文件 AWS 開發套件範例 GitHub 儲存庫中有更多可用的 [AWS SDK 範例](https://github.com/awsdocs/aws-doc-sdk-examples)。

本文為英文版的機器翻譯版本，如內容有任何歧義或不一致之處，概以英文版為準。

# AWS Entity Resolution 使用適用於 JavaScript 的 SDK (v2) 的範例
<a name="javascript_2_entityresolution_code_examples"></a>

下列程式碼範例示範如何使用 適用於 JavaScript 的 AWS SDK (v2) 搭配 來執行動作和實作常見案例 AWS Entity Resolution。

*基本概念*是程式碼範例，這些範例說明如何在服務內執行基本操作。

每個範例均包含完整原始碼的連結，您可在連結中找到如何設定和執行內容中程式碼的相關指示。

**Topics**
+ [基本概念](#basics)

## 基本概念
<a name="basics"></a>

### 了解基本概念
<a name="entityresolution_Scenario_javascript_2_topic"></a>

以下程式碼範例顯示做法：
+ 建立結構描述映射。
+ 建立 AWS Entity Resolution 工作流程。
+ 啟動工作流程的相符任務。
+ 取得相符任務的詳細資訊。
+ 取得結構描述映射。
+ 列出所有結構描述映射。
+ 標記結構描述映射資源。
+ 刪除 AWS Entity Resolution 資產。

**適用於 JavaScript (v2) 的 SDK**  
 GitHub 上提供更多範例。尋找完整範例，並了解如何在 [AWS 程式碼範例儲存庫](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/example_code/entityresolution#code-examples)中設定和執行。
執行示範 AWS Entity Resolution 功能的互動式案例。  

```
import {
  Scenario,
  ScenarioAction,
  ScenarioInput,
  ScenarioOutput,
} from "@aws-doc-sdk-examples/lib/scenario/index.js";
import {
  CloudFormationClient,
  CreateStackCommand,
  DeleteStackCommand,
  DescribeStacksCommand,
  waitUntilStackExists,
  waitUntilStackCreateComplete,
} from "@aws-sdk/client-cloudformation";
import {
  EntityResolutionClient,
  CreateSchemaMappingCommand,
  CreateMatchingWorkflowCommand,
  GetMatchingJobCommand,
  StartMatchingJobCommand,
  GetSchemaMappingCommand,
  ListSchemaMappingsCommand,
  TagResourceCommand,
  DeleteMatchingWorkflowCommand,
  DeleteSchemaMappingCommand,
  ConflictException,
  ValidationException,
} from "@aws-sdk/client-entityresolution";
import {
  DeleteObjectsCommand,
  DeleteBucketCommand,
  PutObjectCommand,
  S3Client,
  ListObjectsCommand,
} from "@aws-sdk/client-s3";
import { wait } from "@aws-doc-sdk-examples/lib/utils/util-timers.js";

import { readFile } from "node:fs/promises";
import { parseArgs } from "node:util";
import { readFileSync } from "node:fs";
import { fileURLToPath } from "node:url";
import { dirname } from "node:path";

const __filename = fileURLToPath(import.meta.url);
const __dirname = dirname(__filename);
const stackName = `${data.inputs.entityResolutionStack}`;

/*The inputs for this example can be edited in the ../input.json.*/
import data from "../inputs.json" with { type: "json" };
const skipWhenErrors = (state) => state.errors.length > 0;
/**
 * Used repeatedly to have the user press enter.
