Linked Data provides us with an incredible opportunity to re-think how we approach sharing information about LAM collections.  However, these opportunities are also fraught with danger and important challenges that we must face.  Translating existing standards into compliant Linked Data will take more than just cross-walking terms with similar meanings, it also means mapping between conceptual models and ontologies.   Linked Data also provides us new opportunities to mix models and vocabularies in ways that we haven’t been able to do before.  How can we take better advantage of these opportunities?

Ultimately, creating Linked Data standards and practices is a set of design problems that we are all engaged in.   Elizabeth Churchill has called for “Data Aware Design” and the need to bring human-computer interaction methods to bear on these problems.  At the Summit I will be presenting a Dork Short about a new site that I’m launching to do just this.   LODLAM Patterns will identify Linked Data design patterns (which I’m calling representation patterns) for cultural heritage resources.   The idea is to identify common problems that we are trying to solve and link them to the solutions that are available across the many, many standards for describing LAM resources.  My goal is to create a resource that will spur discussions focused on problems/solutions,  provide newcomers a way to navigate the LOD standards universe, and a pedagogical tool to teach “design-thinking” for Linked Data.

Participate by signing up at http://lodlampatterns.org or follow along @lodlamp or #lodlamp.

I’ve continued to play around with this data using OpenRefine and the DBpedia SPARQL endpoint.  Attempting to reconcile against no type, I found approximately 19% of the museums in the IMLS data.

Doing a spot check of the unmatched entities:

• if it is a simply named entity,  it’s not in Wikipedia/DBpedia
• it’s an organization that operates a museum of a different name or has a different legal name than the one it’s known by inDBpedia. e.g:
  •  Fort Morgan State Historic Site -> Fort Morgan (Alabama)
  • Gaineswood Mansion -> Gaineswood
• if it is a complex name (i.e. dirty IMLS data),  it’s not matched
• abbreviations in the name that cannot be matched (e.g. Ntnl (national),  Ctr (center), Hist (history/historical), Inc., etc.) or conjunctions/punctuation e.g. ( & for and).

 Mismatches

Looking at the “matched” items,  I found that lots of generically named museums were matched to a specific museum.  For example, there are many “Art Gallery” things (usually at this or that college) that were all matched to the same DBpedia entity. Likewise, there are about 15 different “Pioneer Museum”, “Museum of Natural History,” “Museum of Anthropology,” “Museum of Art,” “University Museum,” “Cowboy Hall of Fame.”   Another area of mismatch are county/city historical societies where the locality has the same name (i.e 12 different “Douglas County Historical Society” all matched to Douglas County Historical Society in Nebraska.

There are also multiple sites that are maintained by a single entity, like a state museum network or a city.  For example,  The Peale Museum (aka Municipal Museum of Baltimore) and Edgar Allen Poe House and Museum are simply listed here as “City of Baltimore” It was necessary to look up the addresses to see what museum entity was there.

So clearly, a more robust approach to reconciliation is needed, perhaps including the city/state of an entity in order to disambiguate similar names.

Lots of challenges here, but also seems to be lots of opportunities to add to and enrich museum representation in Linked Data/wiki resources.

Didn’t quite get as far as I’d hoped this week.   Following Justin’s comments in the previous post,  I did a quick mapping to the Organization Ontology,  vCard, wsg84 and schema.org (seems to be the only one with a DUNS property).

A sample in Turtle looks like and you can download the full set as Turtle:

@prefix schema: <http://schema.org/> .
@prefix v: <http://www.w3.org/2006/vcard/ns#> .
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
@prefix geo: <http://www.w3.org/2003/01/geo/wgs84_pos#> .
@prefix foaf: <http://xmlns.com/foaf/0.1/> .
@prefix owl: <http://www.w3.org/2002/07/owl#> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .
@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
@prefix org: <http://www.w3.org/ns/org#> .
@prefix skos: <http://www.w3.org/2004/02/skos/core#> .

<http://chi.cci.fsu.edu/resource/museum/imls_0> a org:Organization ;
 skos:prefLabel "Carmel Valley Historical Society" ;
 org:hasSite "http://chi.cci.fsu.edu/resource/site/imls_0" .

<http://chi.cci.fsu.edu/site/imls_0> a org:Site ;
 geo:long "-121.726912" ;
 geo:lat "36.47231" ;
 v:street-address "PO BOX 1427" ;
 v:locality "Carmel Valley" ;
 v:region "CA" ;
 v:postal-code "93924" .

