Corrects links to walkthrough to spouse example tutorial

doc/browsing.md

@@ -78,7 +78,7 @@ DeepDive applications written in DDlog typically use multiple relations falling
 3. Relation that holds predictions (random variables)
     * whose expectation predicted by DeepDive
 
-For example, in the spouse example we use in [DeepDive's tutorial](walkthrough.md), the relations are:
+For example, in the spouse example we use in [DeepDive's tutorial](example-spouse.md), the relations are:
 
 1. Source
     * `articles`

doc/changelog/0.03-alpha.md

@@ -47,7 +47,7 @@ no_toc: true
 - Updated `spouse_example` with implementations of different styles of extractors.
 
 - The `nlp_extractor` example has different table requirements and usage. See here:
-  [NLP extractor](../walkthrough-extras.md#nlp_extractor).
+  NLP extractor.
 
 - In the `db.default` configuration, users should define `dbname`, `host`, `port`
   and `user`. If not defined, by default system will use the environmental

doc/changelog/0.04.2-alpha.md

@@ -10,8 +10,8 @@ This release focuses mostly on bug fixing and new features.
 
 - A first version of the [generic features library](../gen_feats.md) is
         now available as part of `ddlib`, the utility library included in DeepDive.
-- The `spouse_example` example and the [application
-        walkthrough](../walkthrough.md) were
+- The `spouse_example` example and the application
+        walkthrough were
         expanded to cover the use of [MindTagger](../labeling.md) and of
         the [generic features library](../gen_feats.md).
 - The ` --reg_param <regularizationParameter>` option was added to the

doc/example-chunking.md

@@ -7,7 +7,7 @@ title: Text Chunking Example
 
 ## Introduction
 
-In this document, we will describe an example application of text chunking using DeepDive, and demonstrate how to use **Multinomial variables**. This example assumes a working installation of DeepDive, and basic knowledge of how to build an application in DeepDive. Please go through the [example application walkthrough](walkthrough.md) before preceding.
+In this document, we will describe an example application of text chunking using DeepDive, and demonstrate how to use **Multinomial variables**. This example assumes a working installation of DeepDive, and basic knowledge of how to build an application in DeepDive. Please go through the [tutorial with the spouse example application](example-spouse.md) before preceding.
 
 Text chunking consists of dividing a text in syntactically correlated parts of words. For example, the following sentence:
 

doc/example-smoke.md

@@ -63,7 +63,7 @@ deepdive {
 ```
 ### <a name="Setting Up" href="#"></a> Setting Up
 
-Before running the example, please check that DeepDive has been properly [installed](http://deepdive.stanford.edu/doc/basics/installation.html) and the necessary files (app.ddlog, db.url, and deepdive.conf) and directory (input/) that are associated with this example are stored in the current working directory. Input directory should have data files (friends.tsv, person_has_cancer.tsv, person_smokes.tsv, and person.tsv). In order to use DeepDive a database instance must be running to accept requests, and the database location must be specified in the db.url. You can refer to the detailed [walkthrough](http://deepdive.stanford.edu/doc/basics/walkthrough/walkthrough.html) to setup the environemnt.
+Before running the example, please check that DeepDive has been properly [installed](http://deepdive.stanford.edu/doc/basics/installation.html) and the necessary files (app.ddlog, db.url, and deepdive.conf) and directory (input/) that are associated with this example are stored in the current working directory. Input directory should have data files (friends.tsv, person_has_cancer.tsv, person_smokes.tsv, and person.tsv). In order to use DeepDive a database instance must be running to accept requests, and the database location must be specified in the db.url. You can refer to the detailed [tutorial](example-spouse.md) to setup the environemnt.
 
 ### <a name="Running" href="#"></a> Running
 

doc/generating_negative_examples.md

@@ -43,5 +43,5 @@ For example, most people mention pairs in sentences are not spouses, so we can r
 <!-- TODO: improve this -->
 
 
-To see an example of how we generate negative evidence in DeepDive, refer to the [example application walkthrough](walkthrough.md#candidate_relations).
+To see an example of how we generate negative evidence in DeepDive, refer to the [example application tutorial](example-spouse.md#1-3-extracting-candidate-relation-mentions).
 

doc/incremental.md

@@ -5,8 +5,10 @@ title: Incremental Workflow
 
 # Building an Incremental Application
 
-This document describes how to build an incremental application using [DDlog][] and
-DeepDive. The example application is [the spouse example](walkthrough.md).
+<todo>Drop this page?</todo>
+
+This document describes how to build an incremental application using DDlog and
+DeepDive. The example application is [the spouse example](example-spouse.md).
 
 This document assumes you are familiar with basic concepts in DeepDive and the
 spouse application tutorial.
@@ -38,7 +40,7 @@ the base part. The workflow can be summarized as follows.
 
 The incremental version of the spouse example is under [`examples/spouse_example/postgres/incremental`](https://github.com/HazyResearch/deepdive/tree/master/examples/spouse_example/postgres/incremental).
 The only difference is that all the arrays are transformed into string using `array_to_string` with delimiter `'~^~'` due to DDlog's limited support for array type.
-You can follow the [corresponding section in the original walkthrough](walkthrough.md#loading_data) to load the data.
+You can follow the [corresponding section in the original walkthrough](example-spouse.md#1-1-loading-raw-input-data) to load the data.
 
 Alternatively, you can try the handy scripts included in the incremental example provided in the source tree.
 
@@ -51,7 +53,7 @@ cd examples/spouse_example/postgres/incremental
 ### Writing Application in DDlog
 
 In order to make use of the incremental support of DeepDive, the application must be written in DDlog.
-Please refer to [DDlog tutorial][DDlog] for how to write your DeepDive application in DDlog.
+Please refer to DDlog tutorial for how to write your DeepDive application in DDlog.
 Let's assume you have put the DDlog program shown below in a file named `spouse_example.f1.ddlog` under the application folder.
 
 <script defer src="https://gist-it.appspot.com/github.com/HazyResearch/deepdive/blob/master/examples/spouse_example/postgres/incremental/spouse_example.f1.ddlog?footer=minimal">
@@ -144,4 +146,3 @@ export DBNAME=deepdive_spouse_noninc
 ```
 