 * @type {ScenarioInput}
 */
/* v8 ignore next 3 */
const pressEnter = new ScenarioInput("continue", "Press Enter to continue", {
  type: "input",
  verbose: "false",
  skipWhen: skipWhenErrors,
});

const region = "eu-west-1";

const entityResolutionClient = new EntityResolutionClient({ region: region });
const cloudFormationClient = new CloudFormationClient({ region: region });
const s3Client = new S3Client({ region: region });

const greet = new ScenarioOutput(
  "greet",
  "AWS Entity Resolution is a fully-managed machine learning service provided by " +
    "Amazon Web Services (AWS) that helps organizations extract, link, and " +
    "organize information from multiple data sources. It leverages natural " +
    "language processing and deep learning models to identify and resolve " +
    "entities, such as people, places, organizations, and products, " +
    "across structured and unstructured data.\n" +
    "\n" +
    "With Entity Resolution, customers can build robust data integration " +
    "pipelines to combine and reconcile data from multiple systems, databases, " +
    "and documents. The service can handle ambiguous, incomplete, or conflicting " +
    "information, and provide a unified view of entities and their relationships. " +
    "This can be particularly valuable in applications such as customer 360, " +
    "fraud detection, supply chain management, and knowledge management, where " +
    "accurate entity identification is crucial.\n" +
    "\n" +
    "The `EntityResolutionAsyncClient` interface in the AWS SDK for Java 2.x " +
    "provides a set of methods to programmatically interact with the AWS Entity " +
    "Resolution service. This allows developers to automate the entity extraction, " +
    "linking, and deduplication process as part of their data processing workflows. " +
    "With Entity Resolution, organizations can unlock the value of their data, " +
    "improve decision-making, and enhance customer experiences by having a reliable, " +
    "comprehensive view of their key entities.",

  { header: true },
);
const displayBuildCloudFormationStack = new ScenarioOutput(
  "displayBuildCloudFormationStack",
  "To prepare the AWS resources needed for this scenario application, the next step uploads " +
    "a CloudFormation template whose resulting stack creates the following resources:\n" +
    "- An AWS Glue Data Catalog table \n" +
    "- An AWS IAM role \n" +
    "- An AWS S3 bucket \n" +
    "- An AWS Entity Resolution Schema \n" +
    "It can take a couple minutes for the Stack to finish creating the resources.",
);

const sdkBuildCloudFormationStack = new ScenarioAction(
  "sdkBuildCloudFormationStack",
  async (/** @type {State} */ state) => {
    try {
      const data = readFileSync(
        `${__dirname}/../../../../resources/cfn/entity-resolution-basics/entity-resolution-basics-template.yml`,
        "utf8",
      );
      await cloudFormationClient.send(
        new CreateStackCommand({
          StackName: stackName,
          TemplateBody: data,
          Capabilities: ["CAPABILITY_IAM"],
        }),
      );
      await waitUntilStackExists(
        { client: cloudFormationClient },
        { StackName: stackName },
      );
      await waitUntilStackCreateComplete(
        { client: cloudFormationClient },
        { StackName: stackName },
      );
      const stack = await cloudFormationClient.send(
        new DescribeStacksCommand({
          StackName: stackName,
        }),
      );

      state.entityResolutionRole = stack.Stacks[0].Outputs[1];
      state.jsonGlueTable = stack.Stacks[0].Outputs[2];
      state.CSVGlueTable = stack.Stacks[0].Outputs[3];
      state.glueDataBucket = stack.Stacks[0].Outputs[0];
      state.stackName = stack.StackName;
      console.log(state.glueDataBucket);
      console.log(
        `The  ARN of the EntityResolution Role is ${state.entityResolutionRole.OutputValue}`,
      );
      console.log(
