Remembering Aaron Swartz

“One of the things the Web teaches us is that everything is connected (hyperlinks) and we all should work together (standards). Too often school teaches us that everything is separate (many different ‘subjects’) and that we should all work alone.” –Aaron Swartz, April 2001.

So Aaron is gone. We were friends a decade ago, and drifted out of touch; I thought we’d cross paths again, but, well, no.

Update: MIT’s report is published.

 I’ll remember him always as the bright kid who showed up in the early data sharing Web communities around RSS, FOAF and W3C’s RDF, a dozen years ago:

"Hello everyone, I'm Aaron. I'm not _that_ much of a coder, (and I don't know
much Perl) but I do think what you're doing is pretty cool, so I thought I'd
hang out here and follow along (and probably pester a bit)."

Aaron was from the beginning a powerful combination of smart, creative, collaborative and idealistic, and was drawn to groups of developers and activists who shared his passion for what the Web could become. He joined and helped the RSS 1.0 and W3C RDF groups, and more often than not the difference in years didn’t make a difference. I’ve seen far more childishness from adults in the standards scene, than I ever saw from young Aaron. TimBL has it right; “we have lost one of our own”. He was something special that ‘child genius’ doesn’t come close to capturing. Aaron was a regular in the early ’24×7 hack-and-chat’ RDF IRC scene, and it’s fitting that the first lines logged in that group’s archives are from him.

I can’t help but picture an alternate and fairer universe in which Aaron made it through and got to be the cranky old geezer at conferences in the distant shiny future. He’d have made a great William Loughborough; a mutual friend and collaborator with whom he shared a tireless impatience at the pace of progress, the need to ask ‘when?’, to always Demand Progress.

I’ve been reading old IRC chat logs from 2001. Within months of his ‘I’m not _that_ much of a coder’ Aaron was writing Python code for accessing experimental RDF query services (and teaching me how to do it, disclaiming credit, ‘However you like is fine… I don’t really care.’). He was writing rules in TimBL’s experimental logic language N3, applying this to modelling corporate ownership structures rather than as an academic exercise, and as ever sharing what he knew by writing about his work in the Web. Reading some old chats, we talked about the difficulties of distributed collaboration, debate and disagreement, personalities and their clashes, working groups, and the Web.

I thought about sharing some of that, but I’d rather just share him as I choose to remember him:

22:16:58 <AaronSw> LOL

Schema.org and One Hundred Years of Search

A talk from London SemWeb meetup hosted by the BBC Academy in London, Mar 30 2012….

Slides and video are already in the Web, but I wanted to post this as an excuse to plug the new Web History Community Group that Max and I have just started at W3C. The talk was part of the Libraries, Media and the Semantic Web meetup hosted by the BBC in March. It gave an opportunity to run through some forgotten history, linking Paul Otlet, the Universal Decimal Classification, schema.org and some 100 year old search logs from Otlet’s Mundaneum. Having worked with the BBC Lonclass system (a descendant of Otlet’s UDC), and collaborated with the Aida Slavic of the UDC on their publication of Linked Data, I was happy to be given the chance to try to spell out these hidden connections. It also turned out that Google colleagues have been working to support the Mundaneum and the memory of this early work, and I’m happy that the talk led to discussions with both the Mundaneum and Computer History Museum about the new Web History group at W3C.

So, everything’s connected. Many thanks to W. Boyd Rayward (Otlet’s biographer) for sharing the ancient logs that inspired the talk (see slides/video for a few more details). I hope we can find more such things to share in the Web History group, because the history of the Web didn’t begin with the Web…

Linked Literature, Linked TV – Everything Looks like a Graph

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Ben Fry in ‘Visualizing Data‘:

Graphs can be a powerful way to represent relationships between data, but they are also a very abstract concept, which means that they run the danger of meaning something only to the creator of the graph. Often, simply showing the structure of the data says very little about what it actually means, even though it’s a perfectly accurate means of representing the data. Everything looks like a graph, but almost nothing should ever be drawn as one.

There is a tendency when using graphs to become smitten with one’s own data. Even though a graph of a few hundred nodes quickly becomes unreadable, it is often satisfying for the creator because the resulting figure is elegant and complex and may be subjectively beautiful, and the notion that the creator’s data is “complex” fits just fine with the creator’s own interpretation of it. Graphs have a tendency of making a data set look sophisticated and important, without having solved the problem of enlightening the viewer.

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Ben Fry is entirely correct.