Since I’m not aware of an authorized URI for IMLS resources,  I just minted one for my own domain.  However at this time these don’t resolve to anything.   Ideally I might be able to reconcile many of the city/zip codes to a previously published URI for a place.

I’ll be offline for most of tomorrow, but will try to check in the evening.

What is a Library?

• /organization/local business/library (Schema.org)
• /organization/educational institution/library (DBpedia)
• /place/Architectural Structure/Building/library (DBpedia)
• /architecture/building function/ (Freebase/Wikipedia)
• /library (Freebase)

What is an Archive?

“An archive is an accumulation of historical records, or the physical place they are located.” (Wikipedia)

• notably, Schema.org does not have an explicit class for archives
• /organization/government agency/ (DBpedia)
• /archive  (dbPedia)   although this class doesn’t seem to be associated with other top-level classes.  Wikipedia/dbPedia resources are given as instances of this class.
• /organization/organization_type (Freebase)
• /organization/organization_sector (Freebase)

Best one yet….  /fictional organization type! (Freebase)

What is a Museum?

“A museum is a building or institution dedicated to the acquisition, conservation, study, exhibition, and educational interpretation of objects having scientific, historical, cultural or artistic value.” (Wikipedia).

• /place/civicStructure/Museum (Schema.org)
• /place/Architectural Structure/Building/Museum (DBpedia)
• /architecture/museum (Freebase)

This is only a partial listing of the kinds of thinks a LAM can be according to these linked data sources.  It raises some interesting questions about how to properly model the IMLS data.  What is IMLS’s view on this information?   The emphasis of the total dataset is on location (address, lat/long, Census Block, etc.), so perhaps this is reflected in how LAMs are represented in Linked Data.   I’ll need to go back and revisit my assumptions about LAMs being organizations that are associated with a structure of some kind.

So what?

As we talk about the convergence of LAMs,  the different ways that each sector has been represented as top-level Linked Data classes raises some interesting questions.

• What does this tell us about public (well…LD public) perception of LAMs?  The narrative definitions on Wikipedia pages seem incongruent with the ontological classes. (is a library a collection? an organization? a building?)
• Much of our attention as professionals is directed at representing our collections.  What is our responsibility to ensure that LD concepts reflect our understanding of what we do?
• What is the impact on these specifications on our other Linked Data work?  Given some of the choices that have been made so far,  it could lead to some unexpected inferences:
  • <A Building> <hasCopyright> <An Image>
  • <A Painting> <isPhysicallyLocatedIn> <An Organization>
  • <A Person> <employedBy> <A Place>

In the course of this project I discovered a there is a fundamental ontological difference between how museums and libraries are represented in the current Linked Data cloud.   It was pretty easy to reconcile library entities, because the Public Library Survey data had been ingested into Freebase.  Museums were much more hit-or-miss.  Looking closer that the data, I  realized that libraries were usually represented as a kind of organization, but museums were considered a kind of building.  This may be because much of the information about museums in the U.S. is derived from the National Register of Historic Places dataset that was also ingested into dbPedia/Freebase.

As part of the National Civic Day of Hacking, IMLS has issued the Museum Data Challenge.   Included in the challenge is a minimal set of data on 35,000 museums.   I don’t think I’ll be able to participate directly this weekend, so I thought I’d take a look at the data that IMLS has released and see what I can do to make someone else’s hacking easier this weekend. Also included in the IMLS challenge is the Public Library Service Data (and data from the work of my colleague Christie Koonz,  imaplibraries.org).  Also check out the DPLA Challenge  and the Pocket Archivist Mobile Challenge from NARA.

Goals for the week:

1. Do any clean up needed. (right now the data *looks* pretty clean, but the challenge suggests that their names and geolocation may be faulty).  The fact that much of the LOD about museums is from NRHP might allow me to identify inconsistencies in the IMLS dataset.
2. Convert the CSV data into Linked Data.
   1. Identify appropriate Linked Data properties for this data (see below).
   2. Transform the CSV into JSON-LD
      1. publish on GitHub
3. Associate these representations of museums as organizations with representations of museums as buildings in the current Linked Data cloud.
   1. Submit data to dbPedia/Freebase

Here’s a start on identifying LOD properties for the IMLS data release:

The IMLS data also includes the following fields,  though I haven’t been able to identify any LOD properties for these yet.  This is actually a bit surprising, since you’d think that U.S. Census data (or at least the properties of Census data) would be a solved problem by now.  For the moment, the information above seems like enough of a start, so I’ll leave these aside.