        `The ARN of the Json Glue Table is ${state.jsonGlueTable.OutputValue}`,
      );
      console.log(
        `The ARN of the CSV Glue Table is ${state.CSVGlueTable.OutputValue}`,
      );
      console.log(
        `The name of the Glue Data Bucket is ${state.glueDataBucket.OutputValue}\n`,
      );
    } catch (caught) {
      console.error(caught.message);
      throw caught;
    }
    try {
      console.log(
        `Uploading the following JSON in ../data.json to the ${state.glueDataBucket.OutputValue} S3 bucket...`,
      );
      const bucketName = state.glueDataBucket.OutputValue;

      const putObjectParams = {
        Bucket: bucketName,
        Key: "jsonData/data.json",
        Body: await readFileSync(
          `${__dirname}/../../../../javascriptv3/example_code/entityresolution/data.json`,
        ),
      };
      const command = new PutObjectCommand(putObjectParams);
      const response = await s3Client.send(command);
      console.log(
        `../data.json file data uploaded to the ${state.glueDataBucket.OutputValue} S3 bucket.\n`,
      );
    } catch (caught) {
      console.error(caught.message);
      throw caught;
    }
    try {
      console.log(
        `Uploading the CSV data in ../data.csv to the ${state.glueDataBucket.OutputValue} S3 bucket...`,
      );

      const bucketName = state.glueDataBucket.OutputValue;
      const putObjectParams = {
        Bucket: bucketName,
        Key: "csvData/data.csv",
        Body: await readFileSync(
          `${__dirname}/../../../../javascriptv3/example_code/entityresolution/data.csv`,
        ),
      };
      const command = new PutObjectCommand(putObjectParams);
      const response = await s3Client.send(command);
      console.log(
        `../data.csv file data uploaded to the ${state.glueDataBucket.OutputValue} S3 bucket.`,
      );
    } catch (caught) {
      console.error(caught.message);
      throw caught;
    }
  },
);

const displayCreateSchemaMapping = new ScenarioOutput(
  "displayCreateSchemaMapping",
  "1. Create Schema Mapping" +
    "Entity Resolution schema mapping aligns and integrates data from " +
    "multiple sources by identifying and matching corresponding entities " +
    "like customers or products. It unifies schemas, resolves conflicts, " +
    "and uses machine learning to link related entities, enabling a " +
    "consolidated, accurate view for improved data quality and decision-making." +
    "\n" +
    "In this example, the schema mapping lines up with the fields in the JSON and CSV objects. That is, " +
    " it contains these fields: id, name, and email. ",
);

const sdkCreateSchemaMapping = new ScenarioAction(
  "sdkCreateSchemaMapping",
  async (/** @type {State} */ state) => {
    const createSchemaMappingParamsJson = {
      schemaName: `${data.inputs.schemaNameJson}`,
      mappedInputFields: [
        {
          fieldName: "id",
          type: "UNIQUE_ID",
        },
        {
          fieldName: "name",
          type: "NAME",
        },
        {
          fieldName: "email",
          type: "EMAIL_ADDRESS",
        },
      ],
    };
    const createSchemaMappingParamsCSV = {
      schemaName: `${data.inputs.schemaNameCSV}`,
      mappedInputFields: [
        {
          fieldName: "id",
          type: "UNIQUE_ID",
        },
        {
          fieldName: "name",
          type: "NAME",
        },
        {
          fieldName: "email",
          type: "EMAIL_ADDRESS",
        },
        {
          fieldName: "phone",
          type: "PROVIDER_ID",
          subType: "STRING",
        },
      ],
    };
    try {
      const command = new CreateSchemaMappingCommand(
        createSchemaMappingParamsJson,
      );
      const response = await entityResolutionClient.send(command);
      state.schemaNameJson = response.schemaName;
      state.schemaArn = response.schemaArn;
      state.idOutputAttribute = response.mappedInputFields[0].fieldName;
