Relation extraction

Relation extraction is a type of document analysis that labels concepts expressed in the text with their semantic role.

Relation extraction also performs knowledge linking: Knowledge Graph information and open data—Wikidata, DBpedia and GeoNames references—are returned for relation items corresponding to syncons of the expert.ai Knowledge Graph. In the case of actual places, geographic coordinates are also provided.

The API resource carrying out relation extraction has the following endpoint:

/api/analyze

In the reference section of this manual you will find all the information you need to perform relations extraction, specifically:

• The format of the request.
• The format of the output.

Here is an example of performing relation extraction on a short English text:

Python

This example is based on the Python client you can find on GitHub.

The client gets user credentials from two environment variables:

EAI_USERNAME
EAI_PASSWORD

Set those variables with your account credentials before running the sample program below.

The program prints a shallow (no nesting) representation of relations.

from expertai.nlapi.edge.client import ExpertAiClient
client = ExpertAiClient()

text = "Michael Jordan was one of the best basketball players of all time. Scoring was Jordan's stand-out skill, but he still holds a defensive NBA record, with eight steals in a half." 

output = client.relations(text)

# Output relations' data

print("Output relations' data:");

for relation in output.relations:
    print(relation.verb.lemma, ":");
    for related in relation.related:
        print("\t", "(", related.relation, ")", related.lemma);

Java

This example is based on the Java client you can find on GitHub.

The client gets user credentials from two environment variables:

EAI_USERNAME
EAI_PASSWORD

Set those variables with your account credentials before running the sample program below.

The program prints the JSON response.

import ai.expert.nlapi.security.Authentication;
import ai.expert.nlapi.security.Authenticator;
import ai.expert.nlapi.security.BasicAuthenticator;
import ai.expert.nlapi.security.DefaultCredentialsProvider;
import ai.expert.nlapi.v2.API;
import ai.expert.nlapi.v2.edge.Analyzer;
import ai.expert.nlapi.v2.edge.AnalyzerConfig;
import ai.expert.nlapi.v2.message.AnalyzeResponse;

public class Main {

    public static Authentication createAuthentication() throws Exception {
        DefaultCredentialsProvider credentialsProvider = new DefaultCredentialsProvider();
        Authenticator authenticator = new BasicAuthenticator(credentialsProvider);
        return new Authentication(authenticator);
    }

    public static Analyzer createAnalyzer() throws Exception {
        return new Analyzer(AnalyzerConfig.builder()
                .withVersion(API.Versions.V2)
                .withHost(API.DEFAULT_EDGE_HOST)
                .withAuthentication(createAuthentication())
                .build());
    }

    public static void main(String[] args) {
        try {
            String text = "Michael Jordan was one of the best basketball players of all time. Scoring was Jordan's stand-out skill, but he still holds a defensive NBA record, with eight steals in a half.";

            Analyzer analyzer = createAnalyzer();

            AnalyzeResponse relations = analyzer.relations(text);


            // Output JSON representation

            System.out.println("JSON representation:");
            relations.prettyPrint();
        }
        catch(Exception ex) {
            ex.printStackTrace();
        }
    }
}