
expression provided as the value to matching
.
When declaring attribute policies, the order matters. E.g. you should declare more specific policies
(e.g. such having a onElements
or matching
key) before less specific or global policies.
Debugging
To debug the rules, you can set the key debug
to true
and reload your editor in the browser.
Restarting the whole application is not required. When inspecting the content of the editor IFrame
in the browser’s developer tools, you will see that elements and attributes matched by a DROP
policy have been prefixed with MASKED-
instead of being fully dropped. Do not forget to set
debug back to false
or to remove the key for actual use.
Editor implementation
Editors can be implemented in JavaScript or languages that can be compiled to JavaScript such as
TypeScript. To facilitate the implementation, INCEpTION provides a set of interface definitions for
TypeScript, in particular AnnotationEditorFactory
and AnnotationEditor
.
To make use of these, set up a package.json
file next to the plugin.json
file. In the package.json
file, add @inception-project/inception-js-api
as a dependency. The example below also already includes
TypeScript and ESBuild as dependencies.
{
"name": "My Editor",
"version": "0.0.1",
"scripts": {
"build": "esbuild src/main.ts --target=es6 --bundle --sourcemap --global-name=MyEditor --outfile=editor.js"
},
"dependencies": {
"@inception-project/inception-js-api": "*"
},
"devDependencies": {
"esbuild": "^0.13.12",
"typescript": "^4.4.2"
}
}
The @inception-project/inception-js-api module should eventually be available from the NPMJS. However, if you
have INCEpTION checked out locally, you may want to build your editor against the latest local version. To do this,
first build INCEpTION once e.g. using mvn clean install or within your IDE. Then go to the folder
inception-application/inception/inception-js-api/src/main/ts in your checkout and run npm link . After that, go to the
folder containing your editor plugin and run npm link "@inception-project/inception-js-api" there.
|
The minimal editor implementation consists of three JavaScript/TypeScript files:
-
main.ts
: the entry point into your editor module. It is referenced by thebuild
script in thepackage.json
file and provides access to your editor factory. -
MyEditorFactory.ts
: a factory class implementing theAnnotationEditorFactory
interface which facilitates access to your editor for the external editor mechanism. In particular, it provides means of instantiating and destroying an editor instance. -
MyEditor.ts
: the actual editor class implementing theAnnotationEditor
interface.
main.ts
file skeletonimport { MyEditorFactory } from './MyEditorFactory';
const INSTANCE = new MyEditorFactory();
export function factory(): MyEditorFactory {
return INSTANCE;
}
MyEditorFactory.ts
file skeletonimport type { AnnotationEditorFactory, AnnotationEditorProperties, DiamClientFactory } from "@inception-project/inception-js-api"
const PROP_EDITOR = "__editor__";
export class MyEditorFactory implements AnnotationEditorFactory {
public async getOrInitialize(element: HTMLElement, diam : DiamClientFactory, props: AnnotationEditorProperties): Promise<RecogitoEditor> {
if (element[PROP_EDITOR] != null) {
return element[PROP_EDITOR];
}
const ajax = diam.createAjaxClient(props.diamAjaxCallbackUrl);
const bodyElement = document.getElementsByTagName("body")[0];
element[PROP_EDITOR] = new MyEditor(bodyElement, ajax);
return element[PROP_EDITOR];
}
public destroy(element: HTMLElement) {
if (element[PROP_EDITOR] != null) {
element[PROP_EDITOR].destroy();
}
}
}
MyEditor.ts
file skeletonimport type { AnnotationEditor, DiamAjax } from "@inception-project/inception-js-api";
const ANNOTATIONS_SERIALIZER = "Brat"; // The annotation format requested from the server
export class RecogitoEditor implements AnnotationEditor {
private ajax: DiamAjax;
public constructor(element: HTMLElement, ajax: DiamAjax) {
this.ajax = ajax;
// Add editor code here - usually the editor code would be in a set of additional classes which would be
// instantiated and configured here and be bound to the given HTML element. Also, you would typically
// register event handlers here that call methods like `createAnnotation` and `selectAnnotation` below, e.g.
// when marking some text or clicking on an existing annotation.
this.loadAnnotations();
}
public loadAnnotations(): void {
this.ajax.loadAnnotations(ANNOTATIONS_SERIALIZER)
.then(data => {
// Place code here that causes your editor to re-render itself using the data received from the server
});
}
public destroy(): void {
// Depending on your editor implementation, it may be necessary to clean up stuff, e.g. to prevent memory leaks.
// Do these cleanup actions here.
