Trends and gaps in linked data for archives
“A funny thing happened on the way to the forum.”
Two recent professional meetings have taught me that — when creating some sort of information service — linked data will reside and be mixed with data collected from any number of Internet sites. Linked data interfaces will coexist with REST-ful interfaces, or even things as rudimentary as FTP. To the archivist, this means linked data is not the be-all and end-all of information publishing. There is no such thing. To the application programmer, this means you will need to have experience with a ever-growing number of Internet protocols. To both it means, “There is more than one way to get there.”
In October of 2013 I had the opportunity to attend the Semantic Web In Libraries conference. It was a three-day event attended by approximately three hundred people who could roughly be divided into two equally sized groups: computer scientists and cultural heritage institution employees. The bulk of the presentations fell into two categories: 1) publishing linked data, and 2) creating information services. The publishers talked about ontologies, human-computer interfaces for data creation/maintenance, and systems exposing RDF to the wider world. The people creating information services were invariably collecting, homogenizing, and adding value to data gathered from a diverse set of information services. These information services were not limited to sets of linked data. They also included services accessible via REST-ful computing techniques, OAI-PMH interfaces, and there were probably a few locally developed file transfers or relational database dumps described as well. These people where creating lists of information services, regularly harvesting content from the services, writing cross-walks, locally storing the content, indexing it, providing services against the result, and sometimes republishing any number of “stories” based on the data. For the second group of people, linked data was certainly not the only game in town.
In February of 2014 I had the opportunity to attend a hackathon called GLAM Hack Philly. A wide variety of data sets were presented for “hacking” against. Some where TEI files describing Icelandic manuscripts. Some was linked data published from the British museum. Some was XML describing digitized journals created by a vendor-based application. Some of it resided in proprietary database applications describing the location of houses in Philadelphia. Some of it had little or no computer-readable structure and described plants. Some of it was the wiki mark-up for local municipalities. After the attendees (there were about two dozen of us) learned about each of the data sets we self-selected and hacked away at projects of our own design. The results fell into roughly three categories: geo-referencing objects, creating searchable/browsable interfaces, and data enhancement. With the exception of the hack repurposing journal content into visual art, the results were pretty typical for cultural heritage institutions. But what fascinated me was way us hackers selected our data sets. Namely, the more complete and well-structured the data the more hackers gravitated towards it. Of all the data sets, the TEI files were the most complete, accurate, and computer-readable. Three or four projects were done against the TEI. (Heck, I even hacked on the TEI files.) The linked data from the British Museum — very well structured but not quite as through at the TEI — attracted a large number of hackers who worked together for a common goal. All the other data sets had only one or two people working on them. What is the moral to the story? There are two of them. First, archivists, if you want people to process your data and do “kewl” things against it, then make sure the data is thorough, complete, and computer-readable. Second, computer programmers, you will need to know a variety of data formats. Linked data is not the only game in town.
The technologies described in this Guidebook are not the only way to accomplish the goals of archivists wishing to make their content more accessible. Instead, linked data is just one of many protocols in the toolbox. It is open, standards-based, and simpler rather than more complex. On the other hand, other protocols exist which have a different set of strengths and weaknesses. Computer technologists will need to have a larger rather than smaller knowledge of various Internet tools. For archivists, the core of the problem is still the collection and description of content. This — a what of archival practice — continues to remain constant. It is the how of archival practice — the technology — that changes at a much faster pace.
With great interest I read the Spring/Summer issue of Information Standards Quarterly entitled “Linked Data in Libraries, Archives, and Museums” where there were a number of articles pertaining to linked data in cultural heritage institutions. Of particular interest to me were the loosely enumerated challenges of linked data. Some of them included:
- the apparent Tower Of Babel when it comes to vocabularies used to describe content, and the same time we need to have “ontology mindfulness”.
- dirty, inconsistent, or wide varieties of data integrity
- persistent URIs
- the “chicken & egg” problem of why linked data if there is no killer application
There are a number of challenges in the linked data process. Some of them are listed below, and some of them have been alluded to previously. Create useful linked data, meaning, create linked that links to other linked data. Linked data does not live in a world by itself. Remember, the “l” stands for “linked”. For example, try to include URIs that are the URIs used on other linked data sets. Sometimes this is not possible, for example, with the names of people in archival materials. When possible, they used VIAF, but other times they needed to create their own URI denoting an individual. There is a level of rigor involved in creating the data model, and there may be many discussions regarding semantics. For example, what is a creator? Or, when is a term intended to be an index term as opposed to a reference. When does one term in one vocabulary equal a different term in a different vocabulary? Balance the creation of your own vocabulary with the need to speak the language of others using their vocabulary. Consider “fixing” the data as it comes in or goes out because it might not be consistent nor thorough. Provenance is an issue. People — especially scholars — will want to know where the linked data came from and whether or not it is authoritative. How to solve or address this problem? The jury is still out on this one. Creating and maintaining linked data is difficult because it requires the skills of a number of different types of people. Computer programmers. Database designers. Subject experts. Metadata specialists. Archivists. Etc. A team is all but necessary.