Next up, I’ll discuss in more depth how museums have been modeled in the current Linked Data environment and suggest some possible models for the IMLS dataset.

Next: What is a Library/Archive/Museum According to Linked Data? 

The publication of structured knowledge representations and open data on the Web opens new possibilities for collaboration among humanities researchers and cultural heritage organizations. This course will introduce participants to the core principles of Linked Open Data (LOD), techniques for building and understanding LOD models, how to locate LOD sources for research, tools for manipulating, visualizing, and integrating available data, and best practice methodologies for publicizing and sharing datasets.

For this course I will be drawing from initial work done by the Learning Linked Data project at the University of Washington iSchool, which has laid out a core inventory of learning topics.  The LOD for Libraries, Archives, and Museums community has also been actively promoting access to increasing amounts of cultural heritage information via Linked Data approaches.  Some of the questions we’ll be exploring in the workshop are:

• what does the digital humanities community need from linked data
• what use can we make of these large data sets
• how we can synchronize scholarly work with the larger linked data community.

To help gain momentum for the workshop, I’ve created a wiki, called Linked Data for Humanities where I will be sharing a drafts of the syllabus, resources, and example humanities projects.   (a big hat tip to Mia Ridge and the Museums and the Machine Processable Web wiki, which has been an important resource for the LODLAM community).   If you have a humanities-based Linked Data project,  questions, comments, or recommendations for things the course should cover, please join in the conversation.

Globally PUBLISHED Metadata SHOULD be TRUE, based on the domain knowledge of the publisher.

For many of us coming from a humanities background (especially those of use brought up in the midst of post-modern relativism) the idea of TRUTH can be a tough one to get our heads around.   In the context of RDF’s semantic model, the TRUTH of a triple has a very limited, formal meaning.  Recall that the original purpose of knowledge representation languages such as RDF was to allow inferencing across a set of statements. If you are using RDF in this way,  its semantic models make EVERY EXPRESSED TRIPLE a TRUE statement.    Because of the limitations of artificial reasoning the designers of RDF had to constrain the features of the language.  It is not natively possible to make a FALSE statement in RDF, or to negate a statement (i.e. say something is NOT TRUE).

This is not some abstract sense of TRUTH, but a logical, mathematical kind of TRUTH.  An RDF statement is TRUE within the context of some interpretation that expresses what kinds of statements are allowed. This is the purpose of an RDF Schema or more complex OWL ontology.  The extent that your model conforms to some communal notion of “truth” in the real world is outside both the metadata and your ontology.  It’s a matter for discussions among community members like the ones we’re having now at DCMI. This also gets to Jon’s next point:

PUBLISHERS of metadata MUST publish the semantics of the metadata, or reference publicly available semantics.

Without an expression of your model – what counts as TRUE statements for your metadata – an aggregator is at a loss.  I can look at your metadata and try to supply an interpretations, but only by talking to you will I understand whether I got it right.   In the past we’ve relied on the idea that people should adopt standards, and by adopting standards up-front you are committing to a particular model.  But one of the things that we see is that global standards often don’t fit local needs.   Too often we make practical choices about how to adopt those standards to our local collections.  Unfortunately,  this also means your model is obscured and unavailable to an aggregator. If you SAY you’ve conformed to a standard or a model and you then deviate from the TRUTH of that model, your metadata will look incoherent to non-local consumers of that metadata (see Jon’s other manifesto points with regard to coherence. I understand coherence to mean LOGICALLY coherent, but they may have a lower expectation given some of the vagaries of legacy data syntaxes).

As an aggregator,  it should be my job not to preserve the SYNTAX of your metadata, but to preserve the TRUTH of what your metadata says – even if that means saying it another way that better conforms to MY models.  Ideally how I translate statements optimized for your model into my model should also be publicly shared by aggregation services (and wouldn’t it be nice if were done in a way that other aggregator could pick up and use?).

Jon’s post doesn’t say much about the Open World model alluded to in the title.  But you may already have your back-up about this notion of the truthfulness of metadata statements.  Part of the open world assumption is that we do not have to adopt a single definition of true statements, but rather that we can express multiple models each of which is internally true and coherent. Different models may overlap or be contradictory (i.e. statements that are coherent in one model may be incoherent in another).   Because our debates have become so focused on syntaxes and conformance to a single model (THE STANDARD), I don’t know how we move to a metadata ecology that allows this kind of diversity.   (The CIDOC CRM acknowledges that it seeks to find some common ground among knowledge bases that may contain contradictory statements, since the truth of a particular statement is often the subject of academic and scholarly debate.)

