Fixed typos and implemented acrolinx suggestions

Author: HeidiSteen

articles/search/cognitive-search-output-field-mapping.md

@@ -26,9 +26,9 @@ This article explains how to set up *output field mappings* that determine a dat
 ],
 ```
 
-In contrast with a ["fieldMappings" definition](search-indexer-field-mappings.md) that maps a path between two physical data structures, an "outputFieldMappings" definition maps in-memory data to fields in a search index.
+In contrast with a [`fieldMappings`](search-indexer-field-mappings.md) definition that maps a path between two physical data structures, an `outputFieldMappings` definition maps in-memory data to fields in a search index.
 
-Output field mappings are required if your indexer has an attached [skillset](cognitive-search-working-with-skillsets.md) that creates new information, such as translated strings or key phrases. During indexer execution, AI-generated information exists in memory. To persist this information in a search index, you'll need to tell the indexer where to send the data.
+Output field mappings are required if your indexer has an attached [skillset](cognitive-search-working-with-skillsets.md) that creates new information, such as text translation or key phrase extraction. During indexer execution, AI-generated information exists in memory only. To persist this information in a search index, you'll need to tell the indexer where to send the data.
 
 Output field mappings can also be used to flatten nested data structures during indexing. A regular [fieldMapping definition](search-indexer-field-mappings.md) doesn't support target fields of a complex type. If you need to set up field associations for hierarchical or nested data structures, you can use a skillset and an output field mapping to create the data path.
 
@@ -38,7 +38,7 @@ Output field mappings apply to:
 
 + Search indexes. If you're populating a [knowledge store](knowledge-store-concept-intro.md), use [projections](knowledge-store-projections-examples.md) for data path configuration.
 
-Output field mappings always occur after [skillset execution](cognitive-search-working-with-skillsets.md), although it is possible for this stage to run even if no skillset is defined.
+Output field mappings always occur after [skillset execution](cognitive-search-working-with-skillsets.md), although it's possible for this stage to run even if no skillset is defined.
 
 <!-- 
 The enriched document is really a tree of information, and even though there is support for complex types in the index, sometimes you may want to transform the information from the enriched tree into a more simple type (for instance, an array of strings). 
@@ -51,9 +51,9 @@ Examples of output field mapping scenarios:
 
 * **Content extraction.** You don’t have a skillset but are indexing a complex type from a Cosmos DB database. You'd like to get to a node on that complex type and map it into a field in your index. -->
 
-## Set up an `outputFieldMappings` array
+## Define an output field mapping
 
-Output field mappings are added to the "outputFieldMappings" array in an indexer definition, typically placed after the "filedMappings" array. An output field mapping consists of three parts.
+Output field mappings are added to the `outputFieldMappings` array in an indexer definition, typically placed after the `fieldMappings` array. An output field mapping consists of three parts.
 
 ```json
 "fieldMappings": []
@@ -68,8 +68,8 @@ Output field mappings are added to the "outputFieldMappings" array in an indexer
 
 | Property | Description |
 |----------|-------------|
-| "sourceFieldName" | Required. Specifies a path to enriched content |
-|  "targetFieldName" | Optional. Specifies the search field that receives the enriched content|
+| "sourceFieldName" | Required. Specifies a path to enriched content. See [Reference annotations in an Azure Cognitive Search skillset](cognitive-search-concept-annotations-syntax.md) for path syntax. |
+|  "targetFieldName" | Optional. Specifies the search field that receives the enriched content. This is always a single top-level field or a collection. |
 | "mappingFunction" | Optional. Adds extra processing provided by [search-indexer-field-mappings.md#predefined functions](#mappingFunctions) supported by indexers. In the case of enrichment nodes, encoding and decoding are the most commonly used functions. |
 
 You can use the REST API or an Azure SDK to define output field mappings.
@@ -157,7 +157,7 @@ More concretely, for the ```/document/content/organizations/*/description``` exa
  ["Microsoft is a company in Seattle","LinkedIn's office is in San Francisco"]
 ```
 
-This is an important principle, so we will provide another example. Imagine that you have an array of complex types as part of the enrichment tree. Let's say there is a member called customEntities that has an array of complex types like the one described below.
+This is an important principle, so we'll provide another example. Imagine that you have an array of complex types as part of the enrichment tree. Let's say there's a member called customEntities that has an array of complex types like the one described below.
 
 ```json
 {

articles/search/search-indexer-field-mappings.md

@@ -22,23 +22,23 @@ If inputs and outputs don't match, you can define explicit *field mappings* to s
 
 Field mappings apply to:
 
-+ Physical data structures on both sides of the data stream (that is, between a [supported data source](search-indexer-overview.md#supported-data-sources) and a search index). If you're importing enriched content created by skills, use [outputFieldMappings](cognitive-search-output-field-mapping.md) instead.
++ Physical data structures on both sides of the data stream (that is, between a [supported data source](search-indexer-overview.md#supported-data-sources) and a [search index](search-what-is-an-index.md)). If you're importing enriched content created by skills, use [outputFieldMappings](cognitive-search-output-field-mapping.md) instead.
 
-+ Search indexes. If you're populating a [knowledge store](knowledge-store-concept-intro.md), use [projections](knowledge-store-projections-examples.md) for data path configuration.
++ Search indexes only. If you're populating a [knowledge store](knowledge-store-concept-intro.md), use [projections](knowledge-store-projections-examples.md) for data path configuration.
 