Linked data represents a modern way of making your archival descriptions accessible to the wider world. In that light, it represents a different way of doing things but not necessary a different what of doing things. You will still be doing inventory. You will still be curating collections. You will still be prioritizing what goes and what stays.
Linked data makes a lot of sense, but there are some personnel and technological gaps needing to be filled before it can really and truly be widely adopted by archives (or libraries or museums). They include but are not limited to: hands-on training, “string2URI” tools, database to RDF interfaces, mass RDF editors, and maybe “killer applications”.
Different people learn in different ways, and hands-on training on what linked data is and how it can be put into practice would go a long way towards the adoption of linked data in archives. These hands-on sessions could be as short as an hour or as long as one or two days. They would include a mixture of conceptual and technological topics. For example, there could be a tutorial on how to search RDF triple stores using SPARQL. Another tutorial would compare & contrast the data models of databases with the RDF data model. A class could be facilitated on how to transform XML files (MARCXML, MODS, EAD) to any number of RDF serializations and publish the result on a Web server. There could be a class on how to design URIs. A class on how to literally draw an RDF ontology would be a good idea. Another class would instruct people on how to formally read & write an ontology using OWL. Yet another hands-on workshop would demonstrate to participants the techniques for creating, maintaining, and publishing an RDF triple store. Etc. Linked data might be a “good thing”, but people are going to need to learn how to work more directly with it. These hands-on trainings could be aligned with hack-a-thons, hack-fests, or THATCamps so a mixture of archivists, metadata specialists, and computer programmers would be in the same spaces at the same times.
There is a need for tools enabling people and computers to automatically associate string literals with URIs. If nobody (or relatively few people) share URIs across their published linked data, then the promises of linked data won’t come to fruition. Archivists (and librarians and people who work in museums) take things like controlled vocabularies and name authority lists very seriously. Identifying the “best” URI for a given thing, subject term, or personal name is something the profession is going to want to do and do well.
Fabian Steeg and Pascal Christoph at the 2013 Semantic Web in Libraries conference asked the question, “How can we benefit from linked data without being linked data experts?” Their solution was the creation of a set of tools enabling people to query a remote service and get back a list of URIs which were automatically inserted into a text. This is an example of a “string2URI” tool that needs to be written and widely adopted. These tools could be as simple as a one-box, one-button interface where a person enters a word for phrase and one or more URIs are returned for selection. A slightly more complicated version would include a drop-down menu allowing the person to select places to query for the URI. Another application suggested by quite a number of people would use natural language to first extract named entities (people, places, things, etc.) from texts (like abstracts, scope notes, biographical histories, etc.). Once these entities were extracted, they would then be fed to string2URI. The LC Linked Data Service, VIAF, and Worldcat are very good examples of string2URI tools. The profession needs more of them. SNAC’s use of EAC-CPF is a something to watch in this space.
Database to RDF publishing systems
There are distinct advantages and disadvantages of the current ways of creating and maintaining the descriptions of archival collections. They fill a particular purpose and function. Nobody is going to suddenly abandon well-known techniques for ones seemingly unproven. Consequenlty, there is a need to easily migrate existing data to RDF. One way towards this goal is to transform or export archival descriptions from their current containers to RDF. D2RQ could go a long way towards publishing the underlying databases of PastPerfect, Archon, Archivist’s Toolkit, or ArchivesSpace as RDF. A seemingly little used database to RDF modeling language — R2RML — could be used for similar purposes. These particular solutions are rather generic. Either a great deal of customizations needs to be done using D2RQ, or new interfaces to the underlying databases need to be created. Regarding the later, this will require a large amount of specialized work. An ontology & vocabulary would need to be designed or selected. The data and the structure of the underlying databases would need to be closely examined. A programmer would need to write reports against the database to export RDF and publish it in one form or another. Be forewarned. Software, like archival description, is never done. On the other hand, this sort of work could be done once and then shared with the wider archival community and then applied to local implementations of Archivist’s Toolkit, ArchivesSpace, etc.