 
-[DDlog]: ddlog

doc/installation.md

@@ -77,7 +77,7 @@ You can install DeepDive and all its dependencies with a single command.
     It downloads all examples and tests for the DeepDive release and runs the tests using the installed one.
     To only download the example applications, use the `deepdive_examples_tests` option.
 
-Congratulations! DeepDive is now installed on your system, and you can proceed to the [next steps](walkthrough.md).
+Congratulations! DeepDive is now installed on your system, and you can proceed to the [next steps](example-spouse.md).
 
 
 

doc/labeling.md

@@ -43,7 +43,7 @@ Therefore, producing the right sample for correct estimation is Mindtagger user'
 
 ### Use Case 1: Measuring Precision of the Spouse Example
 
-In the following few steps, we explain how Mindtagger can help you perform a precision estimation task using the [spouse example in our walkthrough](walkthrough.md).
+In the following few steps, we explain how Mindtagger can help you perform a precision estimation task using the [spouse example in our tutorial](example-spouse.md).
 
 #### 1. Prepare the data items to inspect
 

doc/opendata/index.md

@@ -42,7 +42,7 @@ most datasets, we provide data in both formats.
   - **DeepDive-ready DB Dump.** In this format, the
   data is a database table that can be loaded directly
   into a database with PostgreSQL or Greenplum. The schema of this
-  table is the same as what we used in our [walkthrough example](../walkthrough.md)
+  table is the same as what we used in our [tutorial example](../example-spouse.md)
   such that you can start building your own DeepDive applications
   immediately after download.
 

doc/opendata/schema.md

@@ -11,7 +11,7 @@ as a table.
 This page contains a description of the schema of this table.
 Note that the schema of this table
 is very similar to what was used in our
-[walkthrough example](../walkthrough.md),
+[tutorial example](../examples/spouse.md),
 but with a few twists to represent more sophisticated provenance
 of each word.
 

doc/paleo.md

@@ -13,9 +13,9 @@ found [here](https://github.com/zhangce/pdd/tree/master/example_data).
 ## Prerequisite
 
 To fully understand PaleoDeepDive, it is useful to first go through the online tutorial for DeepDive,
-which provides an overview of [basic concepts](walkthrough.md)
+which provides an overview of [basic concepts](example-spouse.md.md)
 and an example of
-[how relations between people, locations, and organizations](walkthrough.md) are inferred.
+[how relations between people, locations, and organizations](kbc.md) are inferred.
 In this document, you will see how similar approaches can be applied
 to paleontology in order to extract relations between
 biological taxa, geological rock formations, geographic locations, and geological time intervals.
@@ -86,7 +86,7 @@ This row encodes the sentence
 > Edzf U, Ciudad Universitaria, Morelia, Michoacdn, México
 
 The columns wordidxs, words, poses, ners, lemmas, dep\_paths, and dep\_parents are
-defined by the NLP software and are consistent with our [other walkthrough](walkthrough.md). The column
+defined by the NLP software and are consistent with our [tutorial](example-spouse.md). The column
 bounding_boxes contains a list of strings, one for each word. For each word,
 the string defines a bounding box containing that word in the PDF. As an example, `p1l809t1151r871b1181`, defines a box on page 1, left margin 809, top margin 1151, right margin 871, and bottom margin 1181 (units are in pixels).
 
@@ -202,7 +202,7 @@ is:
     }
 
 If you have difficulty in understanding this syntax, please refer to
-our [more general walkthrough for DeepDive](walkthrough.md) first.
+our [more general tutorial for DeepDive](example-spouse.md) first.
 This extractor goes through the sentence table, and for each sentence,
 it executes the extractor in ext\_temporal\_local.py.
 
@@ -447,7 +447,7 @@ This extractor contains four components.
 Inferences rules are used to specify the correlation
 among random variables. Most of these inference
 rules have a similar form as the one in our
-[other walkthroughs](walkthrough.md), and here we show
+[tutorial](example-spouse.md), and here we show
 two examples.
 
     inference_rule_formation : {