XQuery and XPath Full Text 3.0 (Recommendation)

XQuery and XPath Full Text 3.0

From 1.1 Full-Text Search and XML:

As XML becomes mainstream, users expect to be able to search their XML documents. This requires a standard way to do full-text search, as well as structured searches, against XML documents. A similar requirement for full-text search led ISO to define the SQL/MM-FT [SQL/MM] standard. SQL/MM-FT defines extensions to SQL to express full-text searches providing functionality similar to that defined in this full-text language extension to XQuery 3.0 and XPath 3.0.

XML documents may contain highly structured data (fixed schemas, known types such as numbers, dates), semi-structured data (flexible schemas and types), markup data (text with embedded tags), and unstructured data (untagged free-flowing text). Where a document contains unstructured or semi-structured data, it is important to be able to search using Information Retrieval techniques such as
scoring and weighting.

Full-text search is different from substring search in many ways:

  1. A full-text search searches for tokens and phrases rather than substrings. A substring search for news items that contain the string “lease” will return a news item that contains “Foobar Corporation releases version 20.9 …”. A full-text search for the token “lease” will not.
  2. There is an expectation that a full-text search will support language-based searches which substring search cannot. An example of a language-based search is “find me all the news items that contain a token with the same linguistic stem as ‘mouse'” (finds “mouse” and “mice”). Another example based on token proximity is “find me all the news items that contain the tokens ‘XML’ and ‘Query’ allowing up to 3 intervening tokens”.
  3. Full-text search must address the vagaries and nuances of language. Search results are often of varying usefulness. When you search a web site for cameras that cost less than $100, this is an exact search. There is a set of cameras that matches this search, and a set that does not. Similarly, when you do a string search across news items for “mouse”, there is only 1 expected result set. When you do a full-text search for all the news items that contain the token “mouse”, you probably expect to find news items containing the token “mice”, and possibly “rodents”, or possibly “computers”. Not all results are equal. Some results are more “mousey” than others. Because full-text search may be inexact, we have the notion of score or relevance. We generally expect to see the most relevant results at the top of the results list.

Note:

As XQuery and XPath evolve, they may apply the notion of score to querying structured data. For example, when making travel plans or shopping for cameras, it is sometimes useful to get an ordered list of near matches in addition to exact matches. If XQuery and XPath define a generalized inexact match, we expect XQuery and XPath to utilize the scoring framework provided by XQuery and XPath Full Text 3.0.

Definition: Full-text queries are performed on tokens and phrases. Tokens and phrases are produced via tokenization.] Informally, tokenization breaks a character string into a sequence of tokens, units of punctuation, and spaces.

Tokenization, in general terms, is the process of converting a text string into smaller units that are used in query processing. Those units, called tokens, are the most basic text units that a full-text search can refer to. Full-text operators typically work on sequences of tokens found in the target text of a search. These tokens are characterized by integers that capture the relative position(s) of the token inside the string, the relative position(s) of the sentence containing the token, and the relative position(s) of the paragraph containing the token. The positions typically comprise a start and an end position.

Tokenization, including the definition of the term “tokens”, SHOULD be implementation-defined. Implementations SHOULD expose the rules and sample results of tokenization as much as possible to enable users to predict and interpret the results of tokenization. Tokenization operates on the string value of an item; for element nodes this does not include the content of attribute nodes, but for attribute nodes it does. Tokenization is defined more formally in 4.1 Tokenization.

[Definition: A token is a non-empty sequence of characters returned by a tokenizer as a basic unit to be searched. Beyond that, tokens are implementation-defined.] [Definition: A phrase is an ordered sequence of any number of tokens. Beyond that, phrases are implementation-defined.]

Not a fast read but a welcome one!

XQuery and XPath increase the value of all XML-encoded documents, at least down to the level of their markup. Beyond nodes, you are on your own.

XQuery and XPath Full Text 3.0 extend XQuery and XPath beyond existing markup in documents. Content that was too expensive or simply not of enough interest to encode, can still be reached in a robust and reliable way.

If you can “see” it with your computer, you can annotate it.

You might have to possess a copy of the copyrighted content, but still, it isn’t a closed box that resists annotation. Enabling you to sell the annotation as a value-add to the copyrighted content.

XQuery and XPath Full Text 3.0 says token and phrase are implementation defined.

Imagine the user (name) commented version of X movie, which is a driver file that has XQuery links into DVD playing on your computer (or rather to the data stream).

I rather like that idea.

PS: Check with a lawyer before you commercialize that annotation idea. I am not familiar with all EULAs and national laws.

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