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Maui Server HTTP API

This document describes the HTTP API of Maui Server. Maui Server is a RESTful wrapper around the Maui content indexer. Version 1.0 of Maui Server only exposes the subject indexing functionality of Maui, that is, it detects the main topics of documents, where the list of possible topics are provided as a SKOS taxonomy.

Concepts

A Maui Server is a collection of taggers. Each tagger is an auto-tagging service that provides tag recommendations for a particular vocabulary (a.k.a. taxonomy) and has been trained with a particular training set of documents. By supporting multiple taggers on a single Maui Server, we can do auto-tagging for multiple separate vocabularies.

Each tagger comes with several associated resources, such as a configuration resource, a training resource, and the actual tag suggestion service.

Typical flow of API calls

Preparing the tagger:

  1. Create a tagger (POST to home resource).
  2. Configure the tagger (optional, PUT or POST to tagger configuration resource).
  3. Upload the SKOS vocabulary (PUT to tagger vocabulary resource).
  4. Train the tagger by uploading a training corpus (POST to tagger training resource).

Using the tagger:

  1. Call the tag suggestion service to provide tag recommendations for a document (POST to tagger suggestion resource).
  2. Check the log (GET to tagger log resource—not yet implemented!).

Overview

Resource URL pattern GET PUT POST DELETE
Home / List taggers (JSON) Create tagger (formencoded)
Tagger /{tagger-id} Tagger status (JSON) Delete tagger
Tagger configuration /{tagger-id}/config Show config (JSON) Replace config (JSON) Update config (JSON, formencoded) Reset config
Tagger vocabulary /{tagger-id}/vocab Return vocabulary (SKOS) Replace vocabulary (SKOS) Clear vocabulary
Tagger training /{tagger-id}/train Training status (JSON) Send training documents (JSON) Clear model
Tagger suggestions /{tagger-id}/suggest Service description (JSON) Provide recommendations for document (formencoded)
Cross-validation /{tagger-id}/xvalidate Get status (JSON) Send training documents (JSON) Clear results

Error handling

For any response with an HTTP status code other than 2XX, a body in JSON format is included, with the following keys:

Key Description Optional?
status HTTP status code (int)
status_text HTTP status text (e.g., "Not Found")
message Error message
field Request key/field/param causing the error Y
value Value of the offending request field Y
stacktrace Java stacktrace for 500 errors Y

The following status codes indicate exceptional situations:

Code Situation
204 In response to successful DELETE requests
400 Bad client request, see message key for details
405 Method not supported on this resource, see Allow HTTP header
409 Precondition not met, e.g., trying to train a tagger without vocabulary
500 Nonspecific server error, see stacktrace key for debugging purposes

Resource: Service

URL pattern: /

GET: Get list of taggers

Returns a list of the taggers available on the server, in JSON format, as well as some general configuration information about the service.

Example request

curl http://localhost:8080/

Example response

{
  "title": "Maui Server",
  "data_dir": "/usr/local/MauiServer/data",
  "default_lang": "en",
  "version": "1.1.0",
  "taggers": [
    {
      "id": "demo",
      "href": "/demo",
      "title": "Demo Tagger"
    }
  ]
}

POST: Create new tagger

Creates a new tagger. The tagger's ID must be enclosed with the POST message as form-encoded key-value pairs. Additional configuration parameters for the tagger may be enclosed as well. See the Tagger Configuration resource for documentation on the configuration parameters. Any string not containing forward or back slashes is a valid tagger ID.

The response is the same as for GET.

Example request

curl -d id=demo http://localhost:8080/

Resource: Tagger

URL pattern: /{tagger-id}

GET: Get tagger status

Returns general information about the status of the tagger in JSON format, such as:

  • Is it trained? On how many documents?
  • How many concepts in the vocabulary? How deep is the hierarchy?
  • Links to all sub-resources (config, vocab, log, etc.)

Example request

curl http://localhost:8080/demo

Example response

{
  "title": "Demo Tagger",
  "id": "demo",
  "is_trained": true,
  "has_vocabulary": true,
  "vocab_stats": {
    "num_concepts": 150621,
    "num_altlabels": 126736,
    "num_concepts_with_relationships": 6696
  },
  "links": {
    "home": "/",
    "tagger": "/demo",
    "config": "/demo/config",
    "vocab": "/demo/vocab",
    "train": "/demo/train",
    "suggest": "/demo/suggest"
  }
}

DELETE: Delete tagger

This removes the tagger and all its sub-resources from the server. On success, the response is 204 No Content and an empty response body.

Example request

curl -X DELETE http://localhost:8080/demo

Resource: Tagger Configuration

URL pattern: /{tagger-id}/config

GET: Show configuration

Returns the tagger's configuration in JSON format.

Key Format Description
title String Human-readable title for this tagger
description String Human-readable description for this tagger
lang One of en, fr, de, es Language for this tagger, or null for the server default
stemmer_class Qualified Java class name Custom stemmer impementation; overrides lang
stopword_class Qualified Java class name Custom stopword implementation; overrides lang
cross_validation_passes Integer >= 2 Number of cross-validation passes for xvalidate
max_topics_per_document Integer >= 1 Maximum number of suggestions per document
probability_threshold Double 0..1 Minimum probability for suggested tags

Example request

curl http://localhost:8080/demo/config

Example response

{
  "title": "Demo Tagger",
  "description": null,
  "lang": "en",
  "stemmer_class": null,
  "stopwords_class": null,
  "cross_validation_passes": 10,
  "max_topics_per_document": 10,
  "probability_threshold": 0.05
}

PUT: Replace configuration

Updates all configuration settings based on the enclosed JSON document. See GET for supported configuration settings. The response format is the same as for GET.

