REST Service

El servicio REST está compuesto principalmente de un recurso dataset con distintas operaciones

Endpoints

Aquí se detallarán todos los endpoints del servicio. El valor de la prioridad que se muestra indica la importancia que se le va a dar a la implementación de ese servicio. Cuanto menor sea, más importancia se le dará.

Datasets management

The /dataset collection contains several methods to create, add triples to the dataset, train and generate search indexes.

It also contains these main params

{"entities", "relations", "triples", "status", "algorithm"}

The algorithm parameter contains all the information the dataset are trained with. See /algorithm collection to get more information about this.

Dataset will be changing its status when actions such training or indexing are performed. The status can only grow up. When a changing status is taking place, the dataset cannot be edited. In this situations, the status will be a negative integer.

status: untrained -> trained -> indexed

GET /datasets/(int: dataset_id)/

Get all the information about a dataset, given a dataset_id

Sample request and response

GET /datasets/1/

{
        "dataset": {
                "relations": 655,
                "triples": 3307248,
                "algorithm": {
                        "id": 2,
                        "embedding_size": 100,
                        "max_epochs": null,
                        "margin": 2
                },
                "entities": 651759,
                "status": 2,
                "name": null,
                "id": 4
        }
}

Parameters:

• dataset_id (int) – Unique dataset_id

Status Codes:

• 200 OK – Returns all information about a dataset.
• 404 Not Found – The dataset can’t be found.

POST /datasets/(int: dataset_id)/train?algorithm=(int: id_algorithm)

Train a dataset with a given algorithm id. The training process can be quite large, so this REST method uses a asynchronous model to perform each request.

The response of this method will only be a 202 ACCEPTED status code, with the Location: header filled with the task path element. See /tasks collection to get more information about how tasks are managed on the service.

The dataset must be in a ‘untrained’ (0) state to get this operation done. Also, no operation such as add_triples must be being processed. Otherwise, a 409 CONFLICT status code will be obtained.

Parameters:

• dataset_id (int) – Unique dataset_id

Query Parameters:  

• id_algorithm (int) – The wanted algorithm to train the dataset

Status Codes:

• 202 Accepted – The requests has been accepted to the system and a task has been created. See Location header to get more information.
• 404 Not Found – The dataset or the algorithm can’t be found.
• 409 Conflict – The dataset cannot be trained due to its status.

GET /datasets/

Gets all datasets available on the system.

Status Codes:

• 200 OK – All the datasets are shown correctly

POST /datasets?dataset_type=(int: dataset_type)

Creates a new and empty dataset. To fill in you must use other requests.

You also must provide dataset_type query param. This method will create a WikidataDataset (id: 1) by default, but you also can create different datasets providing a different dataset_type.

Inside the body of the request you can provide a name and/or a description for the dataset. The name must be unique. For example:

Sample request

POST /datasets

{"name": "films", "description": "A dataset with favourite films"}

Sample response

The location: header of the response will contain the relative URI for the created dataset. Additionally, the body of the response will contain a dataset object with only id argument filled in:

location: /datasets/32

{
    "dataset": {
        "id": 32
    }
}

Query Parameters:  

• dataset_type (int) – The dataset type to be created. 0 is for a simple Dataset and 1 is for WikidataDataset (default).

Status Codes:

• 201 Created – A new dataset has been created successfuly. See Location: header to get the id and the new resource path.
• 409 Conflict – The given name already exists on the server.

PUT /datasets/(int: dataset_id)

Edits the description from a existing dataset.

Sample request

PUT /datasets

{"description": "A dataset with most awarded films"}

Parameters:

• dataset_id (int) – Unique dataset_id

Status Codes:

• 200 OK – The dataset has been updated successfully. The updated dataset will be returned in the response.
• 404 Not Found – The provided dataset_id does not exist.

POST /datasets/(int: dataset_id)/triples

Adds a triple or a list of triples to the dataset. You must provide a JSON object on the request body, as shown below on the example. The name of the JSON object must be triples and must contain a list of all entities to be introduced inside the dataset. These entities must contain {"subject", "predicate", "object"} params. This notation is similar to other known as head, label and tail.

Only triples can be added on a untrained (0) dataset.

Ejemplo

POST /datasets/6/triples

{"triples": [
        {
            "subject": {"value": "Q1492"},
            "predicate": {"value": "P17"},
            "object": {"value": "Q29"}
        },
        {
            "subject": {"value": "Q2807"},
            "predicate": {"value": "P17"},
            "object": {"value": "Q29"}
        }
    ]
}

Parameters:

• dataset_id (int) – Unique dataset_id

Status Codes:

• 200 OK – The request has been successful
• 404 Not Found – The dataset or the algorithm can’t be found.
• 409 Conflict – The dataset cannot be trained due to its status.

POST /datasets/(int: dataset_id)/generate_triples

Adds triples to dataset doing a sequence of SPARQL queries by levels, starting with a seed vector. This operation is supported only by certain types of datasets (the default one, type=1)

The request will use asyncronous operations. This means that the request will not be satisfied on the same HTTP connection. Instead, the service will return a /task resource that will be queried with the progress of the task.