I suggest two excuses for this indulgence: if the visuals are meaningful only to the creator of the graph, then let’s make everyone a graph curator. And if the things the data attempts to describe — for example, 14 million books and the world they in turn describe — are complex and beautiful and under-appreciated in their complexity and interconnectedness, … then perhaps it is ok to indulge ourselves. When do graphs become maps?

I report here on some experiments that stem from two collaborations around Linked Data. All the visuals in the post are views of bibliographic data, based on similarity measures derrived from book / subject keyword associations, with visualization and a little additional analysis using Gephi. Click-through to Flickr to see larger versions of any image. You can’t always see the inter-node links, but the presentation is based on graph layout tools.

Firstly, in my ongoing work in the NoTube project, we have been working with TV-related data, ranging from ‘social Web’ activity streams, user profiles, TV archive catalogues and classification systems like Lonclass. Secondly, over the summer I have been working with the Library Innovation Lab at Harvard, looking at ways of opening up bibliographic catalogues to the Web as Linked Data, and at ways of cross-linking Web materials (e.g. video materials) to a Webbified notion of ‘bookshelf‘.

In NoTube we have been making use of the Apache Mahout toolkit, which provided us with software for collaborative filtering recommendations, clustering and automatic classification. We’ve barely scratched the surface of what it can do, but here show some initial results applying Mahout to a 100,000 record subset of Harvard’s 14 million entry catalogue. Mahout is built to scale, and the experiments here use datasets that are tiny from Mahout’s perspective.

gothic_idol

In NoTube, we used Mahout to compute similarity measures between each pair of items in a catalogue of BBC TV programmes for which we had privileged access to subjective viewer ratings. This was a sparse matrix of around 20,000 viewers, 12,500 broadcast items, with around 1.2 million ratings linking viewer to item. From these, after a few rather-too-casual tests using Mahout’s evaluation measure system, we picked its most promising similarity measure for our data (LogLikelihoodSimilarity or Tanimoto), and then for the most similar items, simply dumped out a huge data file that contained pairs of item numbers, plus a weight.

There are many many smarter things we could’ve tried, but in the spirit of ‘minimal viable product‘, we didn’t try them yet. These include making use of additional metadata published by the BBC in RDF, so we can help out Mahout by letting it know that when Alice loves item_62 and Bob loves item_82127, we also via RDF also knew that they are both in the same TV series and Brand. Why use fancy machine learning to rediscover things we already know, and that have been shared in the Web as data? We could make smarter use of metadata here. Secondly we could have used data-derrived or publisher-supplied metadata to explore whether different Mahout techniques work better for different segments of the content (factual vs fiction) or even, as we have also some demographic data, different groups of users.

markets

Anyway, Mahout gave us item-to-item similarity measures for TV. Libby has written already about how we used these in ‘second screen’ (or ‘N-th’ screen, aka N-Screen) prototypes showing the impact that new Web standards might make on tired and outdated notions of “TV remote control”.

What if your remote control could personalise a view of some content collection? What if it could show you similar things based on your viewing behavior, and that of others? What if you could explore the ever-growing space of TV content using simple drag-and-drop metaphors, sending items to your TV or to your friends with simple tablet-based interfaces?

medieval_society

So that’s what we’ve been up to in NoTube. There are prototypes using BBC content (sadly not viewable by everyone due to rights restrictions), but also some experiments with TV materials from the Internet Archive, and some explorations that look at TED’s video collection as an example of Web-based content that (via ted.com and YouTube) are more generally viewable. Since every item in the BBC’s Archive is catalogued using a library-based classification system (Lonclass, itself based on UDC) the topic of cross-referencing books and TV has cropped up a few times.

new_colonialism

Meanwhile, in (the digital Public Library of) America, … the Harvard Library Innovation Lab team have a huge and fantastic dataset describing 14 million bibliographic records. I’m not sure exactly how many are ‘books'; libraries hold all kinds of objects these days. With the Harvard folk I’ve been trying to help figure out how we could cross-reference their records with other “Webby” sources, such as online video materials. Again using TED as an example, because it is high quality but with very different metadata from the library records. So we’ve been looking at various tricks and techniques that could help us associate book records with those. So for example, we can find tags for their videos on the TED site, but also on delicious, and on youtube. However taggers and librarians tend to describe things quite differently. Tags like “todo”, “inspirational”, “design”, “development” or “science” don’t help us pin-point the exact library shelf where a viewer might go to read more on the topic. Or conversely, they don’t help the library sites understand where within their online catalogues they could embed useful and engaging “related link” pointers off to TED.com or YouTube.

