Corrects links to rewritten inference_rules page

doc/changelog/0.02-alpha.md

@@ -10,8 +10,8 @@ no_toc: true
   instead of `\[relation_name\].\[column_name\]` for keys to the JSON object.
   Documentation has been updated accordingly.
 - **Non-backward-compatible syntax change**: You must explicitly alias id and
-  variable columns in the inference rules. See the updated [inference rule
-  documentation](../inference_rules.md) for more information.
+  variable columns in the inference rules. See the updated inference rule
+  documentation for more information.
 - Faster factor graph grounding code using pure SQL
 - Support for high-speed C++ sampler
 - Added documentation for high-speed sampler integration

doc/configuration.md

@@ -321,7 +321,7 @@ DeepDive currently supports Boolean and Multinomial variables.
 
 ## <a name="inference_rules" href="#"></a> Inference rules
 
-*Note:* refer to ['Writing inference rules' document](inference_rules.md) for
+*Note:* refer to ['Writing inference rules' document](writing-model-ddlog.md) for
 an in-depth discussion about writing inference rules.
 
 The definitions of inference rules for the factor graphs go in the

doc/index.md

@@ -42,7 +42,7 @@ DeepDive differs from traditional systems in several ways:
 - DeepDive is able to use large amounts of data from a **variety of sources**.
   Applications built using DeepDive have extracted data from millions of documents, web pages, PDFs, tables, and figures.
 
-- DeepDive allows developers to **use their knowledge of a given domain** to improve the quality of the results by [writing simple rules](inference_rules.md) that inform the inference (learning) process.
+- DeepDive allows developers to **use their knowledge of a given domain** to improve the quality of the results by [writing simple rules](writing-model-ddlog.md) that inform the inference (learning) process.
   DeepDive can also take into account user feedback on the correctness of the predictions to improve the predictions.
 
 - DeepDive is able to use the data to [learn "distantly"](distant_supervision.md).

doc/inference.md

@@ -78,7 +78,7 @@ with it? -->
 ## Inference in DeepDive
 
 DeepDive allows the user to write [inference
-rules](inference_rules.md) to specify how to create the factor
+rules](writing-model-ddlog.md) to specify how to create the factor
 graph. A rule expresses concepts like "If John smokes then he is likely to
 have cancer" and, in other words, describes the factor function of a factor and
 which variables are connected to this factor. Each rule has a *weight* (either

doc/inference_rule_functions.md

@@ -6,7 +6,7 @@ title: Inference Rule Function Reference
 # Inference Rule Function Reference
 
 This document lists and describes all functions that can be used in the
-`function` directive when [writing inference rules](inference_rules.md).
+`function` directive when [writing inference rules](writing-model-ddlog.md).
 
 ## DDlog Syntax
 

doc/overview.md

@@ -20,7 +20,7 @@ The results of extraction are stored in the application database and will be the
 DeepDive uses a [factor graph](inference.md) to perform inference.
 The user writes SQL queries to instruct the system about which variables to create.
 These queries usually involve tables populated during the extraction step.
-The variable nodes of the factor graph are connected to factors according to [inference rules](inference_rules.md) specified by the user, who also defines the factor functions which describe how the variables are related.
+The variable nodes of the factor graph are connected to factors according to [inference rules](writing-model-ddlog.md) specified by the user, who also defines the factor functions which describe how the variables are related.
 The user can specify whether the factor weights should be constant or learned by the system (refer to the ['Writing inference rules' document](inference_rules.md) ).
 
 Grounding is the process of writing the graph to disk so that it can be used to perform inference.

doc/reserved_tables.md

@@ -42,7 +42,7 @@ Description of each schema:
 
 - `dd_inference_result_weights_mapping`: a view that maps all distinct factor weights to their description and  their learned values. It is a commonly used view that shows the learned weight value of a factor as well as the number of occurences of a factor.
 
-- `dd_factors_[RULE_NAME]`: a table that is defined by the input query of an [inference rule](inference_rules.md). You can use it as a feature table in BrainDump.
+- `dd_factors_[RULE_NAME]`: a table that is defined by the input query of an [inference rule](writing-model-ddlog.md). You can use it as a feature table in BrainDump.
 
 - `dd_weight_[RULE_NAME]`: a table that stores initial weights for factors, used internally.
 

doc/writing-model-ddlog.md

@@ -373,7 +373,7 @@ and the schema of the weight table. -->
 ### Evidence and Query variables
 
 Evidence is training data that is used to automatically learn [factor
-weights](inference_rules.md). DeepDive will treat variables with existing
+weights](writing-model-ddlog.md). DeepDive will treat variables with existing
 values as evidence. In the above example, rows in the *people* table with a
 `true` or `false` value in the *smokes* or *has_cancer* column will be treated
 as evidence for that variable. Cells without a value (NULL) value will be