Christina Harlow Metadata Lackey

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Thoughts On Geospatial Metadata

I recently decided I need to spend some time after each work day writing up my thoughts on work stuff. If nothing else, these can serve as an outlet and time for improved pondering. So please forgive the winding, rambling nature these posts will take. From these, however, I hope to find little nuggets of ideas that I can actually take and make into decent presentations, workshops, articles, whatever. As ever, I welcome your feedback and questions on these thoughts.

Recently, I made an OpenRefine Reconciliation Service for Geonames, and in particular, for reconciling a metadataset using Library of Congress Authority terms (think LCSH and LCNAF accessible via and wanting to pull back Geonames identifiers (URIs, rather, as they are in that vocabulary) and coordinates following ISO 6709 standard.

A number of interesting (perhaps only to me) points came up while working on this project and using this reconciliation service (which I do quite often, as we are migrating legacy DC/XML from a variety of platforms to MODS/XML, and part of this involves batch data enhancements). The questions and points below are what in particular I hope to address in this post:

  • Can we standardize, for lack of a better work, the process by which someone creates an OpenRefine reconciliation service based off of a REST API on top of any vocabulary? Also, API keys are the devil.
  • More specific to geographic terms/metadata, why do I feel the need to use Geonames? Why not just use LC Authorities, considering they’ve ‘pulled in’ Geonames information, matching URIs, in batch?
  • Do we really want to store coordinates and a label AND a URI (and whatever else) for a geographic term within a descriptive metadata record element? Does it even matter what we want when we have to consider what we need and what our systems can currently handle?
  • As a follow-up, where the heck would we even put all those datapoints within anything other than MODS? What are some of the RDF metadata models doing, and how can folks still working with XML (or even MARC) prepare for conversion to RDF? Some ideas on the best practices I’m seeing put about, as well as a few proposals for our own work.

And other various points that come up while I’m writing this.

Lets All Make OpenRefine Reconciliation Services!

Professionally, I’m in some weird space between cataloging, general data stuff, and systems. So don’t take my word on anything, as usually I’m just translating what already exists in one subdomain to a different subdomain (despite the fact that library domains just assume they can already talk to each other, often).

I start with this though to say I’m not a developer of any sort, yet I was able to pull together the Geonames OpenRefine Reconciliation Service via trial and error, knowledge of how Geonames REST API works (in particular, how queries are structured and data returned), and also by building off all the great community-sourced work that exists. In particular, Ted Lawless wrote a great FAST OpenRefine Reconciliation Service that I used to create something for Geonames. There are some OpenRefine Reconciliation Service templates for others to build off of - in particular, a very simple one in python, some other examples written in php - and an OpenRefine Reconciliation Service API wiki document that you should take with a grain of salt, as it needs seriously revisions, updates, and expansions (which, er, maybe I should help with). This is just scratching the surface of OpenRefine reconciliation examples, templates, and documentation.

However, once you get into building or modifying an existing reconciliation service (recon service from this point on for the sake of my typing hands), you might run into some of the same roadblocks and questions I did. For example, with the Geonames recon service, I wanted in particular to return coordinates for a matched name. However, I did not want to do this via matching with a name, pulling the entire record for that name serialized however (json, xml, doesn’t matter) into a new column, then parsing that records column to find the particular datapoints I wanted to add for each row. This method of ‘reconciliation’ in OpenRefine - seen when someone adds a new column in OpenRefine by retrieving URLs generated from the originating column values - takes far longer than using a recon service, is not scalable unless your metadatasets are amazingly consistent, and offers more chances for errors as you have to parse the records in batch for each datapoint therein you want to pull out (otherwise, you’re spending so much time on each value, you might as well have faceted the column then searched manually in your authority of choice for the datapoints you’re hoping to retrieve). Yet, the recon service templates and the OpenRefine Recon Service metadata (explained somewhat in that above wiki page) did not offer me a place to store and return the coordinates within the recon service metadata (without a hack I didn’t want to make).