So we turned to other sources. Matching TED speaker names against Wikipedia allows us to find more information about many TED speakers. For example the Tim Berners-Lee entry, which in its Linked Data form helpfully tells us that this TED speaker is in the categories ‘Japan_Prize_laureates’, ‘English_inventors’, ‘1955_births’, ‘Internet_pioneers’. All good to know, but it’s hard to tell which categories tell us most about our speaker or video. At least now we’re in the Linked Data space, we can navigate around to Freebase, VIAF and a growing Web of data-sources. It should be possible at least to associate TimBL’s TED talks with library records for his book (so we annotate one bibliographic entry, from 14 million! …can’t we map areas, not items?).

tv

Can we do better? What if we also associated Tim’s two TED talk videos with other things in the library that had the same subject classifications or keywords as his book? What if we could build links between the two collections based not only on published authorship, but on topical information (tags, full text analysis of TED talk transcripts). Can we plan for a world where libraries have access not only to MARC records, but also full text of each of millions of books?

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I’ve been exploring some of these ideas with David Weinberger, Paul Deschner and Matt Phillips at Harvard, and in NoTube with Libby Miller, Vicky Buser and others.

edu

Yesterday I took the time to make some visual sanity check of the bibliographic data as processed into a ‘similarity space’ in some Mahout experiments. This is a messy first pass at everything, but I figured it is better to blog something and look for collaborations and feedback, than to chase perfection. For me, the big story is in linking TV materials to the gigantic back-story of context, discussion and debate curated by the world’s libraries. If we can imagine a view of our TV content catalogues, and our libraries, as visual maps, with items clustered by similarity, then NoTube has shown that we can build these into the smartphones and tablets that are increasingly being used as TV remote controls.

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And if the device you’re using to pause/play/stop or rewind your TV also has access to these vast archives as they open up as Linked Data (as well as GPS location data and your Facebook password), all kinds of possibilities arise for linked, annotated and fact-checked TV, as well as for showing a path for libraries to continue to serve as maps of the entertainment, intellectual and scientific terrain around us.

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A brief technical description. Everything you see here was made with Gephi, Mahout and experimental data from the Library Innovation Lab at Harvard, plus a few scripts to glue it all together.

Mahout was given 100,000 extracts from the Harvard collection. Just main and sub-title, a local ID, and a list of topical phrases (mostly drawn from Library of Congress Subject Headings, with some local extensions). I don’t do anything clever with these or their sub-structure or their library-documented inter-relationships. They are treated as atomic codes, and flattened into long pseudo-words such as ‘occupational_diseases_prevention_control’ or ‘french_literature_16th_century_history_and_criticism’,
‘motion_pictures_political_aspects’, ‘songs_high_voice_with_lute’, ‘dance_music_czechoslovakia’, ‘communism_and_culture_soviet_union’. All of human life is there.

David Weinberger has been calling this gigantic scope our problem of the ‘Taxonomy of Everything’, and the label fits. By mushing phrases into fake words, I get to re-use some Mahout tools and avoid writing code. The result is a matrix of 100,000 bibliographic entities, by 27684 unique topical codes. Initially I made the simple test of feeding this as input to Mahout’s K-Means clustering implementation. Manually inspecting the most popular topical codes for each cluster (both where k=12 to put all books in 12 clusters, or k=1000 for more fine-grained groupings), I was impressed by the initial results.

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I only have these in crude text-file form. See hv/_k1000.txt and hv/_twelve.txt (plus dictionary, see big file
_harv_dict.txt ).

For example, in the 1000-cluster version, we get: ‘medical_policy_united_states’, ‘health_care_reform_united_states’, ‘health_policy_united_states’, ‘medical_care_united_states’,
‘delivery_of_health_care_united_states’, ‘medical_economics_united_states’, ‘politics_united_states’, ‘health_services_accessibility_united_states’, ‘insurance_health_united_states’, ‘economics_medical_united_states’.

Or another cluster: ‘brain_physiology’, ‘biological_rhythms’, ‘oscillations’.

How about: ‘museums_collection_management’, ‘museums_history’, ‘archives’, ‘museums_acquisitions’, ‘collectors_and_collecting_history’?