Example request

curl -X PUT --data-binary @- http://localhost:8080/demo/config
{
  "title": "Demo Tagger",
  "description": null,
  "lang": "en",
  "stemmer_class": null,
  "stopwords_class": null,
  "cross_validation_passes": 10
}

POST: Update configuration settings

Updates individual configuration settings based on the enclosed JSON documents. Any settings that are not present in the JSON document will be left as is. Alternatively, the settings to be updated can be provided in form-encoded format. See GET for supported configuration settings. The response format is the same as for GET.

Example request

curl -d 'lang=en' http://localhost:8080/demo/config

Resource: Tagger Vocabulary

URL pattern: /{tagger-id}/vocab

GET: Show vocabulary

Returns the vocabulary used by this tagger in SKOS format, using Turtle syntax.

Example request

curl http://localhost:8080/demo/vocab

PUT: Replace vocabulary

Replaces the vocabulary with the enclosed SKOS document. If the Content-Type header is application/rdf+xml, text/xml or application/xml, the document is assumed to be in RDF/XML syntax. Otherwise, it is assumed to be in Turtle syntax. The response is the same as for GET.

Example request

curl -X PUT --data-binary @my-taxonomy.ttl http://localhost:8080/demo/vocab

DELETE: Delete vocabulary

Deletes the vocabulary. The response is 204 No Content.

Example request

curl -X DELETE http://localhost:8080/demo/vocab

Resource: Tagger Training

URL pattern: /{tagger-id}/train

Trains the tagger by submitting a collection of already tagged documents as training data. Training runs asynchronously in the background.

GET: Training status

Returns a JSON document indicating training status.

Key Format Description
service_status String ready, running, no vocabulary, error
completed Boolean true if training was successfully completed
documents Integer Number of training documents
skipped Integer Number of documents skipped due to lack of content or tags
start_time xs:dateTime Time when training was started
end_time xs:dateTime Time when training was completed
runtime_millis Integer Runtime of running or completed training, in ms
error_message String Error message service_status is error

Example request

curl http://localhost:8080/demo/train

Example response

{
  "service_status": "ready",
  "completed": true,
  "documents": 1000,
  "skipped": 0,
  "start_time": "2016-08-02T16:57:28.355+01:00",
  "runtime_millis": 1135,
  "end_time": "2016-08-02T16:57:29.490+01:00",
}

The keys is_trained and training_status are deprecated, use completed and service_status instead.

POST: Train tagger with training data

Enclosed with the POST request there must be a collection of documents to be used as training data, in JSONL format (one JSON object per line). The JSON object on each line should have the following fields:

Key Format Description
id String Optional document ID
content String Text content of the document
topics Array of Strings Tags of the document; must match vocabulary prefLabels

Example request

curl --data-binary @training-corpus.jsonl http://localhost:8080/test/train

DELETE: Reset model

Removes the Maui model for this tagger, freeing up memory. If a training job is in progress, it will be cancelled. The tagger itself will remain on the server, but must be re-trained before it can be used again for suggestions.

Resource: Tagger Suggestions

URL pattern: /{tagger-id}/suggest

GET: Service description

A simple JSON document stating whether the service is operational (that is, a SKOS vocabulary is present and the tagger has been trained).

Example request

curl http://localhost:8080/demo/suggest

Example response

{
  "title": "Tag Suggestion Service for Tagger: demo",
  "usage": "GET or POST with parameter 'text' to get tag suggestions",
  "is_ready": true
}

GET or POST with text: Perform tag recommendation

This is the key function of the entire server! Text content is submitted as the text parameter, either as a GET parameter or as a form-encoded POST parameter. Returned is a list of recommended concepts from the SKOS vocabulary in JSON. For each concept, the preferred label, URI, and probability is included.

Example request

curl -d 'text=The liver is susceptible to disease.' http://localhost:8080/demo/suggest

Example response

{
  "title": "3 recommendations from demo",
  "topics": [
    {
      "id": "http://www.nlm.nih.gov/mesh/2006#D004198",
      "label": "Disease Susceptibility",
      "probability": 0.5105573342012862
    },
    {
      "id": "http://www.nlm.nih.gov/mesh/2006#D008099",
      "label": "Liver",
      "probability": 0.07828825803579727
    },
    {
      "id": "http://www.nlm.nih.gov/mesh/2006#D004194",
      "label": "Disease",
      "probability": 0.0044551539164485905
    }
  ]
}

Resource: Tagger Cross-Validation

URL pattern: /{tagger-id}/xvalidate

This works similar to training, but instead of training and storing a Maui model from training data, it will evaluate the training process, computing precision and recall by cross-validation. The number of cross-validation passes can be set in the configuration resource.

GET: Cross-validation status and results

Returns a JSON document indicating cross-validation status and results. The meaning of most fields is identical to the training resource, with these additions:

Key Format Description
precision Double Precision of tag recommendations
recall Double Recall of tag recommendations

Example request

curl http://localhost:8080/demo/xvalidate

Example response

{
  "completed": true,
  "service_status": "ready",
  "runtime_millis": 2151,
  "start_time": "2016-07-31T09:41:41.822+01:00",
  "end_time": "2016-07-31T09:41:43.973+01:00",
  "documents": 146,
  "skipped": 0,
  "precision": 0.3433
  "recall": 0.2721
}

POST: Run cross-validation with training data

The expected format is the same as for Tagger Training. This overwrites the previous cross-validation result.

Example request

curl --data-binary @training-corpus.jsonl http://localhost:8080/demo/xvalidate

DELETE: Reset cross-validation results

Deletes the previous cross-validation result and resets the cross-validator to its original state. If a cross-validation job is in progress, it will be cancelled.