As I’m writing this, I realize that a post detailing all the ways one can use OpenRefine to do ‘reconciliation’ work would be helpful, so we know we are comparing apples to apples when discussing. For example, another way that reconciliation can happen in OpenRefine - using the now unsupported but still viable and immensely useful DERI RDF Extension - is yet another approach that has its merits, but could possibly muddle someone’s understanding of what I’m discussing here: the Reconcilation Service API script/app, in my case built in python and working with a REST API.

For what it’s worth, I’d really like to have an OpenRefine in LODLAM call on the different reconciliation services, examples, and how to build them. If you’re interested in this, let me know. I’m happy to talk part of this about my own experiences, but I’d like to have at least 1 other person talk.

Regardless, back to building the Geonames recon service, I could get a basic recon service running by plugging in the Geonames API information in place of the FAST API information in Lawless’ FAST Recon service code, with minor modifications for changes in how Geonames returns data, and the inclusion of an API key. The requirement of an API key made this work that much harder, because it means folks need to go in an add their own (or use the sample one provided and hit the daily API calls limit rather quickly) in the core flask app code. I’m sure there are ways to have the user submit their own code via the CLI before firing up the service, or in other ways, but I kept it as simple as possible since this annoyance wasn’t my main concern.

My main concern with this API was getting good matches with metadata using terms taken from the Library of Congress Authorities, in particular LCSH and LCNAF, and returning top matches along with coordinates (and the term and term URI from Geonames, luckily built into the recon service metadata by default). The matching for terms use the fuzzy-wuzzy library, normally seen in most python Openrefine recon apps regardless. The coordinates for a match were simply appended to the matched term with a vertical bar, something easy to split values off of in OpenRefine (or to remove via the substring function if you happen to not want the coordinates).

But the first tests of this service described above returned really poor results (less than 10 direct or above 90% matches for ~100 record test metadatasets), considering the test metadatasets were already reconciled, meaning the subject_geographic terms I was reconciling were consistent and LCNAF or LCSH (as applicable) form. This is when I took a few and searched in Geonames manually. I invite you to try this yourself: search Knoxville (Tenn.) in You get no matches to records from the Geonames database, and instead (as is the Geonames default), have results from Wikipedia instead. This is because Geonames doesn’t like that abbreviation - and my sample metadatasets, all taken from actually metadatasets here at work - are all United States-centric, particularly as regards subject_geographic terms. Search now for Knoxville (Tennessee), or Knoxville Tennessee, or Tennessee Knoxville - the first result will be exactly what you’re searching.

What to do, at least in the context of OpenRefine recon services? Well, write a simple python script that replaces those LC abbreviations for states with the full name of the state, then searches Geonames for matches. See that simple, embarassingly simple solution here: Yep, it’s very basic, but all of a sudden, the reconciliation service was returning much, much better results (for my tests, around 80% direct matches). I invite others to try using this recon service and return your results, as well as other odd Library of Congress to Geonames matching roadblocks for more international geographic metadata sets.

There are other things I wish to improve in the Geonames recon service - some recon services offer the ability, if the top three results returned from reconciliation are not what you wanted at all, to then search the vocabulary further without leaving OpenRefine. I played around a bit with adding this, but had little luck getting it to work. I also want to see if I can expand the OpenRefine recon service metadata to avoid the silly Coordinates hack. I’d love to show folks how to host this somewhere on the web so you do not need to run the Geonames recon service via the simple python server before being able to use it in OpenRefine - however, the API key requirement nips this in the bud.

More to the point though, I want to figure out how better to improve Geonames matching for other, ‘standard’ library authority sources. It seems to me like something is fundamentally off with library data work with the authority services are, from an external data reconciliation viewpoint, so siloed. Not at all what we want if going towards a library data on the web, RDF-modeled, world. It seems. to me.