}
private createAnnotation(annotation): void {
// This is an example event handler to be called by your editor. For example, it could pick up start and end offsets
// of the text to be annotated as well as the annotated text itself and send these to the server using the DIAM AJAX API
// that was injected by the exsternal editor mechanism. The server will update its state and send a `loadAnnotations()`
// call to the browser to trigger a re-rendering.
this.ajax.createSpanAnnotation([[annotation.begin, annotation.end]], annotation.text);
}
private selectAnnotation(annotation): void {
// This is an example event handler to be called by your editor. For example, it could pick up the annotation ID from
// the selected annotation and send it to the server using the DIAM AJAX API that was injected by the external editor
// mechanism. The server will update its state and send a `loadAnnotations()` call to the browser to trigger a re-rendering.
this.ajax.selectAnnotation(annotation.id);
}
}
PDF Annotation Editor
The PDF-Editor module allows the view and annotation of PDF documents.
The module consists of several parts:
-
the
VisualPdfReader
is using pdfbox to extract the text from the PDF files. During this process, it keeps track of the positions of each glyph (the "visual model") and also includes this information as annotations in the CAS. Theorg.dkpro.core.api.pdf.type.PdfPage
type encodes information about page boundaries while theorg.dkpro.core.api.pdf.type.PdfChunk
type encodes information about short sequences of glyphs that have the same orientation and script direction (typically belonging to the same word). -
the
PdfDocumentFrameView
is using pdf.js to display the PDF file in the browser. It provides endpoints for the browser to access the PDF as well as for obtaining the visual model. -
the
PdfAnnotationEditor
which builds on thePdfDocumentFrameView
and includes the client-side JavaScript code (loosely based on PDFAnno). For the communication of the editor with the backend, the INCEpTION JS editor API (DIAM) is used.
PDF Annotation Editor (legacy)
Legacy feature. To use this functionality, you need to enable it first by adding ui.pdf-legacy.enabled=true to the settings.properties file.
|
Support for this feature will be removed in a future version. The replacement is PDF Annotation Editor.
The PDF-Editor module allows the view and annotation of PDF documents. This is implemented using PDFAnno, PDFExtract and DKPro PDF Reader. The choice for PDFAnno and other implementation choices are explained in the following.
Selecting a PDF Annotation Tool
There are only few requirements to a PDF annotation tool for integration into INCEpTION. It must provide support for span and relation annotations and it should also be lightweight and easily modifiable to fit into INCEpTION.
There are two PDF annotation tools up for discussion. The first one is PDFAnno and the second is Hypothes.is. Both tools are web-based and open source software available on GitHub.
PDFAnno is a lightweight annotation tool that only supports the PDF format. It was created specifically to solve the lack of free open source software for annotating PDF documents which is also capable of creating relations between annotations. This is described in the publication about PDFAnno by Shindo et al.
Hypothes.is is a project that was created to provide an annotation layer over the web. The idea is to be able to create annotations for all content available on the internet and to share it with other people. Hence Hypothes.is provides the functionality to annotate PDF documents.
PDFAnno compared to Hypothes.is comes with a smaller code base and is less complex. Both editors feature span annotations, however only PDFAnno provides the functionality to create relations between span annotations which is required in INCEpTION. As Hypothes.is was designed to share annotations with others a login mechanism is part of the software.
PDFAnno provides relations, is more lightweight and does not have a login functionality, which would have to be removed. Hence PDFAnno fits the requirements better than Hypothes.is and was chosen as the PDF annotation tool for integration into INCEpTION.
Differences in PDF Document Text Extractions
PDFAnno uses PDF.js to render PDF documents in the browser. The tool PDFExtract is used to extract information about the PDF document text. It produces a file in which each line contains information about one character of the text. Information includes the page, the character and the position coordinates of the character in the PDF document, in the given order and separated by a tab character. An example:
1 E 0 1 2 3 1 x 4 5 6 7 1 a 8 9 10 11 1 m 12 13 14 15 1 p 16 17 18 19 1 l 20 21 22 23 1 e 24 25 26 27 2 [MOVE_TO] 28 29 2 NO_UNICODE 30 31 32 33
There are also draw operations included which are of no relevance for the use in
INCEpTION.
Characters which have no unicode mapping have the value NO_UNICODE
.
The PDFExtract file does not contain information about any whitespaces that
occur in the PDF document text.
PDFAnno requires the PDF document and the PDFExtract file to work.
The PDF document can be obtained from the INCEpTION backend.
To also provide the PDFExtract file, the tool was slightly modified so that it
can be used as a library in INCEpTION.
PDFAnno provides an API for handling annotations. It is possible to import a list of annotations by providing an URL for download. This list has to be in the TOML format. Span annotations require the begin and end positions of the characters it covers. This positions are equal to the line number of characters in the PDFExtract file. A span annotation example in TOML format:
[[span]] id = "1" page = 1 label = "" color = "#ff00ff" text = "Example" textrange = [1, 7]
The Brat editor used in INCEpTION works only on plain text. For PDF documents this plain text is obtained by the use of DKPro PDF Reader. The reader extracts the text information from the PDF document and performs operations to ensure correct text ordering and to replace certain character sequences with substitutes from a substitution table.
