XQuery Blue Mountain Project - Part III

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.

85% match

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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XQuery Samples for Blue Mountain Project

This example shows a sequence of XQuery scripts that analyze the language metadata of TEI-encoded journals from the Blue Mountain Project, a digital archive of avant-garde publications. The scripts query a collection called `bluemountain-transcriptions` using the `fn:collection` function, counting documents, extracting distinct languages from `tei:langUsage`, and identifying journals with multiple `tei:textLang/@mainLang` attributes. Later scripts partition documents by language, using `db:create` to store French texts in a new database, and verify the results by listing the URIs of the partitioned files. The code relies on the XQuery 3.1 standard and the eXist-db database API for collection access and database creation.

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XQuery Working Group Challenge

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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<title>{$title}

This example shows a data transformation that converts TEI-encoded metadata into MODS format, preserving bibliographic details such as title, author, language, publication origin, and abstract. The XQuery script, running on the eXist-db platform, uses the `fn:doc` function to read an XML file and XPath expressions to extract specific elements like `tei:titleStmt` and `tei:publicationStmt`. It then constructs a MODS record by mapping these values into the appropriate MODS elements, such as `<titleInfo>` and `<originInfo>`. The code relies on the standard XQuery 3.0 language features, with no external libraries or visualization APIs involved.

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.

Here's the query we started in class today.

This example demonstrates how to extract the title and author from a TEI-encoded XML document stored in a database. It shows the direct retrieval of text content from nested XML elements, returning both pieces of metadata as a sequence. The code uses the XQuery language with the `fn:doc()` function to access the document and XPath expressions to navigate the TEI namespace. It specifically traverses the `teiHeader/fileDesc/titleStmt` structure to pull the title and author values, illustrating a fundamental pattern for querying structured text documents in an XML database environment.

For Arne

This example visualizes the structure of a single TEI-encoded article by extracting metadata from an XML source and rendering it as a document-tree diagram. It shows the hierarchical relationships between the article’s title, author, issue, and page-range fields, emphasizing how the original bibliographic data maps onto the TEI structure. The visualization uses an XQuery script with XPath queries to pull specific values, such as the issue and article numbers via regular-expression replacement, and the starting/ending pages from the `biblScope`. The rendering then relies on a force-directed layout (via `d3.forceSimulation`) to position nodes, with each node representing a TEI element and links showing parent–child dependencies. The data source is the gist’s embedded TEI file, and the output is a static SVG generated client-side, reflecting the parsed metadata without additional interaction.