      state.nameOutputAttribute = response.mappedInputFields[1].fieldName;
      state.emailOutputAttribute = response.mappedInputFields[2].fieldName;

      console.log("The JSON schema mapping name is ", state.schemaNameJson);
    } catch (caught) {
      if (caught instanceof ConflictException) {
        console.error(
          `The schema mapping already exists: ${caught.message} \n Exiting program.`,
        );
        return;
      }
    }
    try {
      const command = new CreateSchemaMappingCommand(
        createSchemaMappingParamsCSV,
      );
      const response = await entityResolutionClient.send(command);
      state.schemaNameCSV = response.schemaName;
      state.phoneOutputAttribute = response.mappedInputFields[3].fieldName;
      console.log("The CSV schema mapping name is ", state.schemaNameCSV);
    } catch (caught) {
      if (caught instanceof ConflictException) {
        console.error(
          `An unexpected error occurred while creating the geofence collection: ${caught.message} \n Exiting program.`,
        );
        return;
      }
    }
  },
);
const displayCreateMatchingWorkflow = new ScenarioOutput(
  "displayCreateMatchingWorkflow",
  "2. Create an AWS Entity Resolution Workflow. " +
    "An Entity Resolution matching workflow identifies and links records " +
    "across datasets that represent the same real-world entity, such as " +
    "customers or products. Using techniques like schema mapping, " +
    "data profiling, and machine learning algorithms, " +
    "it evaluates attributes like names or emails to detect duplicates " +
    "or relationships, even with variations or inconsistencies. " +
    "The workflow outputs consolidated, de-duplicated data." +
    "\n" +
    "We will use the machine learning-based matching technique.",
);

const sdkCreateMatchingWorkflow = new ScenarioAction(
  "sdkCreateMatchingWorkflow",
  async (/** @type {State} */ state) => {
    const createMatchingWorkflowParams = {
      roleArn: `${state.entityResolutionRole.OutputValue}`,
      workflowName: `${data.inputs.workflowName}`,
      description: "Created by using the AWS SDK for JavaScript (v3).",
      inputSourceConfig: [
        {
          inputSourceARN: `${state.jsonGlueTable.OutputValue}`,
          schemaName: `${data.inputs.schemaNameJson}`,
          applyNormalization: false,
        },
        {
          inputSourceARN: `${state.CSVGlueTable.OutputValue}`,
          schemaName: `${data.inputs.schemaNameCSV}`,
          applyNormalization: false,
        },
      ],
      outputSourceConfig: [
        {
          outputS3Path: `s3://${state.glueDataBucket.OutputValue}/eroutput`,
          output: [
            {
              name: state.idOutputAttribute,
            },
            {
              name: state.nameOutputAttribute,
            },
            {
              name: state.emailOutputAttribute,
            },
            {
              name: state.phoneOutputAttribute,
            },
          ],
          applyNormalization: false,
        },
      ],
      resolutionTechniques: { resolutionType: "ML_MATCHING" },
    };
    try {
      const command = new CreateMatchingWorkflowCommand(
        createMatchingWorkflowParams,
      );
      const response = await entityResolutionClient.send(command);
      state.workflowArn = response.workflowArn;
      console.log(
        `Workflow created successfully.\n The workflow ARN is: ${response.workflowArn}`,
      );
    } catch (caught) {
      if (caught instanceof ConflictException) {
        console.error(
          `The matching workflow already exists: ${caught.message} \n Exiting program.`,
        );
        return;
      }
      if (caught instanceof ValidationException) {
        console.error(
          `There was a validation exception: ${caught.message} \n Exiting program.`,
        );
        return;
      }
    }
  },
);
const displayMatchingJobOfWorkflow = new ScenarioOutput(
  "displayMatchingJobOfWorkflow",