As the extractions between PDFAnno and INCEpTION differ a mapping of those representations must be implemented to ensure annotations can be exchanged between the frontend and the backend and are usable across all editor modes.
Preparing Representations
To use a mapping method between the text representation of PDFAnno and INCEpTION at first they must be preprocessed to have a similar structure.
As the PDFExtract file does not only contain the text string, first
the characters of the file need to be obtained and appended to a
string. All draw operations and NO_UNICODE
lines are ignored.
As DKPro PDF Reader uses a substitution table to sanitize the document text,
the same substitution table is used to sanitize the obtained string.
The PDFExtract file does not contain any whitespaces present in the document text, however DKPro PDF Reader preserves them. The whitespaces are removed from the DKPro PDF Reader string to have a similar structure to the PDFExtract sanitized string content.
Even though both representations now are in a similar shape it can still happen that the content in both strings differs. For example ordering of text areas could be messed up which can especially happen for PDF documents that contain multiple text columns on one page. As both representations are not equal even after preprocessing, a mapping algorithm has to be implemented to find the text of annotations from one representation in the respective other representation.
Mapping Annotations
There are multiple ways to achieve a mapping between PDFAnno and INCEpTION for annotations. Two methods were tested during development: exact string search with context and sequence alignment.
The first option is to make an exact search for the annotation text. However as annotations often cover only one token an exact search for the annotation text would result in multiple occurrences. To get a unique result it is required to add context to the annotation text. As this still can yield multiple occurrences, context is expanded until a unique mapping or no mapping at all is found. Performing this for all annotations results in a lot of string search operations. However the performance can be improved by searching for all annotations in the target string at once with the help of the Aho-Corasick algorithm.
Another approach is to use sequence alignment methods which are popular in bioinformatics. PDF document texts are rather large and most sequence alignment algorithms require O(M x N) memory space, where M and N are the size of the two sequences. This results in a large memory consumption on computing the alignment, hence an algorithm should be used that works with less memory. Such an algorithm is Hirschbergs algorithm. It consumes only O(min(M,N)) memory.
The advantage of the sequence alignment method would be a direct mapping between the representation of PDFExtract and DKPro PDF Reader. However, during testing for larger documents, for example 40 pages, the duration until Hirschbergs algorithm finished was too long and would be unsatisfying for a user. The exact string search however takes increasingly longer to compute mappings the larger the document is and the more annotations have to be mapped. As discussed the Aho-Corasick algorithm reduces the time. However, this still does not scale well for larger documents. To overcome this issue a page wise rendering of annotations was introduced. When navigating through the PDF document in PDFAnno annotations are rendered dynamically per page. In detail, this means whenever the user moves through the document, the current page changes and the user stops movement for 500 ms, the annotations for the previous, current and next page are rendered. This way large documents can be handled by the PDF editor without long wait times for the user.
The exact string search seemed to perform well in terms of finding matching occurrences for annotations in both directions. For the manually tested documents all annotations were found and matched.
File Formats
This section explains how to support different type of file formats for importing and exporting
annotated texts. The file format supports are mainly based on DKPro-Core-compatible reader and
writer UIMA components. They are then simply made known to the application via a FormatSupport
implementation.
The extension mechanism consists of the following classes and interfaces:
-
The
FormatSupport
interface which provides the API necessary to make file formats known to the application. It providse means to fetch the format ID and the human-readable format name shown in the UI. It also allows to create reader and/or writer components. Various implementations of this interface are included with the application, e.g. theWebAnnoTsv3FormatSupport
. -
The
ImportExportService
interface and its implementationImportExportServiceImpl
which provide access to the registered format supports and also offers methods to import and export annotated text in any of the formats.
Repository
The repository is a folder below the INCEpTION home folder which contains most of the applications data that is not stored in the database. This includes in particular the original documents imported into the application as well as annotations made by the users.
The source document data is managed by the DocumentService
while the annotated documents are managed
by the CasStorageService
.
β£ <project ID>.log
- project log file
β project
β <project ID>
- data related to the project with the given ID
β£ document
- managed by the CasStorageService
β β <document ID>
β β annotation
β β£ INITIAL_CAS.ser
- initial converted version of the source document
β β£ <user ID>.ser
β β <user ID>.ser.<timestamp>.bak
- backups of the user’s annotations (if enabled)
β£ source
- managed by the DocumentService
β β <original file>
- original source file
β settings
β <user ID>
- user-specific preferences
β annotation.properties
- annotation preferences
include::./developer-guide/release.adoc