-+ Top-level search fields only, where the "targetFieldName" is either a simple field or a collection. If you need to establish a data path to subfields in a complex type (such as mapping to `address/city`), see [this workaround using outputFieldMappings](cognitive-search-output-field-mapping.md).
++ Top-level search fields only, where the "targetFieldName" is either a simple field or a collection. If you're working with complex data (nested or hierarchical structures), see [outputFieldMappings](cognitive-search-output-field-mapping.md) for workarounds.
 
 ## Supported scenarios
 
 | Use-case | Description |
 |----------|-------------|
-| Name discrepancy | Suppose your data source has a field named `_city`. Given that Azure Cognitive Search doesn't allow field names that start with an underscore, a field mapping lets you effectively map "_city" to "city". </p>If your indexing requirements includes retrieving content from multiple data sources, where field names vary among the sources,you could use a field mapping to clarify the path.|
+| Name discrepancy | Suppose your data source has a field named `_city`. Given that Azure Cognitive Search doesn't allow field names that start with an underscore, a field mapping lets you effectively map "_city" to "city". </p>If your indexing requirements include retrieving content from multiple data sources, where field names vary among the sources, you could use a field mapping to clarify the path.|
 | Type discrepancy | Supposed you want a source integer field to be of type `Edm.String` so that it's searchable in the search index. Because the types are different, you'll need to define a field mapping in order for the data path to succeed. Note that Cognitive Search has a smaller set of [supported data types](/rest/api/searchservice/supported-data-types) than many data sources. If you're importing SQL data, a field mapping allows you to [map the SQL data type](search-howto-connecting-azure-sql-database-to-azure-search-using-indexers.md#mapping-data-types) you want in a search index.|
 | One-to-many data paths | You can populate multiple fields in the index with content from the same source field. For example, you might want to apply different analyzers to each field to support different use cases in your client app.|
 | Encoding and decoding | You can apply [mapping functions](#mappingFunctions) to support Base64 encoding or decoding of data during indexing. |
 | Split strings or recast arrays into collections | You can apply [mapping functions](#mappingFunctions) to split a string that includes a delimiter, or to send a JSON array to a search field of type `Collection(Edm.String)`.
 
-## Set up a `fieldMappings` array
+## Define a field mapping
 
 Field mappings are added to the "fieldMappings" array of an indexer definition. A field mapping consists of three parts.
 
@@ -163,7 +163,7 @@ A document key (both before and after conversion) can't be longer than 1,024 cha
 
 #### Example: Make a base-encoded field "searchable"
 
-There are times when you need to use an encoded version of a field like "metadata_storage_path" as the key, but also need an un-encoded version for full text search. To support both scenarios, you can map "metadata_storage_path" to two fields: one for the key (encoded), and a second for a path field that we can assume is attributed as "searchable" in the index schema.
+There are times when you need to use an encoded version of a field like "metadata_storage_path" as the key, but also need an unencoded version for full text search. To support both scenarios, you can map "metadata_storage_path" to two fields: one for the key (encoded), and a second for a path field that we can assume is attributed as "searchable" in the index schema.
 
 ```http
 PUT /indexers/blob-indexer?api-version=2020-06-30
@@ -226,7 +226,7 @@ Your source data might contain Base64-encoded strings, such as blob metadata str
 
 If you don't include a parameters property, it defaults to the value `{"useHttpServerUtilityUrlTokenEncode" : true}`.
 
-Azure Cognitive Search supports two different Base64 encodings. You should use the same parameters when encoding and decoding the same field. For more details, see [base64 encoding options](#base64details) to decide which parameters to use.
+Azure Cognitive Search supports two different Base64 encodings. You should use the same parameters when encoding and decoding the same field. For more information, see [base64 encoding options](#base64details) to decide which parameters to use.
 
 <a name="base64details"></a>
 
@@ -239,11 +239,11 @@ If the `useHttpServerUtilityUrlTokenEncode` or `useHttpServerUtilityUrlTokenDeco
 > [!WARNING]
 > If `base64Encode` is used to produce key values, `useHttpServerUtilityUrlTokenEncode` must be set to true. Only URL-safe base64 encoding can be used for key values. See [Naming rules](/rest/api/searchservice/naming-rules) for the full set of restrictions on characters in key values.
 
-The .NET libraries in Azure Cognitive Search assume the full .NET Framework, which provides built-in encoding. The `useHttpServerUtilityUrlTokenEncode` and `useHttpServerUtilityUrlTokenDecode` options leverage this built-in functionality. If you're using .NET Core or another framework, we recommend setting those options to `false` and calling your framework's encoding and decoding functions directly.
+The .NET libraries in Azure Cognitive Search assume the full .NET Framework, which provides built-in encoding. The `useHttpServerUtilityUrlTokenEncode` and `useHttpServerUtilityUrlTokenDecode` options apply this built-in functionality. If you're using .NET Core or another framework, we recommend setting those options to `false` and calling your framework's encoding and decoding functions directly.
 
 The following table compares different base64 encodings of the string `00>00?00`. To determine the required processing (if any) for your base64 functions, apply your library encode function on the string `00>00?00` and compare the output with the expected output `MDA-MDA_MDA`.
 
-| Encoding | Base64 encode output | Additional processing after library encoding | Additional processing before library decoding |
+| Encoding | Base64 encode output | Extra processing after library encoding | Extra processing before library decoding |
 | --- | --- | --- | --- |
 | Base64 with padding | `MDA+MDA/MDA=` | Use URL-safe characters and remove padding | Use standard base64 characters and add padding |
 | Base64 without padding | `MDA+MDA/MDA` | Use URL-safe characters | Use standard base64 characters |