Example XQuery for Sara S.

Tricky Metadata Problem

This visualization demonstrates the geographic distribution of artists' books by filtering MARCXML records according to publication location codes, then organizing them into a simple table. It shows which US states and territories are represented in the source collection, with each row containing the record’s leader, normalized title, and three-letter location code. The XQuery script uses the `fn:doc()` function to fetch remote XML, the `marc` namespace to parse MARC fields, and `csv:serialize()` from the BaseX or eXist-db library to generate a CSV output. Titles are cleaned by stripping trailing slashes and normalizing whitespace via string functions.

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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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Simple example of requesting items from the SRU gateway at the Library of Congress

This example demonstrates how to query the Library of Congress SRU gateway for a single bibliographic record, using the Library of Congress Control Number (LCCN) as the search key. It shows how to request metadata in a specific format—here, MODS, though DC and MARCXML are also supported—and then extract the record data from the SRU XML response. The code relies on the `http:send-request` function from the EXPath HTTP Client to perform the GET request, and uses XQuery 3.0 with namespace declarations for MODS, Dublin Core, MARCXML, and the SRU/Z39.50 protocol. The returned record is selected via the `zs:recordData` path.

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Management XQuery Exercise

This example shows how XQuery 3.1 can structure and filter flat HTML documents, specifically extracting sections from a management report. It uses `fn:collection()` to load source documents, `fn:index-of()` and positional predicates to locate and slice a section between “ITEM 1A” and “ITEM 1B,” and text-matching (`contains text`) to filter paragraphs for keywords like “refinery.” The final query, `return-items-filtered.xqy`, cleans results by removing empty paragraphs, spans, and break tags, then wraps the output in a custom `<section>` element with an `item` attribute.

Xquery for Unit Dates

This example demonstrates a preprocessing step for visualizing dates in archival finding aids, extracting four-digit years from `<unitdate>` elements in a single EAD XML document. The XQuery script uses the XQuery 3.0 `copy` and `modify` expressions to insert a `normal` attribute containing the extracted year onto each unitdate node. It leverages a namespace declaration for the EAD standard and the `fn:doc` and `fn:replace` functions to fetch the remote XML file from GitHub and parse the date text via regular expressions. The output is the modified XML nodes, ready for downstream temporal analysis or charting.

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.

XPath Examples - Mellon Institute

This example demonstrates how XPath queries navigate and extract specific content from a TEI-encoded document of the Mellon Institute at Vanderbilt. It shows the progression from simple absolute paths to descendant-or-self axes, then to predicate-based filtering, and finally to more complex relational paths. The code uses the XPath API to select titles, names, sex attributes, and speaker-related paragraphs, referencing the updated TEI Guidelines for the sex element. The example is presented as a gist authored by CliffordAnderson, with the source data being the structured XML markup of the institute’s records, illustrating query patterns for text extraction and attribute matching.

fn:analyze-string results

This example demonstrates the results of an XQuery transformation that uses the XPath 3.1 `fn:analyze-string` function to parse bibliographic text. Given a TEI XML source, the query extracts page ranges from bibliographic entries by matching patterns like "p. 35-42" and wraps them in a `tei:citedRange` element, while preserving the remaining text. The output shows a series of TEI `<bibl>` records where the cited page ranges are cleanly separated from the bibliographic citation text, enabling structured access to page references for downstream processing or display.