Hi Laura,
Great question.  Unfortunately, I think you’re going to be fairly limited when it comes to having granular control over fields and facet indexing in ContentDM (someone correct me if I’m wrong).

But to answer your question about general steps involved with indexing the metadata AND full text of a METS document…

To have the most control over how your data is indexed, you will want to use a search platform.  Apache Solr<> is used in a majority of library-related software, so I’ll use that in my examples, although there are several others.  Solr doesn’t have a concept of “metadata” and “content”, just “fields" that you can use to search both.

In the case of your METS data, you will need to first transform it into a more simplified document (Solr XML) containing the fields that matter for a particular search interface and are defined in the schema<>.  This transform step can be done in any number of ways, but XSLT is fairly common.  To index the full-text content that your METS document points to, you can build that into your transform script/stylesheet, or you can run a separate script/process later that updates the record with the full-text.  In the case of a “compound object” you may need to have a script iterate over lots of separate content files and add them to the Solr document that represents a yearbook.

There are a few ways to add data to a solr index, but a common one in library-land is to add (and update) records to the Solr index by POSTing your freshly “transformed" data via HTTP (here’s the Solr quickstart tutorial<>).

Customizing your search results (weighting, stemming, rows per page, etc.) can be handled in the Solr config file<>.  For example, you can tweak the weight/relevance of the query based on which fields it matches.

When you query Solr over HTTP, it will return results in XML or JSON that you can then render in a display or discovery interface. Blacklight<> is one example of a discovery interface.

Sorry if I’ve covered stuff you already know.  There are lots of tools, applications, and frameworks that will simplify the process (perhaps too much in some cases!), but the best give you the most control over how you index and retrieve your data.  I think that covers the basics and hopefully answers your question.

P.S. -  I’m not sure that even Solr will help you locate the Doyle Owl. ;)

On Jan 26, 2016, at 7:30 PM, Laura Buchholz <[log in to unmask]<mailto:[log in to unmask]>> wrote:

Hi all,

I'm trying to understand how digital library systems work when there is a
need to search both metadata and item text content (plain text/full text),
and when the item is made up of more than one file (so, think a digitized
multi-page yearbook or newspaper). I'm not looking for answers to a
specific problem, really, just looking to know what is the current state of
community practice.

In our current system (ContentDM), the "full text" of something lives in
the metadata record, so it is indexed and searched along with the metadata,
and essentially treated as if it were metadata. (Correct?). This causes
problems in advanced searching and muddies the relationship between what is
typically a descriptive metadata record and the file that is associated
with the record. It doesn't seem like a great model for the average digital
library. True? I know the answer is "it depends", but humor me... :)

If it isn't great, and there are better models, what are they? I was taught
METS in school, and based on that, I'd approach the metadata in a METS or
METS-like fashion. But I'm unclear on the steps from having a bunch of METS
records that include descriptive metadata and pointers to text files of the
OCR (we don't, but if we did...) to indexing and providing results to
users. I think another way of phrasing this question might be: how is the
full text of a compound object (in the sense of a digitized yearbook or
similar) typically indexed?

The user requirements for this situation are essentially:
1. User can search for something and get a list of results. If something
(let's say a pamphlet) appears in results based on a hit in full text, the
user selects the pamphlet which opens to the file (or page of the pamphlet)
that contains the text that was matched. This is pretty normal and does
work in our current system.
2. In an advanced search, a user might search for a name in the "author"
field and a phrase in the "full text" field, and say they want both
conditions to be fulfilled. In our current system, this won't provide
results when it should, because the full text content is in one record and
the author's name is in another record, so the AND condition can't be met.
3. Librarians can link description metadata records (DC in our case) to
particular files, sometimes one to one, sometimes many to one, sometimes
one to many.

If this is too unclear, let me know...

Laura Buchholz
Digital Projects Librarian
Reed College Library
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