CONSUMERS of published metadata SHOULD assume that the global metadata is locally INVALID, INCOHERENT, and UNTRUE.

I’m certainly onboard with this statement, which is backed up by much of the research conducted on IMLS DCC metadata aggregations.   However, much of this evaluation has used metadata where I’m not sure that anyone was thinking about the logical truth of what they were doing.  Often,  these kinds of analysis are like shooting fish in a barrel (not to say that they analysis themselves are easy…right Wickett?).  We also know that XML, while great for representing document structures, is not necessarily the best at representing statements that lend themselves to evaluation about their truth.

• Wickett, K. and Renear, A.H.  (2012) The logical form of the proposition expressed by a metadata record.  JCDL 2012.  http://dl.acm.org/citation.cfm?doid=2232817.2232917
• Dubin, D., Futrelle, J., Plutchak, J., & Eke, J. (2009). Preserving Meaning, Not Just Objects: Semantics and Digital Preservation. Library Trends, 57(3), 595–609. http://muse.jhu.edu/journals/library_trends/v057/57.3.dubin.html
• Dubin, D., & Birnbaum, D. J. (2004). Interpretation Beyond Markup. Proceedings of Extreme Markup Languages 2004, Montreal, Quebec, August 2004. IDEAlliance and Mulberry Technologies, Inc. Retrieved from http://hdl.handle.net/2142/11838
  Renear, A. H., Dubin, D., Sperberg-McQueen, C. M., & Huitfeldt, C. (2003). XML semantics and digital libraries. Proceedings of the 3rd ACM/IEEE-CS Joint Conference on Digital Libraries. Presented at the JCDL’03, Washington, D.C.: IEEE. Retrieved from: http://dl.acm.org/citation.cfm?id=827192

In the context of DCMI standards,  the Dublin Core Application Profile is an important mechanism with which metadata publishers could use to express their models. Even though DCAP is less formal than, say, an OWL ontology, it allows us to express our intentions for published metadata.  I think DCMI still needs to work on changing the perception of DCAP as something that requires a lot of effort on the part of a community towards understanding DCAP as a way to document “as built” features of a repository that can aid in the aggregation of metadata.  This is especially true for the normal use case where a single repository may have collection-by-collection profiles.

I’d also like to see some more conversations at these higher levels regarding bibliographic representations. Do you believe that a MARC tag/value has some kind of truth to it? What would the model of truth look like?  (how would I evaluate the truthfulness of a MARC record?) How do these notions relate to bibliographic principles, such as the Principle of Accuracy?  This seems especially important in the push to represent RDA as RDF concepts.

For me, this was a dream come true. I’ve wanted to visit KSC ever since I stuffed myself with too many Cheerios in order to get a special Space Shuttle kit in the 80s. Sadly, I never made it to a shuttle launch before the program ended. KSC was high on my to-do list since I moved to Florida, so it was exciting to receive the invitation to the #KSC50 event. The NASASocial team hosted us in the KSC press center for two days of presentations about current NASA programs, especially focusing on the Curiosity mission. We also were able to participate in the first multi-Site NASA social event by joining a live simulcast with MSL scientists and engineers at the Jet Propulsion Lab and other NASA Centers that were hosting similar events). The highlight of the event for me was our tour of early launch sites and getting to go inside the Launch Control Center and Vehicle Assembly Building.

This was my first “social media event” of this type and its given me a lot to ponder. Interestingly, this felt very different than my use of social media during conferences. When I’m at MCN, MW, etc., etc. I have a pretty good idea of who the audience is for my tweets, but here I felt a little spammy. I’m not sure what you all thought of the stream coming at you last week, but I was trying to be somewhat restrained in crossing the streams. I am also a more casual fan of the space program and rank pretty low on the space geek ladder. After arriving, I’d wished I’d done some more reading up about what’s going on at NASA so I could ask our panelists better questions.

I was a little disappointed that we didn’t hear more history during and event cast as a 50th Anniversary celebration. I’m not sure this is a criticism as much as a surprising mismatch of expectations.  We did get to hear from some NASA old-timers who shared some great personal anecdotes about their time at NASA.  We did get a fat copy of This New Ocean, a part of NASA’s historical publications, but little mention was made of NASA’s other historical collections or efforts to document it’s history.  During the event I started tweeting links to oral histories from some of our speakers (Jay Honeycutt, Lee Solid, Roy Tharpe).  As we went around on our bus tour our guide did point out some landmarks, but only provided a little bit of what I’d call interpretation. Throughout the tour I was pulling up information from Wikipedia and other NASA sites about the locations we were visiting (had I thought about it, I should have looked for any dedicated apps related to KSC.  They do seem to have an official app, but the one review doesn’t make it look worth $.99)   I’d be curious to see what kinds of interpretation is offered on the public tour that covers the same area.