Another, conceptually nearby (but that proximity isn’t visible through this simple clustering approach), ‘art_thefts’, ‘theft_from_museums’, ‘archaeological_thefts’, ‘art_museums’, ‘cultural_property_protection_law_and_legislation’, …

Ok, I am cherry picking. There is some nonsense in there too, but suprisingly little. And probably some associations that might cause offense. But it shows that the tooling is capable (by looking at book/topic associations) at picking out similarities that are significant. Maybe all of this is also available in LCSH SKOS form already, but I doubt it. (A side-goal here is to publish these clusters for re-use elsewhere…).

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So, what if we take this, and instead compute (a bit like we did in NoTube from ratings data) similarity measures between books?

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I tried that, without using much of Mahout’s sophistication. I used its ‘rowsimilarityjob’ facility and generated similarity measures for each book, then threw out most of the similarities except the top 5, later the top 3, from each book. From this point, I moved things over into the Gephi toolkit (“photoshop for graphs”), as I wanted to see how things looked.

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First results shown here. Nodes are books, links are strong similarity measures. Node labels are titles, or sometimes title + subtitle. Some (the black-background ones) use Gephi’s “modularity detection” analysis of the link graph. Others (white background) I imported the 1000 clusters from the earlier Mahout experiments. I tried various of the metrics in Gephi and mapped these to node size. This might fairly be called ‘playing around’ at this stage, but there is at least a pipeline from raw data (eventually Linked Data I hope) through Mahout to Gephi and some visual maps of literature.

1k_overview

What does all this show?

That if we can find a way to open up bibliographic datasets, there are solid opensource tools out there that can give new ways of exploring the items described in the data. That those tools (e.g. Mahout, Gephi) provide many different ways of computing similarity, clustering, and presenting. There is no single ‘right answer’ for how to present literature or TV archive content as a visual map, clustering “like with like”, or arranging neighbourhoods. And there is also no restriction that we must work dataset-by-dataset, either. Why not use what we know from movie/TV recommendations to arrange the similarity space for books? Or vice-versa?

I must emphasise (to return to Ben Fry’s opening remark) that this is a proof-of-concept. It shows some potential, but it is neither a user interface, nor particularly informative. Gephi as a tool for making such visualizations is powerful, but it too is not a viable interface for navigating TV content. However these tools do give us a glimpse of what is hidden in giant and dull-sounding databases, and some hints for how patterns extracted from these collections could help guide us through literature, TV or more.

Next steps? There are many things that could be tried; more than I could attempt. I’d like to get some variant of these 2D maps onto ipad/android tablets, loaded with TV content. I’d like to continue exploring the bridges between content (eg. TED) and library materials, on tablets and PCs. I’d like to look at Mahout’s “collocated terms” extraction tools in more details. These allow us to pull out recurring phrases (e.g. “Zero Sum”, “climate change”, “golden rule”, “high school”, “black holes” were found in TED transcripts). I’ve also tried extracting bi-gram phrases from book titles using the same utility. Such tools offer some prospect of bulk-creating links not just between single items in collections, but between neighbourhood regions in maps such as those shown here. The cross-links will never be perfect, but then what’s a little serendipity between friends?

As full text access to book data looms, and TV archives are finding their way online, we’ll need to find ways of combining user interface, bibliographic and data science skills if we’re really going to make the most of the treasures that are being shared in the Web. Since I’ve only fragments of each, I’m always drawn back to think of this in terms of collaborative work.

A few years ago, Netflix had the vision and cash to pretty much buy the attention of the entire machine learning community for a measly million dollars. Researchers love to have substantive datasets to work with, and the (now retracted) Netflix dataset is still widely sought after. Without a budget to match Netflix’, could we still somehow offer prizes to help get such attention directed towards analysis and exploitation of linked TV and library data? We could offer free access to the world’s literature via a global network of libraries? Except everyone gets that for free already. Maybe we don’t need prizes.

Nearby in the Web: NoTube N-Screen, Flickr slideshow

XMPP untethered – serverless messaging in the core?

In the XMPP session at last february’s FOSDEM I gave a brief demo of some NoTube work on how TV-style remote controls might look with XMPP providing their communication link. For the TV part, I showed Boxee, with a tiny Python script exposing some of its localhost HTTP API to the wider network via XMPP. For the client, I have a ‘my first iphone app‘ approximation of a remote control that speaks a vapourware XMPP remote control protocol, “Buttons”.

