Category: Uncategorized

One of the outcomes of last week’s LOD-LAM Summit was a draft document proposing a new way to assess the openness/usefulness of linked data for the LAM community. This is a work in progress, but is already provoking interesting debate on our options as we try to create a shared strategy. Here’s what the document looks like today, and we welcome your comments, questions and feedback as we work towards version 1.0.

*******************************************************************

DRAFT

A 4 star classification-scheme for linked open cultural metadata

Publishing openly licensed data on the Web and contributing to the Linked Open Data ecosystem can have a number of benefits for libraries, archives and museums.

1. Driving users to your online content (e.g., by improved search engine optimization);
2. Enabling new scholarship that can only be done with open data;
3. Allowing the creation of new services for discovery;
4. Stimulating collaboration in the library, archives and museums world and beyond.

In order to achieve these benefits libraries, museums and archives are faced with decisions about releasing their metadata under various open terms. To be open and useful as linked data requires deliberate design choices and systems must be built from the beginning with openness and utility in mind. To be useful for third parties, all metadata made available online must be published under a clear rights statement.

This 4-star classification system arranges those rights statements (e.g. licenses or waivers) that comply with the relevant conditions (2-11) of the open knowledge definition (version 1.1) by order of openness and usefulness: the more stars the more open and easier the metadata is to used in a linked data context. Libraries, archives and museums wanting to contribute to the Linked Open Data ecosystem should strive to make their metadata available under the most open instrument that they are comfortable with that maximizes the data’s usefulness to the community..

Note: This system assumes that libraries, archives and museums have the required rights over the metadata to make it available under the waivers and licenses listed below. If the metadata you want to make available includes external data (for example vocabularies) you may be constrained by contract or copyright to release the data under one of the licenses below.

★★★★ Public Domain (CC0 / ODC PDDL / Public Domain Mark)

as a user:

• metadata can be used by anyone for any purpose
• permission to use the metadata is not contingent on anything
• metadata can be combined with any other metadata set (including closed metadata sets)

as a provider:

• you are waiving all rights over your metadata so it can be most easily reused
• you can specify whether and how you would like acknowledgement (attribution or citation, and by what mechanism) from users of your metadata, but it will not be legally binding

This option is considered best since it requires the least action by the user to reuse the data, and to link or integrate the data with other data. It supports the creation of new services by both non-commercial and commercial parties (e.g. search engines), encourages innovation, and maximizes the value of the library, archive or museum’s investment in creating the metadata.

★★★ Attribution License (CC-BY / ODC-BY) when the licensor considers linkbacks to meet the attribution requirement

as a user:

• metadata can be used by anyone for any purpose
• permission to use the metadata is contingent on providing attribution by linkback to the data source
• metadata can be combined with any other metadata set, including closed metadata sets, as long as the attribution link is retained

as a provider:

• you get attribution whenever your data is used

This option meets the definition of openness, but constrains the user of the data by requiring them to provide attribution (in the legal sense, which is not the same as citation in the scholarly sense). Here, attribution is satisfied by a simple, standard Web mechanism from the new data product or service. By using standard practice such as a linkback, attribution is satisfied without requiring the user to discover which attribution method is required and how to implement it for each dataset reused. Note that there are other methods of satisfying a legal attribution requirement (see below) but here we propose a specific mechanism that would minimize the effort needed to use the data if the LAM community collectively agrees to it. Also note that even this simple (ideally shared) attribution method could prevent some applications of linked data if linkbacks are required by many datasets from many sources.

★★ Attribution License (CC-BY / ODC-BY) with another form of attribution

as a user:

• metadata can be used by anyone for any purpose
• permission to use the metadata is contingent on providing attribution in a way specified by the provider
• metadata can be combined with any other metadata set (including closed metadata sets)

as a data provider:

• you get attribution whenever your data is used by the method you specify

This option meets the definition of openness in the same way as the linkback attribution open,  but requires the user to provide attribution is some way other than a linkback, as specified by the data provider. The provider could specify an equally simple mechanism (e.g. by retention of another field, such as ‘creator’ from the original metadata record) or by a more complex mechanism  (e.g. a scholarly citation in a Web page connected to the new data product or service). The disadvantage of this option is that the user must discover what mechanism is wanted by the particular data provider and how to comply with it, potentially needing a different mechanism for each dataset reused. For large-scale open data integration (e.g. mashups) this option is difficult to implement.