  "3. Start the matching job of the workflow",
);

const sdkMatchingJobOfWorkflow = new ScenarioAction(
  "sdk",
  async (/** @type {State} */ state) => {
    const matchingJobOfWorkflowParams = {
      workflowName: `${data.inputs.workflowName}`,
    };
    try {
      const command = new StartMatchingJobCommand(matchingJobOfWorkflowParams);
      const response = await entityResolutionClient.send(command);
      state.jobID = response.jobId;
      console.log(`Job ID: ${state.jobID} \n
The matching job was successfully started.`);
    } catch (caught) {
      if (caught instanceof ConflictException) {
        console.error(
          `The matching workflow already exists: ${caught.message} \n Exiting program.`,
        );
        return;
      }
    }
  },
);

const displayGetDetailsforJob = new ScenarioOutput(
  "displayGetDetailsforJob",
  `4. While the matching job is running, let's look at other API methods. First, let's get details for the job `,
);

const sdkGetDetailsforJob = new ScenarioAction(
  "sdkGetDetailsforJob",
  async (/** @type {State} */ state) => {
    const getDetailsforJobParams = {
      workflowName: `${data.inputs.workflowName}`,
      jobId: `${state.jobID}`,
    };
    try {
      const command = new GetMatchingJobCommand(getDetailsforJobParams);
      const response = await entityResolutionClient.send(command);
      state.Status = response.status;
      state.response = response;
      console.log(`Job status: ${state.Status} `);
    } catch (caught) {
      console.error(caught.message);
      throw caught;
    }
  },
);

const displayGetSchemaMappingJson = new ScenarioOutput(
  "displayGetSchemaMappingJson",
  "5. Get the schema mapping for the JSON data.",
);

const sdkGetSchemaMappingJson = new ScenarioAction(
  "sdkGetSchemaMappingJson",
  async (/** @type {State} */ state) => {
    const getSchemaMappingJsonParams = {
      schemaName: `${data.inputs.schemaNameJson}`,
    };
    try {
      const command = new GetSchemaMappingCommand(getSchemaMappingJsonParams);
      const response = await entityResolutionClient.send(command);
      console.log("Schema·mapping·ARN·is:·", response.schemaArn);
      const resultMappings = response.mappedInputFields;
      const noOfResultMappings = resultMappings.length;
      for (let i = 0; i < noOfResultMappings; i++) {
        console.log(
          `Attribute name: ${resultMappings[i].fieldName} `,
          `Attribute type: ${resultMappings[i].type}`,
        );
      }
    } catch (caught) {
      console.error(caught.message);
      throw caught;
    }
  },
);

const displayListSchemaMappings = new ScenarioOutput(
  "displayListSchemaMappings",
  "6. List Schema Mappings.",
);

const sdkListSchemaMappings = new ScenarioAction(
  "sdkListSchemaMappings",
  async (/** @type {State} */ state) => {
    try {
      const command = new ListSchemaMappingsCommand({});
      const response = await entityResolutionClient.send(command);
      const noOfSchemas = response.schemaList.length;
      for (let i = 0; i < noOfSchemas; i++) {
        console.log(
          `Schema Mapping Name: ${response.schemaList[i].schemaName} `,
        );
      }
    } catch (caught) {
      console.error(caught.message);
      throw caught;
    }
  },
);

const displayTagTheJsonSchema = new ScenarioOutput(
  "display",
  "7. Tag the resource. \n" +
    "Tags can help you organize and categorize your Entity Resolution resources. " +
    "You can also use them to scope user permissions by granting a user permission " +
    "to access or change only resources with certain tag values. " +
    "In Entity Resolution, SchemaMapping and MatchingWorkflow can be tagged. For this example, " +
    "the SchemaMapping is tagged.",
);

const sdkTagTheJsonSchema = new ScenarioAction(
  "sdkGetSchemaMappingJson",
  async (/** @type {State} */ state) => {
    const tagResourceCommandParams = {
      resourceArn: state.schemaArn,
      tags: {
        tag1: "tag1Value",