The point of all this is about breaking open the Web-TV environment, so that different people and groups get to innovate without having to be colleagues or close-nit business partners. Control your Apple TV with your Google Android phone; or your Google TV with your Apple iPad, or your Boxee box with either. Write smart linking and bookmarking and annotation apps that improve TV for all viewers, rather than only those who’ve bought from the same company as you. I guess I managed to communicate something of this because people clapped generously when my iphone app managed to pause Boxee. This post is about how we might get from evocative but toy demos to a useful and usable protocol, and about one of our largest obstacles: XMPP’s focus on server-mediated communications.

So what happened when I hit the ‘pause’ button on the iphone remote app? Well, the app was already connected to the XMPP network, e.g. signed in as bob.notube@gmail.com via Google Talk’s servers. And so an XMPP stanza flowed out from the room we were in, across to Google somewhere, and then via XMPP server-to-server protocol over to my self-run XMPP server (an ejabberd hosted on Amazon EC2’s east USA zone somewhere). And from there, the message returned finally to Brussels, flowing through whichever Python library I was using to Boxee (signed in as buttons@foaf.tv), causing the video to pause. This happened quite quickly, and generally very quickly; but sometimes it can take more than a second. This can be very frustrating, and while there are workaround (keep-alive messages, smart code that ignores sequences of buffered ‘Pause!’ messages, apps that download metadata and bring more UI to the second screen, …), the problem has a simple cause: it just doesn’t make sense for a ‘pause’ message to cross the atlantic twice, and pass through two XMPP servers, on its the way across the living room from remote control to TV.

But first – why are we even using XMPP at all, rather than say HTTP? Partly because XMPP lets us easily address devices on home networks, that aren’t publically exposed as running a Web server. Partly for the symmetry of the protocol, since ipads, touch tables, smart phones, TVs and media centres all can host and play media items on their own displays, and we may have several such devices in a home setting that need to be in touch with one another. There’s also a certain lazyness; XMPP already defines lots of useful pieces, like buddylist rosters, pubsub notifications, group chats; it has an active and friendly community, and it comes with a healthy collection of tools and libraries. My own interests are around exploring and collectively annotating the huge archives of content that are slowly coming online, and an expectation that this could be a more shared experience, so I’m following an intuition that XMPP provides more useful ‘raw materials’ for social content exploration than raw HTTP. That said, many elements of remote control can be defined and implemented in either environment. But for today, I’m concentrating on the XMPP side.

So back at FOSDEM I raised a couple of concerns, as a long-term XMPP well-wisher but non-insider.

The first was that the technology presents itself as a daunting collection of extensions, each of which might or might not be supported in some toolkit. To this someone (likely Dave Cridland) responded with the reassuring observation that most of these could be implemented by 3rd party app developer simply reading/writing XMPP stanzas. And that in fact pretty much the only ‘core’ piece of XMPP that wasn’t treated as core in most toolkits was the serverless, point-to-point XEP-0174 ‘serverless messaging‘ mode. Everything else, the rest of us mortals could hack in application code. For serverless messaging we are left waiting and hoping for the toolkit maintainers to wire things in, as it generally requires fairly intimate knowledge of the relevant XMPP library.

My second point was in fact related: that if XMPP tools offered better support for serverless operation, then it would open up lots of interesting application options. That we certainly need it for the TV remotes use case to be a credible use of XMPP. Beyond TV remotes, there are obvious applications in the area of open, decentralised social networking. The recent buzz around things like StatusNet, GNU Social, Diaspora*, WebID, OneSocialWeb, alongside the old stuff like FOAF, shows serious interest in letting users take more decentralised control of their online social behaviour. Whether the two parties are in the same room on the same LAN, or halfway around the world from each other, XMPP and its huge collection of field-tested, code-supported extensions is relevant, even when those parties prefer to communicate directly rather than via servers.

With XMPP, app party developers have a well-defined framework into which they can drop ad-hoc stanzas of information; whether it’s a vCard or details of recently played music. This seems too useful a system to reserve solely for communications that are mediated by a server. And indeed, XMPP in theory is not tied to servers; the XEP-0174 spec tells us both how to do local-network bonjour-style discovery, and how to layer XMPP on top of any communication channel that allows XML stanzas to flow back and forth.

From the abstract,

This specification defines how to communicate over local or wide-area networks using the principles of zero-configuration networking for endpoint discovery and the syntax of XML streams and XMPP messaging for real-time communication. This method uses DNS-based Service Discovery and Multicast DNS to discover entities that support the protocol, including their IP addresses and preferred ports. Any two entities can then negotiate a serverless connection using XML streams in order to exchange XMPP message and IQ stanzas.