Geonames versus Library of Congress Authorities

So this brings me to two questions, both of which I got from various people hearing my talk about this work: why not just reconcile with the Library of Congress Authorities (which have been matched with Geonames terms via some batch enhancements recently and should have coordinates information now, as it is a requirement for geographic names authoirties in RDA)? And, alternatively, why not just match with Geonames and use their URI, leaving out LCSH for subject_geographic or other geographic metadata (and using it instead for personal/corporate names that aren’t geographic entities, or topical terms, etc.)?

I think this shows better than anything I could say a fundamental divide in how different parts of library tech world see “Authority” data work.

Here is why I decided to not use the Library of Congress Authorities entirely for geographic reconciliation:

  1. The reconciliation with Geonames within LCNAF/LCSH is present, but is also a second level of work that undermines my wanting to make a helpful, fast, error-averse Openrefine recon service. This is not to say that I don’t think linked authorities data shouldn’t have these cross-file links; of course they should, be also read my bit below on descriptive versus authority record contents.
  2. The hierarchies in LCNAF/LCSH are…lacking. I’d like to know that, for example, Richmond (Va.) is in Virginia (yes, I know it says Va. in that original heading, but where is the link to the record to Virginia? It’s not there), which is in United States, etc. etc. Geonames has this information captured.
  3. When there are coordinates, even if matched with Geonames, it is often stored in a mads:citation-note, without machine-readable data on how the coordinates are encoded. I know I want to pull ISO 6709, but not have to check manually the coordinates for each record to get the information from the right statement and check the encoding.

Note: I’d really love to pull the Library of Congress Name Authority File linked open dataset from and test what my limited experience has led me to believe on LCNAF lacking consistent Geonames matching, coordinates, and hierarchies - particular for international geographic names, as my own work leads me often to work just with geographic names from the United States.

Note: Because I don’t think the Library of Congress Authorities are the best currently for geographic metadata DOES NOT MEAN I do not use them all the time, appreciate the work it took to build them, or think they should be depreciated or disappear. What I’d like to see is more open ways for the Library of Congress Authorities to share data and data modeling improvements with 1. the library tech community already working on this stuff 2. other, non-traditional ‘authorities’ like Geonames that have a lot to offer. Some batch reconciliation work pulling in limited parts of existing, non-traditional ‘authorities’ without a mind to how we can pull that data out in matchine-actionable reconciliation processes hasn’t really helped boost their implementation in the new world of library data work.

Yet, I am really, really appreciative of all the work the Library of Congress folks do, I wish they were so understaffed, and hell, I’d give my left arm to work there except I’m not smart enough.

Alright, moving on…why not just use the Geonames URIs and labels alone, if I feel this way about the Library of Congress Authorities and geographic terms? The simple reasons is: Facets. Most subject terms are being reconciled, if they weren’t already created using, the LCNAF and LCSH vocabularies. LCSH and LCNAF makes perfect sense as the remaining top choice for topical subjects and names (although there are other players in the non-traditional names authorities field, which I’ll discuss in some other post maybe). As our digital platform discovery interface, as well as our primary library catalog/all data sources discovery interface, are not built currently to facet geographic subjects separate from topical subjects (separate from names as subjects, etc. etc.). So for the sake of good sorting, intelligible grouping, etc., LC Authorities for the terms/labels remain the de facto.

Additionally, I’m not sold that the Library of Congress won’t catch on to the need to open up more to cross-referencing and using non-traditional data sources in more granular and machine-actionable ways. They seem to be at work on it, so I’d prefer to keep their URIs or labels there so reconciliation can happen with their authorities. One must mention too that Geonames does not store references to the same concepts but in the Library of Congress Authorities, so keeping just the Geonames term and URI would make later reconciliation with the Library of Congress Authorities a pain (not to mention, search ‘Knoxville Tennessee’, the preference for Geonames queries, in, and see all the results you get that aren’t ‘Knoxville (Tenn.)’. ARgh.)

What to do, what to do… well, build a Geonames recon service that takes Library of Congress Authorities headings and returns Geonames additional information, for now.