The graph_pattern argument must be the where part of a SPARQL query. It must contain three variables named as ?subject, ?predicate and ?object. The service will try to make a query with these names.

You also must provide the levels to make a deep lookup of the entities retrieved from previous queries.

The optional param batch_size is used on the first lookup for SPARQL query. For big queries you must tweak this parameter to avoid server errors as well as to increase performance. It is the LIMIT statement when doing this queries.

Sample request

{
    "generate_triples":
        {
            "graph_pattern": "SPARQL Query",
            "levels": 2,
            "batch_size": 30000
        }
}

Sample response

The location: header of the response will contain the relative URI for the created task resouce. Additionally, it is possible to get the task id from the response in json format.

location: /tasks/32

{
    "message": "Task 32 created successfuly",
    "status": 202,
    "task": {
        "id": 32
    }
}

Parameters:

• dataset_id (int) – Unique identifier of dataset

Status Codes:

• 404 Not Found – The provided dataset_id does not exist.
• 409 Conflict – The dataset_id does not allow this operation
• 202 Accepted – A new task has been created. See /tasks resource to get more information.

POST /datasets/(int: dataset_id)/embeddings

Retrieve from the trained dataset the embeddings from a list of entities.

If on the request list the user requests for a entity that does not exist, the response won’t contain that element. The 404 error is limited to the dataset, not the entities inside the dataset.

The dataset must be in trained status (>= 1), because a model must exist to extract triples from. If not, a 409 CONFLICT will be returned.

This could be useful if it is used with /similar_entities endpoint, to find similar entities given a different embedding vector.

Sample request

POST /datasets/6/embeddings

{"entities": [
    "http://www.wikidata.org/entity/Q1492",
    "http://www.wikidata.org/entity/Q2807",
    "http://www.wikidata.org/entity/Q1" ]
}

Sample response

{ "embeddings": [
    [
        "Q1",
        [0.321, -0.178, 0.195, 0.816]
    ],
    [
        "Q2807",
        [-0.192, 0.172, -0.124, 0.138]
    ],
    [
        "Q1492",
        [0.238, -0.941, 0.116, -0.518]
    ]
  ]
}

Note: The upper vectors are only shown as illustrative, they are not real values

Parameters:

• dataset_id (int) – Unique id of the dataset

Status Codes:

• 200 OK – Operation was successful
• 404 Not Found – The dataset ID does not exist
• 409 Conflict – The dataset is not on a correct status

POST /datasets/(int: dataset_id)/generate_index?n_trees=(int: n_trees)

Generates an Spotify Annoy index to use dataset services. The execution of this action is needed to use triples-prediction_ services.

See more info on Server module.

Sample request

POST /datasets/1/generate_index

Sample response

{
    "status": 202,
    "message": "Task 4 created successfuly"
}

Parameters:

• dataset_id (int) – Unique id of the dataset
• n_trees (int) – Number of trees to generate with Annoy

Status Codes:

• 202 Accepted – The request has been accepted in the system and a task has been created. See Location header to get more information.
• 404 Not Found – The dataset can’t be found.
• 409 Conflict – The dataset cannot be trained due to it’s status.

POST /datasets/(int: dataset_id)/generate_autocomplete_index

Creates a task to build an autocomplete index

The task will perform a request to SPARQL endpoint for each entity. This will extract the labels, description and altLabels and store it on an Elasticsearch database.

It is also possible give the languages desired to build the autocomplete index, allowing not only having english language, but others available on the endpoint. You must specify in the body a param named langs with a list with all language codes in ISO 639-1 format.

Sample request

POST /datasets/6/generate_autocomplete_index

{
    "langs" :  [
        "en", "es"
    ]
}

Sample response

{
    "status": 202,
    "message": "Task 73 created successfuly"
}

Parameters:

• dataset_id (int) – Unique id of the dataset

Status Codes:

• 202 Accepted – The request has been accepted in the system and a task has been created. See Location header to get more information.
• 404 Not Found – The dataset can’t be found.
• 409 Conflict – The dataset cannot be trained due to it’s status.

Algorithms

The algorithm collection is used mainly to create and see the different algorithms created on the server.

The hyperparameters that are allowed currently to tweak are: - embedding_size: The size of the embeddigs the trainer will use - margin: The margin used on the trainer - max_epochs: The maximum number of iterations of the algorithm

GET /algorithms/

Gets a list with all the algorithms created on the service.

GET /algorithms/(int: algorithm_id)

Gets only one algorithm

Parameters:

• algorithm_id (int) – The algorithm unique identifier

POST /algorithms/

Create one algorithm on the service. On success, this method will return a 201 CREATED status code and the header parameter Location: filled with the relative path to the created resource.

The body of the request must contain all parameters for the new algorithm. See the example below:

Sample request

POST /algorithms

{
        "algorithm": {
                "embedding_size": 50,
                "margin": 2,
                "max_epochs": 80
        }
}

Sample successfull response The response when creating a new algorithm gives the location header filled with the URI of the new resource. It also returns the HTTP 202 status code, and the body has information about the request in json format.

location: /algorithm/2

{
    "status": 202,
    "algorithm": {
        "id": 2
    },
    "message": "Algorithm 2 created successfuly"
}

Status Codes:

• 201 Created – The request has been processed successfuly and a new resource has been created. See Location: header to get the new path.