But somehow this remains a niche use of XMPP. Many of the toolkits have some support for it, perhaps as work-in-progress or a patch, but it remains somewhat ‘out there’ rather than core to the XMPP approach. I’d love to see this change in 2011. The 0174 spec combines a few themes; it talks a lot about discovery, motivated in part by trade-fair and conference type scenarios. When your Apple laptop finds people locally on some network to chat with by “Bonjour”, it’s doing more or less XEP-0174. For the TV remote scenario, I’m interested in having nodes from a normal XMPP network drop down and “re-discover” themselves in a hopefully-lower-latency point to point mode (within some LAN or across the Internet, or between NAT-protected home LANs). There are lots of scenarios when having a server in the loop isn’t needed, or adds cost and risk (latency, single point of failure, privacy concerns).

XEP-0174 continues,

6. Initiating an XML Stream
In order to exchange serverless messages, the initiator and
recipient MUST first establish XML streams between themselves,
as is familiar from RFC 3920.
First, the initiator opens a TCP connection at the IP address
and port discovered via the DNS lookup for an entity and opens
an XML stream to the recipient, which SHOULD include 'to' and
'from' address. [...]

This sounds pretty precise; point-to-point communication is over TCP.  The Security Considerations section discussed some of the different constraints for XMPP in serverless mode, and states that …

To secure communications between serverless entities, it is RECOMMENDED to negotiate the use of TLS and SASL for the XML stream as described in RFC 3920

Having stumbled across Datagram TLS (wikipedia, design writeup), I wonder whether that might also be an option for the layer providing the XML stream between entities.  For example, the chownat tool shows a UDP-based trick for establishing bidirectional communication between entities, even when they’re both behind NAT. I can’t help but wonder whether XMPP could be layered somehow on top of that (OpenSSL libraries have Datagram TLS support already, apparently). There are also other mechanisms I’ve been discussing with Mo McRoberts and Libby Miller lately, e.g. Mo’s dynamic dns / pubkeys idea, or his trick of running an XMPP server in the home, and opening it up via UPnP. But that’s for another time.

So back on my main theme: XMPP is holding itself back by always emphasising the server-mediated role. XEP-0174 has the feel of an afterthought rather than a core part of what the XMPP community offers to the wider technology scene, and the support for it in toolkits lags similarly. I’d love to hear from ‘live and breath XMPP’ folk what exactly they think is needed before it can become a more central part of the XMPP world.

From the TV remotes use case we have a few constraints, such as the need to associate identities established in different environments (eg. via public key). If xmpp:danbri-ipad@danbri.org is already on the server-based XMPP roster of xmpp:nevali-tv@nevali.net, can pubkey info in their XMPP vCards be used to help re-establish trusted communications when the devices find themselves connected in the same LAN? It seems just plain nuts to have a remote control communicate with another box in the same room via transatlantic links through Google Talk and Amazon EC2, and yet that’s the general pattern of normal XMPP communications. What would it take to have more out-of-the-box support for XEP-0174 from the XMPP toolkits? Some combination of beer, money, or a shared sense that this is worth doing and that XMPP has huge potential beyond the server-based communications model it grew from?

Lonclass and RDF

Lonclass is one of the BBC’s in-house classification systems – the “London classification”. I’ve had the privilege of investigating lonclass within the NoTube project. It’s not currently public, but much of what I say here is also applicable to the Universal Decimal Classification (UDC) system upon which it was based. UDC is also not fully public yet; I’ve made a case elsewhere that it should be, and I hope we’ll see that within my lifetime. UDC and Lonclass have a fascinating history and are rich cultural heritage artifacts in their own right, but I’m concerned here only with their role as the keys to many of our digital and real-world archives.

Why would we want to map Lonclass or UDC subject classification codes into RDF?

Lonclass codes can be thought of as compact but potentially complex sentences, built from the thousands of base ‘words’ in the Lonclass dictionary. By mapping the basic pieces, the words, to other data sources, we also enrich the compound sentences. We can’t map all of the sentences as there can be infinitely many of them – it would be an expensive and never-ending task.

For example, we might have a lonclass code for “Report on the environmental impact of the decline of tin mining in Sweden in the 20th century“. This would be an jumble of numbers and punctuation which I won’t trouble you with here. But if we parsed out that structure we can see the complex code as built from primitives such as ‘tin mining’ (itself e.g. ‘Tin’ and ‘Mining’), ‘Sweden’, etc. By linking those identifiable parts to shared Web data, we also learn more about the complex composite codes that use them. Wikipedia’s Sweden entry tells us in English, “Sweden has land borders with Norway to the west and Finland to the northeast, and water borders with Denmark, Germany, and Poland to the south, and Estonia, Latvia, Lithuania, and Russia to the east.”. Increasingly this additional information is available in machine-friendly form. Although right now we can’t learn about Sweden’s borders from the bits of Wikipedia reflected into DBpedia’s Sweden entry, but UN FAO’s geopolitical ontology does have this information and more in RDF form.