Descriptive Metadata & Authority ‘Metadata’

Let me start this section by saying that Authorities are within the realm of ‘descriptive metadata’, sure. However, when we say ‘descriptive metadata’, we normally think of what is known in Cataloging parlance (for better or for worse) as bibliographic metadata. Item-specific metadata. This digital resource, or physical resources, present in the catalog/digital collection, that we are describing so you can discovery, identify, access (okay okay access metadata isn’t descriptive metadata).

What about authority data? We see a lot of authority files/vocabularies are becoming available as Linked Open Data, but how do we see these interacting with our descriptive metadata beyond generation of ‘authorized’ access point URIs and perhaps some reconciliation, inference, tricks of the discovery interface via the linking and modeling? The Linked Open Data world is quickly blurring the demarcation between authority and non-authority, in my opinion - and I find this really exciting.

So, returning to geospatial metadata, it is not my preference to store coordinates, label, URI(s) - maybe even multiple URIs if I really want to make sure I capture both Geonames and LCNAF - in the descriptive record access point. That’s to say, I’m not terribly excited that, in MODS/XML, this is how I handle the geospatial metadata involved presently:

    <mods:geographic authority="naf" valueURI="">Knoxville (Tenn.)</mods:geographic>
        <mods:coordinates>35.96064, -83.92074</mods:coordinates>

Sure, that works, but it can be hell to parse for advanced uses in the discovery layer, as well as to reconcile/create in the descriptive metadata records in batch, and to update as information changes. Also, where is the Geonames authority/uri? Can, and more importantly, should we repeat the authority and valueURI attributes? Break the MODS validation and apply perhaps an authority attribute to the coordinates element, stating from where we retrieved that data? Where is the attribute on either cartographics or coordinates stating what standard the coordinates are following for the machine parsing this to know?

Also, more fundamentally, how much of this should be statements in an Authority record? Wouldn’t you rather have (particularly if you’re soon going to MODS/RDF or perhaps another model in RDF that is actually working at present) something that just gives the locally-preferred label and a valueURI to 1 authority source that can then link to other authority sources in a reliable and efficient manner? Perhaps link to the URI for the Geonames record, then use the Library of Congress Authorities Geonames batch matching to pull the appropriate, same as Library of Congress Authority record that way.

So this is something I’ve been thinking about and working on a lot lately: creating an intermediate, local datastore that handles authority data negotiation. Instead of waiting for LC Authorities to add missing terms to their database (like Cherokee town names, or Southern Appalachia cross-references for certain regional plants, or whatever), or parseable coordinates from Geonames, or for Geonames to add LC Authorities preferred terms or URIs, or whatever other authority you’d like to work with but has XYZ issues, have a local datastore working based off an ontology that is built to interact with the chosen authorities you want to expand upon, links to their records, but puts in your local authority information too. It is a bit of a pipe dream at the moment, but I’ve had some small luck building such a thing using Skosmos, LCNAF, LCSH, Geonames, and Great Smoky Mountain Regional Project vocabularies. We’ll see if this goes anywhere.

Basically, returning to the point of this post, I want the authority data to store information related to the access point mentioned in the descriptive record, not the descriptive record storing all the information. There are data consistency issues as mentioned, as well as the need then for discovery interfaces being built for ever more complex data models (speaking of XML).

However, for the time being, the systems I work with are not great at this Authority reconciliation, so I put it as consistently as I can all in that MODS element(s).

I should note, as a final note I think for this post, that I do not add these URIs or other identifiers as ‘preparation for RDF’. Sure, it’ll help, but I’m adding these URIs and identifiers because text matching has many flaws, especially when it comes to names.

Things to follow-up on:

  1. Getting an OpenRefine Recon Service call together
  2. Discussing some of the geographic data models out there, as well as what a person working with something other than MODS can do with geographic or other complex authorized accent point data
  3. A million other things under the sun.