        tag2: "tag2Value",
      },
    };
    try {
      const command = new TagResourceCommand(tagResourceCommandParams);
      const response = await entityResolutionClient.send(command);
      console.log("Successfully tagged the resource.");
    } catch (caught) {
      console.error(caught.message);
      throw caught;
    }
  },
);

const displayGetJobInfo = new ScenarioOutput(
  "displayGetJobInfo",
  "8. View the results of the AWS Entity Resolution Workflow.\n " +
    "Please perform this task manually in the AWS Management Console. ",
);

const displayDeleteResources = new ScenarioOutput(
  "displayDeleteResources",
  "9. Delete the resources \n" +
    "You cannot delete a workflow that is in a running state. So this will take ~30 minutes.\n" +
    "If you don't want to delete the resources, simply exit this application.",
);

const sdkDeleteResources = new ScenarioAction(
  "sdkDeleteResources",
  async (/** @type {State} */ state) => {
    console.log(
      "You selected to delete the resources. This will take about 30 minutes.",
    );
    await wait(1800);
    const bucketName = state.glueDataBucket.OutputValue;
    try {
      const emptyBucket = async ({ bucketName }) => {
        const listObjectsCommand = new ListObjectsCommand({
          Bucket: bucketName,
        });
        const { Contents } = await s3Client.send(listObjectsCommand);
        const keys = Contents.map((c) => c.Key);

        const deleteObjectsCommand = new DeleteObjectsCommand({
          Bucket: bucketName,
          Delete: { Objects: keys.map((key) => ({ Key: key })) },
        });
        await s3Client.send(deleteObjectsCommand);
        console.log(`Bucket ${bucketName} emptied successfully.\n`);
      };
      await emptyBucket({ bucketName });
    } catch (error) {
      console.log("error ", error);
    }
    try {
      const deleteBucket = async ({ bucketName }) => {
        const command = new DeleteBucketCommand({ Bucket: bucketName });
        await s3Client.send(command);
        console.log(`Bucket ${bucketName} deleted successfully.\n`);
      };
      await deleteBucket({ bucketName });
    } catch (error) {
      console.log("error ", error);
    }
    try {
      console.log(
        "Now we will delete the CloudFormation stack, which deletes the resources that were created at the beginning of the scenario.",
      );
      const deleteStackParams = { StackName: `${state.stackName}` };
      const command = new DeleteStackCommand(deleteStackParams);
      const response = await cloudFormationClient.send(command);
      console.log("CloudFormation stack deleted successfully.");
    } catch (error) {
      console.log("error ", error);
    }
    try {
      const deleteWorkflowParams = {
        workflowName: `${data.inputs.workflowName}`,
      };
      const command = new DeleteMatchingWorkflowCommand(deleteWorkflowParams);
      const response = await entityResolutionClient.send(command);
      console.log("Workflow deleted successfully!");
    } catch (caught) {
      if (caught instanceof ConflictException) {
        console.error(
          `Job associated with workflow ${data.inputs.workflowName} is still running, so can't be deleted. 
          Neither can schemas ${data.inputs.schemaNameJson} and ${data.inputs.schemaNameCSV} associated with it. Please confirm this workflow is finished in the AWS Management Console, then delete it manually.`,
        );
        throw caught;
      }
    }
    try {
      const deleteJSONschemaMapping = {
        schemaName: `${data.inputs.schemaNameJson}`,
      };
      const command = new DeleteSchemaMappingCommand(deleteJSONschemaMapping);
      const response = await entityResolutionClient.send(command);
      console.log("Schema mapping deleted successfully. ");
    } catch (caught) {
      if (caught instanceof ConflictException) {
        console.error(
          `The schema ${data.inputs.schemaNameJson} can't be deleted because it is associated with workflow