★ Attribution Share-Alike License (CC-BY-SA/ODC-ODbL)

as a user:

• metadata can be used by anyone for any purpose

• permission to use the metadata is contingent on providing attribution in a way specified by the provider
• metadata can only be combined with data that allows re-distributions under the terms of this license

as a provider:

• you get attribution whenever your data is used
• you only allow use of your data by entities that also make make their data available for open reuse under exactly the same license

This option meets the definition of openness but potentially limits reuse of data since if more than one dataset is reused and if each dataset has an associated Share-Alike license. Under an Share-Alike license, the only way to legally combine two datasets is if they share exactly the same SA license, since most SA licenses require that reused data be redistributed under exactly same license. If the source datasets had different Share-Alike licenses originally (e.g. CC-BY-SA and ODC-ODbl) then there is no way for the user to comply with the requirements of both source data licenses so this option only allows users to link or integrate data distributed under one particular SA license (or one SA license and any of the other license or waiver options above). In the LAM domain, where significant value is created by combining datasets, the Share-Alike license requirement severely reduces the utility of a dataset.

Related Material

• Open Knowledge Definition
• Principles on Open Bibliographic Data
• Discovery Open Metadata Principles

• Linked Open Data star scheme by example

Following up on this afternoon’s dork shorts, with Tom Baker presenting W3C’s Library Linked Data incubator, and Adrian Pohl telling us about the really useful ckan.net, here’s a graphical rendering of library linked datasets on the CKAN LLD group, courtesy of William Waites:

We plan to keep updating and use this in one of our incubator’s deliverables (draft in progress here, comments welcome as for the main LLD report draft). The idea is to get something closer to our community than the ever growing general LOD cloud.

So, we’ve got a start for the “L” in LAM. But where are As and Ms? C’mon!!!

I guess augmenting such a library graph with the published datasets from museums and archives could be a first work item for a W3C community group on LOD-LAM…

I offered two activities on LOD at ELAG which also may be topic at the LOD-LAM summit:In a bootcamp during pre-conference – mainly led by my colleague Felix Ostrowski – people learned how to enrich webpages providing information about one’s organization with RDFa to make the institution part of the Linked Data web. I also led a small workshop that dealt with open data licensing issues. People interested in opening up their institution’s data discussed the problems they encountered as well as strategies to cope with them. (BTW, I started a list of projects using or publishing open bibliographic data for this workshop. I am happy about hints to relevant projects that aren’t listed there.)

“We, the participants of ELAG 2011, holds these truths to be self-evident, that MARC must die, and that Linked Open Data is the future.”

The LOD-LAM summit is just a few more days away, I’m looking forward to some foggy, cooler weather, since it’s been in the 90s here in Washington.

The meeting should be quite interesting. It will be run in the style of an unconference. The list of attendees is a pretty much world-wide list of great people. There are lots of people that I know (including those from my own personal Wayback machine, yes, you Richard Urban), but even more that I follow on Twitter or elsewere on the InterToobz that I’m excited to meet for the first time in person (I’m calling out Adrian Stevenson, Mia Ridge, Mathias Schindler, Perian Sully, just to pick four at random). You can find the whole list of participants here.

Also there will be a good representative group from the recent Global Interoperability and Linked Data Workshop in Amsterdam (see my reports here and here), including:

• Antoine Isaac
• David Weinberger
• Josh Greenberg
• Rachel Frick
• Paul Keller

My Smithsonian colleague, Effie Kapsalis will be there and that powerhouse from the Powerhouse Museum, Luke Dearnley is coming in from Australia.

Reading Jonathan Rochkind’s musings on using Wikipedia as an authority file (something I’m all for), I was struck by comment that

I think wikipedia-miner, by applying statistical analysis text-mining ‘best guess’ type techniques, provides more relationships than dbpedia alone does. I know that wikipedia-miner’s XML interface is more comprehensible and easily usable by me than dbpedia’s (sorry linked data folks).

XML-over-REST vs. SPARQL debates aside, I think there is an interesting issue here regarding the kind of relationships that statistical text-mining produces vs. the kind typically found in Linked Data. Linked Data favors “factoids” like date-and-place-of-birth, while statistical text-mining produces (at least in this case) distributions interpretable as “relationship strength”. The wikipedia-miner results aren’t “facts” in any normal sense, but as Rochkind suggests they may be more useful. Now sure, you could represent the wikipedia-miner results as Linked Data, but what I’m trying to get at here isn’t a question of data models or syntax. It’s about how and when we choose to treat the patterns in our data as facts, and when we are content to treat them as patterns. Thoughts?

er… Data.  The LOD-LAM Summit will be a great place to explore ideas and collaborations, but we’ll also be looking at concrete and actionable ways to move this field forward in the year to come.  I can’t think of a better venue for teams to form up or solidify plans for the International Digging Into Data Challenge, and I’m hoping there will be some strong Linked Data applicants this year.  If you’re not already working on something, bring your project ideas in search of partners.   Of course, there’s a pretty quick turn around time for getting proposals in by June 16th, but hey, what’s summer without cramming for at least one grant proposal?