Effective Search: Improve cross linking

Author: amotl

docs/explain/index.md

@@ -13,6 +13,11 @@ about applications and use cases of CrateDB, trying to put things into a
 bigger picture and joining things together to help answer the question _why_?
 
 
+:::{rubric} 2018
+:::
+
+- {ref}`effective-fulltext-search`
+
 
 :::{note}
 You can expect the more recent documents to be more up-to-date than previous

docs/feature/search/fts/index.md

@@ -274,7 +274,7 @@ files, and corresponding technical backgrounds about their implementations.
 ::::
 
 
-:::{rubric} Guides
+:::{rubric} Tutorials
 :::
 
 ::::{info-card}
@@ -298,7 +298,7 @@ by exploring how to manage a dataset of Netflix titles.
 ::::
 
 
-:::{rubric} Articles
+:::{rubric} Explanations
 :::
 
 ::::{info-card}
@@ -338,34 +338,26 @@ Inverted Indexes for text values, BKD-Trees for numeric values, and Doc Values.
 ::::
 
 
-::::{info-card}
-:::{grid-item}
-:columns: auto 9 9 9
-**Indexing Text for Both Effective Search and Accurate Analysis**
-
-This article explores how Qualtrics uses CrateDB in Text iQ to provide text
-analysis services for everything from sentiment analysis to 
-identifying key topics, and powerful search-based data exploration.
+:::{card} Indexing Text for Both Effective Search and Accurate Analysis
+:link: effective-fulltext-search
+:link-type: ref
 
-{hyper-navigate}`Indexing Text for Both Effective Search and Accurate Analysis
-<[Indexing Text for Both Effective Search and Accurate Analysis]>`
+This article explores how Qualtrics uses CrateDB in their Text iQ product
+to provide text analysis services for everything from sentiment analysis
+to identifying key topics, and powerful search-based data exploration.
 
+It explains integral parts of an FTS text processing pipeline,
+including analyzers, optionally using tokenizers or character filters,
+and how they can be customized to specific needs, using plugins for CrateDB.
++++
 CrateDB uses Elasticsearch technology under the hood to manage cluster
 creation and communication, and also exposes an Elasticsearch API that provides
 access to all the indexing capabilities in Elasticsearch that Qualtrics needed.
 
-The articles explains integral parts of an FTS text processing pipeline,
-including analyzers, optionally using tokenizers or character filters,
-and how they can be customized to specific needs, using plugins for CrateDB.
-
-:::
-:::{grid-item}
-:columns: auto 3 3 3
-{tags-primary}`Introduction` \
-{tags-secondary}`Analyzer, Tokenizer` \
+{tags-primary}`Introduction`
+{tags-secondary}`Analyzer, Tokenizer`
 {tags-secondary}`Plugin`
 :::
-::::
 
 
 :::{toctree}
@@ -383,7 +375,6 @@ effective-search
 [BM25 vs. Lucene Default Similarity]: https://www.elastic.co/blog/found-bm-vs-lucene-default-similarity
 [full-text search]: https://en.wikipedia.org/wiki/Full_text_search
 [Indexing and Storage in CrateDB]: https://cratedb.com/blog/indexing-and-storage-in-cratedb
-[Indexing Text for Both Effective Search and Accurate Analysis]: https://web.archive.org/web/20250210021928/https://www.qualtrics.com/eng/indexing-text-for-both-effective-search-and-accurate-analysis/
 [MATCH predicate]: inv:crate-reference#predicates_match
 [Okapi BM25]: https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/okapi_trec3.pdf
 [search engine]: https://en.wikipedia.org/wiki/Search_engine