There is more, much more, to know about Sweden than can possibly be represented directly within Lonclass or UDC. Yet those facts may also be very useful for the retrieval of information tagged with Sweden-related Lonclass codes. If we map the Lonclass notion of ‘Sweden’ to identified concepts described elsewhere, then whenever we learn more about the latter, we also learn more about the former, and indirectly, about anything tagged with complex lonclass codes using that concept. Suddenly an archived TV documentary tagged as covering a ‘report on the environmental impact of the decline of tin mining in Sweden’ is accessible also to people or machines looking under Scandinavia + metal mining. Environmental matters, after all, often don’t respect geo-political borders; someone searching for coverage of environmental trends in a neighbouring country might well be happy to find this documentary. But should Lonclass or UDC maintain an index of which countries border which others? Surely not!

Lonclass and UDC codes have a rich hidden structure that is rarely exploited with modern tools. Lonclass by virtue of its UDC heritage, does a lot of work itself towards representing complex conceptual inter-relationships. It embodies a conceptual map of our world, with mysterious codes (well known in the library world) for topics such as ‘622 – mining’, but also specifics e.g. ‘622.3 Mining of specific minerals, ores, rocks’, and combinations (‘622.3:553.9 Extraction of carbonaceous minerals, hydrocarbons’). By joining a code for ‘mining a specific mineral…’ to a code for ‘553.9 Deposits of carbonaceous rocks. Hydrocarbon deposits’ we get a compound term. So Lonclass/UDC “knows” about the relationship between “Tin Mining” and “Mining”, “metals” etc., and quite likely between “Sweden” and “Scandinavia”. But it can’t know everything! Sooner or later, we have to say, “Sorry, it’s not reasonable to expect the classification system to model the entire world; that’s a bigger problem”.

Even within the closed, self-supporting universe of UDC/Lonclass, this compositional semantics system is a very powerful tool for describing obscure topics in terms  of well known simpler concepts. But it’s too much for any single organisation (whether the BBC, the UDC Consortium, or anyone) to maintain and extend such a system to cover all of modern life; from social, legal and business developments to new scientific innovations. The work needs to be shared, and RDF is currently our best bet on how to create such work sharing, meaning sharing, information-linking systems in the Web. The hierarchies in UDC and Lonclass don’t attempt to represent all of objective reality; they instead show paths through information.

If the metaphor of a ‘conceptual map’ holds up, then it’s clear that at some point it’s useful to have our maps made by different parties, with different specialised knowledge. The Web now contains a smaller but growing Web of machine readable descriptions. Over at MusicBrainz is a community who take care of describing the entities and relationships that cover much of music, or at least popular music. Others describe countries, species, genetics, languages, historical events, economics, and countless other topics. The data is sometimes messy or an imperfect fit for some task-in-hand, but it is actively growing, curated and connected.

I’m not arguing that Lonclass or UDC should be thrown out and replaced by some vague ‘linked cloud’. Rather, that there are some simple steps that can be taken towards making sure each of these linked datasets contribute to modernising our paths into the archives. We need to document and share opensource tools for an agreed data model for the arcane numeric codes of UDC and Lonclass. We need at least the raw pieces, the simplest codes, to be described for humans and machines in public, stable Web pages, and for their re-use, mapping, data mining and re-combination to be actively encouraged and celebrated. Currently, it is possible to get your hands on this data if you work with the BBC (Lonclass), pay license fees (UDC) or exchange USB sticks with the right party in some shady backstreet. Whether the metaphor of choice is ‘key to the archives’ or ‘conceptual map of…’, this is a deeply unfortunate situation, both for the intrinsic public value of these datasets, but also for the collections they index. There’s a wealth of meaning hidden inside Lonclass and UDC and the collections they index, a lot that can be added by linking it to other RDF datasets, but more importantly there are huge communities out there who’ll do much of the work when the data is finally opened up…

I wrote too much. What I meant to say is simple. Classification systems with compositional semantics can be enriched when we map their basic terms using identifiers from other shared data sets. And those in the UDC/Lonclass tradition, while in some ways they’re showing their age (weird numeric codes, huge monolithic, hard-to-maintain databases), … are also amongst the most interesting systems we have today for navigating information, especially when combined with Linked Data techniques and companion datasets.