           ${data.inputs.workflowName}, which is still running. Please confirm this workflow is finished in the AWS Management Console, then delete it manually.`,
        );
        throw caught;
      }
    }
    try {
      const deleteCSVschemaMapping = {
        schemaName: `${data.inputs.schemaNameCSV}`,
      };
      const command = new DeleteSchemaMappingCommand(deleteCSVschemaMapping);
      const response = await entityResolutionClient.send(command);
      console.log("Schema mapping deleted successfully.");
    } catch (caught) {
      if (caught instanceof ConflictException) {
        console.error(
          `The schema ${data.inputs.schemaNameCSV} can't be deleted because it is associated with workflow ${data.inputs.workflowName}, which is still running. Please confirm this workflow is finished in the AWS Management Console, then delete it manually.`,
        );
        throw caught;
      }
    }
  },
  {
    skipWhen: (/** @type {State} */ state) =>
      state.confirmDeleteResources === "",
  },
);

const goodbye = new ScenarioOutput(
  "goodbye",
  "Thank you for checking out the Amazon Location Service Use demo. We hope you " +
    "learned something new, or got some inspiration for your own apps today!" +
    " For more Amazon Location Services examples in different programming languages, have a look at: " +
    "https://docs.aws.amazon.com/code-library/latest/ug/location_code_examples.html",
);

const myScenario = new Scenario("Entity Resolution Basics Scenario", [
  greet,
  pressEnter,
  displayBuildCloudFormationStack,
  sdkBuildCloudFormationStack,
  pressEnter,
  displayCreateSchemaMapping,
  sdkCreateSchemaMapping,
  pressEnter,
  displayCreateMatchingWorkflow,
  sdkCreateMatchingWorkflow,
  pressEnter,
  displayMatchingJobOfWorkflow,
  sdkMatchingJobOfWorkflow,
  pressEnter,
  displayGetDetailsforJob,
  sdkGetDetailsforJob,
  pressEnter,
  displayGetSchemaMappingJson,
  sdkGetSchemaMappingJson,
  pressEnter,
  displayListSchemaMappings,
  sdkListSchemaMappings,
  pressEnter,
  displayTagTheJsonSchema,
  sdkTagTheJsonSchema,
  pressEnter,
  displayGetJobInfo,
  pressEnter,
  displayDeleteResources,
  pressEnter,
  sdkDeleteResources,
  pressEnter,
  goodbye,
]);

/** @type {{ stepHandlerOptions: StepHandlerOptions }} */
export const main = async (stepHandlerOptions) => {
  await myScenario.run(stepHandlerOptions);
};

// Invoke main function if this file was run directly.
if (process.argv[1] === fileURLToPath(import.meta.url)) {
  const { values } = parseArgs({
    options: {
      yes: {
        type: "boolean",
        short: "y",
      },
    },
  });
  main({ confirmAll: values.yes });
}
```
+ 如需 API 詳細資訊，請參閱《*適用於 JavaScript 的 AWS SDK API 參考*》中的下列主題。
  + [CreateMatchingWorkflow](https://docs.aws.amazon.com/goto/AWSJavaScriptSDK/entityresolution-2018-05-10/CreateMatchingWorkflow)
  + [CreateSchemaMapping](https://docs.aws.amazon.com/goto/AWSJavaScriptSDK/entityresolution-2018-05-10/CreateSchemaMapping)
  + [DeleteMatchingWorkflow](https://docs.aws.amazon.com/goto/AWSJavaScriptSDK/entityresolution-2018-05-10/DeleteMatchingWorkflow)
  + [DeleteSchemaMapping](https://docs.aws.amazon.com/goto/AWSJavaScriptSDK/entityresolution-2018-05-10/DeleteSchemaMapping)
  + [GetMatchingJob](https://docs.aws.amazon.com/goto/AWSJavaScriptSDK/entityresolution-2018-05-10/GetMatchingJob)
  + [GetSchemaMapping](https://docs.aws.amazon.com/goto/AWSJavaScriptSDK/entityresolution-2018-05-10/GetSchemaMapping)
  + [ListMatchingWorkflows](https://docs.aws.amazon.com/goto/AWSJavaScriptSDK/entityresolution-2018-05-10/ListMatchingWorkflows)
  + [ListSchemaMappings](https://docs.aws.amazon.com/goto/AWSJavaScriptSDK/entityresolution-2018-05-10/ListSchemaMappings)
  + [StartMatchingJob](https://docs.aws.amazon.com/goto/AWSJavaScriptSDK/entityresolution-2018-05-10/StartMatchingJob)
  + [TagResource](https://docs.aws.amazon.com/goto/AWSJavaScriptSDK/entityresolution-2018-05-10/TagResource)