Tasks

The task collection stores all the information that async request need. This collection are made mainly to get the actual state of tasks, but no to edit or delete tasks (Not implemented).

GET /tasks/(int: task_id)?get_debug_info=(boolean: get_debug_info)&?no_redirect=(boolean: no_redirect)

Shows the progress of a task with a task_id. The finished tasks can be deleted from the system without previous advise.

Some tasks can inform to the user about its progress. It is done through the progress param, which has current and total relative arguments, and current_steps and total_steps absolute arguments. When a task involves some steps and the number of small tasks to be done in next step cannot be discovered, the current and total will only indicate progress in current step, and will not include previous step, expected to be already done, or next step which is expected to be empty.

The resource has two optional parameters: get_debug_info and no_redirect. The first one, get_debug_info set to true on the query params will return additional information from the task. The other param, no_redirect will avoid send a 303 status to the client to redirect to the created resource. Instead it will send a simple 200 status code, but with the location header filled.

Parameters:

• task_id (int) – Unique task_id from the task.

Status Codes:

• 200 OK – Shows the status from the current task.
• 303 See Other – The task has finished. See Location header to find the resource it has created/modified. With no_redirect query param set to true, the location header will be filled, but a 200 code will be returned instead.
• 404 Not Found – The given task_id does not exist.

Triples prediction

GET /datasets/(int: dataset_id)/similar_entities/(string: entity)?limit=(int: limit)?search_k=(int: search_k)

POST /datasets/(int: dataset_id)/similar_entities?limit=(int: limit)?search_k=(int: search_k)

Get the limit entities most similar to a entity inside a dataset_id. The given number in limit excludes the entity given itself.

The POST method allows any representation of the wanted resource. See the example below. You can provide an entity as an URI or other similar representation, even an embedding. The type param inside entity JSON object must be “uri” for a URI or similar representation and “embedding” for an embedding.

The search_k param is used to tweak the results of the search. When this value is greater, the precission of the results are also greater, but the time it takes to find the response is also bigger.

Sample request

GET /datasets/7/similar_entities?limit=1&search_k=10000

{ "entity":
      {"value": "http://www.wikidata.org/entity/Q1492", "type": "uri"}
}

Sample response

{    "similar_entities":
    {    "response":
        [
            {"distance": 0, "entity": "http://www.wikidata.org/entity/Q1492"},
            {"distance": 0.8224636912345886, "entity": "http://www.wikidata.org/entity/Q15090"}
        ],
        "entity": "http://www.wikidata.org/entity/Q1492",
        "limit": 2
    },
    "dataset": {
        "entities": 664444,
        "relations": 647,
        "id": 1,
        "status": 2,
        "triples": 3261785,
        "algorithm": 100
    }
}

Parameters:

• dataset_id (int) – Unique id of the dataset

Query Parameters:  

• limit (int) – Limit of similar entities requested. By default this is set to 10.
• search_k (int) – Max number of trees where the lookup is performed. This increase the result quality, but reduces the performance of the request. By default is set to -1

Status Codes:

• 200 OK – The request has been performed successfully
• 404 Not Found – The dataset or the entity can’t be found

POST /datasets/(int: dataset_id)/distance

Returns the distance between two elements. The lower the number is, most probable to be both the same triple. The minimum distance is 0.

Request Example

POST /datasets/0/similar_entities

{
    "distance": [
         "http://www.wikidata.org/entity/Q1492",
         "http://www.wikidata.org/entity/Q5682"
    ]
}

HTTP Response

{
    "distance": 1.460597038269043
}

Parameters:

• dataset_id (int) – Unique id of the dataset

Status Codes:

• 200 OK – The request has been performed successfully
• 404 Not Found – The dataset or the entity can’t be found

POST /datasets/(int: dataset_id)/suggest_name

Gives a list of autocomplete suggestions. For each entity, this will show labels on every language available, descriptions and altLabels.

If any suggestion is available, this will return an empty list.

Request Example

POST /datasets/7/suggest_name

{
    "input": "human"
}

HTTP Response

[
    {
        "text": "humano",
        "entity": {
            "alt_label": {
                "es": [
                    "humano",
                    "Homo sapiens sapiens",
                    "persona",
                ],
                "en": [
                    "people",
                    "person",
                    "human being"
                ]
            },
            "label": {
                "en": "human",
                "es": "ser humano",
            },
            "entity": "Q5",
            "description": {
                "en": "common name of Homo sapiens (Q15978631), unique extant species of the genus Homo",
                "es": "especie animal perteneciente a la familia Hominidae, única superviviente del género Homo",
            }
        }
    }
]

Parameters:

• dataset_id (int) – Unique id of the dataset

Status Codes:

• 200 OK – The request has been performed successfully
• 404 Not Found – The dataset or the entity can’t be found