apoc.nlp.gcp.entities.stream

Procedure APOC Full

Returns a stream of entities for provided text

Signature

apoc.nlp.gcp.entities.stream(source :: ANY?, config = {} :: MAP?) :: (node :: NODE?, value :: MAP?, error :: MAP?)

Input parameters

Name Type Default

source

ANY?

null

config

MAP?

{}

Config parameters

The procedure support the following config parameters:

Table 1. Config parameters
name type default description

key

String

null

API Key for Google Natural Language API

nodeProperty

String

text

The property on the provided node that contains the unstructured text to be analyzed

Output parameters

Name Type

node

NODE?

value

MAP?

error

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-4.1.0.4.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 that has access to the Cloud Natural Language API by going to console.cloud.google.com/apis/credentials. Once we’ve created a key, we can populate and execute the following command to create a parameter that contains these details.

The following defines the apiKey parameter
:param apiKey => ("<api-key-here>")

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

apoc.conf
apoc.static.gcp.apiKey=<api-key-here>
The following retrieves GCP credentials from apoc.conf
RETURN apoc.static.getAll("gcp") AS gcp;
Table 2. Results
gcp

{apiKey: "<api-key-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 extract the entities from the Article node. The text that we want to analyze is stored in the body property of the node, so we’ll need to specify that via the nodeProperty configuration parameter.

The following streams the entities for the Pokemon article:

MATCH (a:Article {uri: "https://neo4j.com/blog/pokegraph-gotta-graph-em-all/"})
CALL apoc.nlp.gcp.entities.stream(a, {
  key: $apiKey,
  nodeProperty: "body"
})
YIELD value
UNWIND value.entities AS entity
RETURN entity;
Table 3. Results
entity

{name: "card games", salience: 0.17967656, metadata: {}, type: "CONSUMER_GOOD", mentions: [{type: "COMMON", text: {content: "card games", beginOffset: -1}}]}

{name: "role playing games", salience: 0.16441391, metadata: {}, type: "OTHER", mentions: [{type: "COMMON", text: {content: "role playing games", beginOffset: -1}}]}

{name: "Switch", salience: 0.143287, metadata: {}, type: "OTHER", mentions: [{type: "COMMON", text: {content: "Switch", beginOffset: -1}}]}

{name: "friends", salience: 0.13336793, metadata: {}, type: "PERSON", mentions: [{type: "COMMON", text: {content: "friends", beginOffset: -1}}]}

{name: "Nintendo", salience: 0.12601112, metadata: {mid: "/g/1ymzszlpz"}, type: "ORGANIZATION", mentions: [{type: "PROPER", text: {content: "Nintendo", beginOffset: -1}}]}

{name: "board games", salience: 0.08861496, metadata: {}, type: "CONSUMER_GOOD", mentions: [{type: "COMMON", text: {content: "board games", beginOffset: -1}}]}

{name: "tournaments", salience: 0.0603245, metadata: {}, type: "EVENT", mentions: [{type: "COMMON", text: {content: "tournaments", beginOffset: -1}}]}

{name: "offices", salience: 0.034420907, metadata: {}, type: "LOCATION", mentions: [{type: "COMMON", text: {content: "offices", beginOffset: -1}}]}

{name: "Mario Kart 8", salience: 0.029095741, metadata: {wikipedia_url: "https://en.wikipedia.org/wiki/Mario_Kart_8", mid: "/m/0119mf7q"}, type: "PERSON", mentions: [{type: "PROPER", text: {content: "Mario Kart 8", beginOffset: -1}}]}

{name: "European", salience: 0.020393685, metadata: {mid: "/m/02j9z", wikipedia_url: "https://en.wikipedia.org/wiki/Europe"}, type: "LOCATION", mentions: [{type: "PROPER", text: {content: "European", beginOffset: -1}}]}

{name: "Neo4j", salience: 0.020393685, metadata: {mid: "/m/0b76t3s", wikipedia_url: "https://en.wikipedia.org/wiki/Neo4j"}, type: "ORGANIZATION", mentions: [{type: "PROPER", text: {content: "Neo4j", beginOffset: -1}}]}

{name: "8", salience: 0, metadata: {value: "8"}, type: "NUMBER", mentions: [{type: "TYPE_UNKNOWN", text: {content: "8", beginOffset: -1}}]}

We get back 12 different entities. We could then apply a Cypher statement that creates one node per entity and an ENTITY relationship from each of those nodes back to the Article node.

The following streams the entities for the Pokemon article and then creates nodes for each entity:

MATCH (a:Article {uri: "https://neo4j.com/blog/pokegraph-gotta-graph-em-all/"})
CALL apoc.nlp.gcp.entities.stream(a, {
  key: $apiKey,
  nodeProperty: "body"
})
YIELD value
UNWIND value.entities AS entity
MERGE (e:Entity {name: entity.name})
SET e.type = entity.type
MERGE (a)-[:ENTITY]->(e);

If we want to automatically create an entity graph, see apoc.nlp.gcp.entities.graph.