Mass RDF editors
Archivists curate physical collections as well as descriptions of those collections. Ideally, the descriptions would reside in a triple store as if it were a database. The store would be indexed. Queries could be applied against the store. Create, read, update, and delete operations could be easily done. As RDF is amassed it will almost definitely need to be massaged and improved. URIs may need to be equated. Controlled vocabulary terms may need to be related. Supplementary statements may need to be asserted enhancing the overall value of the store. String literals may need to be normalized or even added. This work will not be done on a one-by-one statement-by-statement basis. There are simply too many triples — 100′s of thousands, if not millions of them. Some sort of mass RDF editor will need to be created. If the store was well managed, and if a peson was well-versed in SPARQL, then much of this work could be done through SPARQL statements. But SPARQL is not for the faint of heart, and despite what some people say, it is not easy to write. Tools will need to be created — akin to the tools described by Diane Hillman and articulated through the experience with the National Science Foundation Digital Library — making it easy to do large-scale additions and updates to RDF triple stores.
To some degree, the idea of the Semantic Web and linked data has been oversold. We were told, “Make very large sets of RDF freely available and new relationships between resources will be discovered.” The whole thing smacks of artificial intelligence which simultaneously scares people and makes them laugh out loud. On the other hand, a close reading of Allemang’s and Hendler’s book Semantic Web For The Working Ontologist describes exactly how and why these new relationships can be discovered, but these discoveries do take some work and a significant volume of RDF from a diverse set of domains.
So maybe the “killer” application is not so much a sophisticated super-brained inference engine but something less sublime. A number of examples come to mind. Begin by recreating the traditional finding aid. Transform an EAD file into serialized RDF, and from the RDF create a finding aid. This is rather mundane and redundant but it will demonstrate and support a service model going forward. Go three steps further. First, create a finding aid, but supplement it with data and information from your other finding aids. Your collections do not exist in silos nor isolation. Supplement these second-generation finding aids with images, geographic coordinates, links to scholarly articles, or more thorough textual notes all discovered from outside linked data sources. Third, establish relationships between your archival collections and the archival collections outside your institution. Again, relationships could be created between collections and between items in the collections. These sorts of “killer” applications enables the archivist to stretch the definition of finding aid.
Another “killer” application may be a sort of union catalog. Each example of the union catalog will have some sort of common domain. The domain could be topical: Catholic studies, civil war history, the papers of a particular individual or organization. Collect the RDF from archives of these domains, put it into a single triple store, clean up and enhance the RDF, index it, and provide a search engine against the index. The domain could be regional. For example, the RDF from an archive, library, and museum of an individual college or university could be created, amalgamated, and presented. The domain could be professional: all archives, all libraries, or all museums.
Another killer application, especially in an academic environment, would be the integration of archival description into course management systems. Manifest archival descriptions as RDF. Manifest course offerings across the academy in the form of RDF. Manifest student and instrutor information as RDF. Discover and provide links between archival content and people in specific classes. This sort of application will make archival collections much more relevant to the local population.
Tell stories. Don’t just provide links. People want answers as much as they want lists of references. After search queries are sent to indexes, provide search results in the form of lists of links, but also mash together information from search results into a “named graph” that includes an overview of the apparent subject queried, images of the subject, a depiction of where the subject is located, and few very relevant and well-respected links to narrative descriptions of the subject. You can see these sorts of enhancement in the results of many Google and Facebook search results.
Support the work of digital humanists. Amass RDF. Clean, normalize, and enhance it. Provide access to it via searchable and browsable interfaces. Provide additional services against the results such as timelines built from the underlying dates found in the RDF. Create word clouds based on statistically significant entities such as names of people or places or themes. Provide access to search results in the form of delimited files so the data can be imported into other tools for more sophisticated analysis. For example, support a search results to Omeka interface. For that matter, create an Omeka to RDF service.
The “killer” application for linked data is only as far away as your imagination. If you can articulate it, then it can probably be created.
Linked data changes the way your descriptions get expressed and distributed. It is a lot like taking a trip across country. The goal was always to get to the coast to see the ocean, but instead of walking, going by stage coach, taking a train, or driving a car, you will be flying. Along the way you may visit a few cities and have a few layovers. Bad weather may even get in the way, but sooner or later you will get to your destination. Take a deep breath. Understand that the process will be one of learning, and that learning will be applicable in other aspects of your work. The result will be two-fold. First, a greater number of people will have access to your collections, and consequently, more people will will be using your collections.