XQuery Working Group Challenge

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XQuery Working Group Challenge

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Last edited Jul 22, 2015

Created on Jul 22, 2015

This example transforms the PROIEL treebank’s Latin New Testament XML into a sequence of verse elements, each tagged with its citation and concatenated token forms. It fetches the remote XML via `fn:doc`, extracts distinct citation parts, and filters tokens by matching `@citation-part` to build structured verses. The code uses XQuery 3.1’s element constructors and `fn:data` to output text, demonstrating a direct pipeline from hierarchical treebank data to flat verse-like text.

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For XQuery4Humanists

This example visualizes the frequency and distribution of words extracted from a TEI-encoded poem, processing the text to remove punctuation, normalize case, and tokenize it into a list. The visualization shows the resulting word list as a simple textual display, highlighting the raw output of the XQuery processing pipeline. The code uses XQuery 3.0 functions like `fn:tokenize`, `fn:lower-case`, and `fn:replace` to clean the data, pulling the source directly from a remote XML file hosted on GitHub. The rendering relies solely on the XQuery return, with no additional JavaScript or charting libraries involved.

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XQuery Blue Mountain Project - Part III

This example shows a pipeline for transforming a single TEI XML document from the Blue Mountain Project—a digitized 1920 issue of the French journal *Action*—into clean, article-level TEI files. The first script removes facsimile elements, end-hyphenation markers, and line breaks, replacing `<lb>` elements with spaces. A second script extracts metadata and article content into a fresh TEI template, while a third builds individual TEI files for each article. The code uses XQuery 3.1 with the `fn:doc` API for document access and node operations like `copy`, `modify`, and `replace node`.

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XQuery for Partitioning the Blue Mountain Project

This example demonstrates a pipeline for cleaning, partitioning, and exporting articles from the Blue Mountain Project’s TEI transcriptions into language-specific databases. It shows how XQuery 3.1 scripts transform raw periodical XML by removing facsimile and hyphenation artifacts, splitting documents into individual articles, and grouping them by `mainLang` attributes into new databases. The workflow then exports French-language texts as plain .txt files, and R code using the `tm` and `SnowballC` libraries performs text mining, including term-document matrices and stemming.

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Code snippets for XQuery Working Group (Text Mining at Scale)

This example demonstrates a text-mining workflow for querying the Victorian Women Writers Project corpus stored in a BaseX database, using XQuery and XPath Full Text 1.0. It walks through creating structured records from TEI XML, saving them to a new database, and running full-text searches with scoring, stemming, and thesaurus-based matching. The code exercises functions like `db:replace`, `ft:mark`, and `ft:extract` to highlight matching text, while also showing stop-word filtering and boolean operators for queries.

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Blue Mountain Project - Dividing Up Languages

This example shows how XQuery processes TEI-encoded periodical documents from the Blue Mountain Project, splitting them into individual articles for database storage. The code traverses document collections with `fn:collection` and `fn:doc`, prunes unwanted elements and attributes using the `copy`/`modify` XQuery Update Facility, and reconstructs TEI structures. It uses the `db:create` function from the eXist-db API to build a new database collection from the transformed articles, demonstrating a pipeline for dividing source documents into discrete, queryable units.

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Gist ccc08dae693660d7aa1b8cf2708fd13d

This example demonstrates how to query and highlight text passages in the Victorian Women Writers Project TEI corpus using XQuery 3.1. It shows a progression from simple full-text searches for repeated terms to fuzzy matching via `ft:search` and `ft:extract`, and finally visualizes results by marking hits with `ft:mark` and segmenting them into tumbling windows. The code relies on BaseX’s full-text and windowing capabilities to extract and structure paragraph-level matches containing “garden” or “house.”

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Gist 162dbae87c5cab9f59fd433e235bafb2

This example demonstrates how to extract and collate poems from the Victorian Women Writers Project, a corpus of TEI P5-encoded texts. It shows the process of filtering documents by genre, retrieving individual poems, and normalizing their text. The code uses XQuery 3.1 with the BaseX database and its `fn:collection`, `fn:doc`, and `fn:normalize-space` functions. It outputs the results as CSV using BaseX's serialization options, specifying comma separators and headers, while converting poem text to lowercase.

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This example of recursive descent in XQuery is adapted from Joe Wicentowski (https://gist.github.com/joewiz/2331558#file-typeswitch-skeleton-xq). It rather naively formats scenes from the Folger Library Shakespeare TEI editions for display in HTML. This example is intended for the demonstration of the concept, not actual use.

This example shows recursive descent in XQuery rendering scenes from a Folger Library Shakespeare TEI edition into HTML. It demonstrates how a typeswitch pattern matches distinct TEI elements—such as `tei:head`, `tei:w`, `tei:stage`, and `tei:speaker`—to produce formatted output like headings, stage directions, and speaker labels. The code relies on XQuery 3.0’s built-in `typeswitch` and function recursion, using `local:render` and `local:recurse` to traverse the XML tree. It serializes to XHTML via `exist:serialize`, pulling the source document from `/db/shakespeare/JC.xml` without external libraries.

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