My other takeaway from this event, is that I need to work at being social at social events.  I’m usually pretty good in a crowd of people I know, but still shy among strangers.  I sense there was some un-official back channel that I might have tuned into if I’d been a little more aggressive about talking to other attendees.   The organizers seemed to leave this part of being social to us and it has me wondering what impact “icebreakers,” etc. have on these kinds of events.  Compared to my conference experiences, I didn’t see as much direct back-and-forth on Twitter among participants (at least using the NASASocial hashtag).  Again, as a n00b, I may have been missing out on something (and ugh! I could never get the wifi to work right, so I was limited to my phone – regretting my wifi-only iPad this weekend).   From NASA’s perspective, I’m guessing that the events were successful.  The NASASocial tag   trended in the US on both days and seemed to feed the buzz leading up to the landing.

Big thanks to NASASocial for letting me come aboard for this event. It was a great opportunity to learn more about KSC and the MSL Mission.  Since it was my first time participating in an event like this, it also has me thinking hard about how museums can use social media in this way to engage their audiences.  It’s going to provide a great example for my students when we discuss social media and museums later this fall.

(and yes, it’s been a while. Do we really need another “I haven’t been blogging for a while post…I don’t think so!)

In my previous post, I shared an example collection-level record set as RDF. I’ve gone back and simplified this transformation to leave out the representations of institutions and projects. Turns out the URIs that are present will resolve to a vCard RDF representation. e.g. http://imlsdcc.grainger.uiuc.edu/Registry/Institution/?1316 wil return some XML. Maybe not the best representation, but we can work on that as a separate problem. This has the benefit of making the CLD instances simpler. I made a small change that will still associate a project with a funding agency (to demonstrate the contributions they’ve made).

Using the SIMILE Gadget tool, I’ve also extracted unique terms & frequency counts from the CLD records(1). These terms/frequencies are then imported into Google Refine and reconciled against appropriate LOD data:

• cld:itemType:TALIS repository of id.loc.gov for Thesaurus of Graphic Materials terms.
• cld:itemFormat: Freebase Media Type scheme (http://www.freebase.com/schema/type/media_type)
• dcterms:spatial: (http://www.freebase.com/view/location)

Using Freebase has been pretty painless.  When a column of terms is reconciled,  Refine stores the Freebase ID.   To get the Freebase URI,  simply create a “New Column Based on This One” using the following GREL

Using this Freebase URL I can replace the literal statement

into and linked data statement:

Reconciling against id.loc.gov has been more difficult. From my literal values I can create a query string (sometimes) fetch the correct set of triples for a term. This works for most of our terms, though a few uncontrolled terms have been contributed by participants that don’t match. e.g. http://api.talis.com/stores/lcsh-info/items?query=preflabel:photographs&max=1

It is a little sensitive to plural/singular terms. For example the difference between “scrapbooks” and “scrapbook.” Most terms are plural, but there seems to be some distinction I don’t understand between Painting and Paintings.

In Refine I can pull back the RDF for these terms, but am still working how how I might extract the canonical concept URI for each term. This looks like it will require parsing the RDF to get the right URI out of it. If anyone has a good cookbook for reconciling terms with id.loc.gov URIs, I’d love to see it. (something using the Refine ReconciliationServiceAPI would be swell).

I may give our subject headings a twirl, but I may need some subject cataloger help there.  The published LC authorities include headings like “Cemeteries – Recording” but not localized forms “Cemeteries – Recording — Illinois”  Since these are all strings in a dc:subject, some way of parsing the subdivisions is needed.

Update: After posting this, I started playing with my subject headings and found that the LCSH triples were loaded into Freebase in May. (http://www.freebase.com/view/topic/en/loc_subject_headings_full_load). The Refine reconciliation service will pick them by creating a “namespaced” reconciliation service. (point it at the Library of Congress Namespace). Now, let’s get those names & other vocabularies loaded!

(1) I’ve tried to replicate this in Google Refine, but on my computer it seems to choke on the complex XML record structure. It’s quite happy with large tabular representations though.