apoc.nlp.azure.entities.graph

Procedure APOC Full

Creates a (virtual) entity graph for provided text

Signature

apoc.nlp.azure.entities.graph(source :: ANY?, config = {} :: MAP?) :: (graph :: MAP?)

Input parameters

Name	Type	Default
source	ANY?	null
config	MAP?	{}

Name

Type

Default

source

ANY?

null

config

MAP?

{}

Output parameters

Name	Type
graph	MAP?

Name

Type

graph

MAP?

Install Dependencies

The NLP procedures have dependencies on Kotlin and client libraries that are not included in the APOC Library.

These dependencies are included in apoc-nlp-dependencies-4.4.0.33.jar, which can be downloaded from the releases page. Once that file is downloaded, it should be placed in the plugins directory and the Neo4j Server restarted.

Setting up API Key

We can generate an API key and URL by following the instructions in the Quickstart: Use the Text Analytics client library article. Once we’ve done that, we should be able to see a page listing our credentials, similar to the screenshot below:

Figure 1. Azure Text Analytics credentials

In this case our API URL is https://neo4j-nlp-text-analytics.cognitiveservices.azure.com/, and we can use either of the hidden keys.

Let’s populate and execute the following commands to create parameters that contains these details.

The following define the apiKey and apiSecret parameters

:param apiKey => ("<api-key-here>");
:param apiUrl => ("<api-url-here>");

Alternatively we can add these credentials to apoc.conf and retrieve them using the static value storage functions. See Static Value Storage

apoc.conf

apoc.static.azure.apiKey=<api-key-here>
apoc.static.azure.apiUrl=<api-url-here>

The following retrieves AWS credentials from apoc.conf

RETURN apoc.static.getAll("azure") AS azure;

Table 1. Results
azure
{apiKey: "<api-key-here>", apiUrl: "<api-url-here>"}

Usage Examples

The examples in this section are based on the following sample graph:

CREATE (:Article {
  uri: "https://neo4j.com/blog/pokegraph-gotta-graph-em-all/",
  body: "These days I’m rarely more than a few feet away from my Nintendo Switch and I play board games, card games and role playing games with friends at least once or twice a week. I’ve even organised lunch-time Mario Kart 8 tournaments between the Neo4j European offices!"
});

CREATE (:Article {
  uri: "https://en.wikipedia.org/wiki/Nintendo_Switch",
  body: "The Nintendo Switch is a video game console developed by Nintendo, released worldwide in most regions on March 3, 2017. It is a hybrid console that can be used as a home console and portable device. The Nintendo Switch was unveiled on October 20, 2016. Nintendo offers a Joy-Con Wheel, a small steering wheel-like unit that a Joy-Con can slot into, allowing it to be used for racing games such as Mario Kart 8."
});

We can use this procedure to automatically create the entity graph. As well as having the Entity label, each entity node will have another label based on the value of the type property. By default a virtual graph is returned.

The following returns a virtual graph of entities for the Pokemon and Nintendo Switch articles:

MATCH (a:Article)
WITH collect(a) AS articles
CALL apoc.nlp.azure.entities.graph(articles, {
  key: $apiKey,
  url: $apiUrl,
  nodeProperty: "body",
  writeRelationshipType: "ENTITY"
})
YIELD graph AS g
RETURN g

We can see a Neo4j Browser visualization of the virtual graph in Pokemon and Nintendo Switch entities graph.

Figure 2. Pokemon and Nintendo Switch entities graph

On this visualization we can also see the score for each entity node. This score represents the level of confidence that the API has in its detection of the entity. We can specify a minimum cut off value for the score using the scoreCutoff property.

The following returns a virtual graph of entities with a score >= 0.7 for the Pokemon and Nintendo Switch articles:

MATCH (a:Article)
WITH collect(a) AS articles
CALL apoc.nlp.azure.entities.graph(articles, {
  key: $apiKey,
  url: $apiUrl,
  nodeProperty: "body",
  scoreCutoff: 0.7,
  writeRelationshipType: "ENTITY"
})
YIELD graph AS g
RETURN g

We can see a Neo4j Browser visualization of the virtual graph in Pokemon and Nintendo Switch entities graph with confidence >= 0.7.

apoc.nlp.azure.entities multiple.graph cutoff

Figure 3. Pokemon and Nintendo Switch entities graph with confidence >= 0.7

If we’re happy with this graph and would like to persist it in Neo4j, we can do this by specifying the write: true configuration.

The following creates a HAS_ENTITY relationship from the article to each entity:

MATCH (a:Article)
WITH collect(a) AS articles
CALL apoc.nlp.azure.entities.graph(articles, {
  key: $apiKey,
  url: $apiUrl,
  nodeProperty: "body",
  scoreCutoff: 0.7,
  writeRelationshipType: "HAS_ENTITY",
  writeRelationshipProperty: "azureEntityScore",
  write: true
})
YIELD graph AS g
RETURN g;

We can then write a query to return the entities that have been created.

The following returns articles and their entities:

MATCH (article:Article)
RETURN article.uri AS article,
       [(article)-[r:HAS_ENTITY]->(e:Entity) | {text: e.text, score: r.azureEntityScore}] AS entities;

Table 2. Results
article	entities
"https://neo4j.com/blog/pokegraph-gotta-graph-em-all/"	[{score: 0.72, text: "Mario Kart"}, {score: 0.7802000593632747, text: "Mario Kart 8"}, {score: 0.8, text: "8"}, {score: 0.8, text: "a week"}, {score: 0.94, text: "Nintendo Switch"}, {score: 0.8150388253887939, text: "Neo4j"}]
"https://en.wikipedia.org/wiki/Nintendo_Switch"	[{score: 0.9023679924293266, text: "Joy-Con"}, {score: 0.98, text: "Nintendo"}, {score: 0.8, text: "March 3, 2017"}, {score: 0.9355623498560008, text: "Nintendo Switch"}, {score: 0.92, text: "Mario Kart"}, {score: 0.8, text: "8"}, {score: 0.8863202650046607, text: "Mario Kart 8"}, {score: 0.8, text: "October 20, 2016"}]

If we want to stream back entities and apply custom logic to the results, see apoc.nlp.azure.entities.stream.