Easier in RDFa: multiple types and the influence of syntax on semantics

RDF is defined as an abstract data model, plus a collection of practical notations for exchanging RDF descriptions (eg. RDF/XML, RDFa, Turtle/N3). In theory, your data modelling activities are conducted in splendid isolation from the sleazy details of each syntax. RDF vocabularies define classes of thing, and various types of property/relationship that link those things. And then instance data uses arbitrary combinations of those vocabularies to make claims about stuff. Nothing in your vocabulary design says anything about XML or text formats or HTML or other syntactic details.

All that said, syntactic considerations can mess with your modelling. I’ve just written this up for the Linked Library Data group, but since the point isn’t often made, I thought I’d do so here too.

RDF instance data, ie. descriptions of stuff, is peculiar in that it lets you use multiple independent schemas at the same time. So I might use SKOS, FOAF, Bio, Dublin Core and DOAP all jumbled up together in one document. But there are some considerations when you want to mention that something is in multiple classes. While you can do this in any RDF notation, it is rather ugly in RDF/XML, historically RDF’s most official, standard notation. Furthermore, if you want to mention that two things are related by two or more specified properties, this can be super ugly in RDF/XML. Or at least rather verbose. These practical facts have tended to guide the descriptive idioms used in real world RDF data. RDFa changes the landscape significantly, so let me give some examples.

Backstory – decentralised extensibility

RDF classes from one vocabulary can be linked to more general or specific classes in another; we use rdfs:subClassOf for this. Similarly, RDF properties can be linked with rdfs:subPropertyOf claims. So for example in FOAF we might define a class foaf:Organization, and leave it at that. Meanwhile over in the Org vocabulary, they care enough to distinguish a subclass, org:FormalOrganization. This is great! Incremental, decentralised extensibility. Similarly, FOAF has foaf:knows as a basic link between people who know each other, but over in the relationship vocabulary, that has been specialized, and we see relationships like ‘livesWith‘, ‘collaboratesWith‘. These carry more specific meaning, but they also imply a foaf:knows link too.

This kind of machine-readable (RDFS/OWL) documentation of the patterns of meaning amongst properties (and classes) has many uses. It could be used to infer missing information: if Ian writes RDF saying “Alice collaboratesWith Bob” but doesn’t explicitly say that Alice also knows Bob, a schema-aware processor can add this in. Or it can be used at query time, if someone asks “who does Alice know?”. But using this information is not mandatory, and this creates a problem for publishers. Should they publish redundant information to make it easier for simple data consumers to understand the data without knowing about the more detailed (and often more recent) vocabulary used?

Historically, adding redundant triples to capture the more general claims has been rather expensive – both in terms of markup beauty, and also file size. RDFa changes this.

Here’s a simple RDF/XML description of something.

<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
xmlns:foaf="http://xmlns.com/foaf/0.1/">
<foaf:Person rdf:about="#fred">
 <foaf:name>Fred Flintstone</foaf:name>
</foaf:Person>
</rdf:RDF>

…and here is how it would have to look if we wanted to add a 2nd type:

<foaf:Person rdf:about="#fred"
 rdf:type="http://example.com/vocab2#BiblioPerson">
  <foaf:name>Fred Flintstone</foaf:name>
</foaf:Person>
</rdf:RDF>

To add a 3rd or 4th type, we’d need to add in extra subelements eg.

<rdf:type rdf:resource="http://example.com/vocab2#BiblioPerson"/>

Note that the full URI for the vocabulary needs to be used at every occurence of the type.  Here’s the same thing, with multiple types, in RDFa.

<html>
<head><title>a page about Fred</title></head>
<body>
<div xmlns:foaf="http://xmlns.com/foaf/0.1/"
xmlns:vocab2="http://example.com/vocab2#"
 about="#fred" typeof="foaf:Person vocab2:BiblioPerson" >
<span property="foaf:name">Fred Flintstone</span>
</div>
</body>
</html>

RDFa 1.0 requires the second vocabulary’s namespace to be declared, but after that it is pretty concise if you want to throw in a 2nd or a 3rd type, for whatever you’re describing. If you’re talking about a relationship between people, instead of ” rel=’foaf:knows’ ” you could put “rel=’foaf:knows rel:livesWith’ “; if you wanted to mention that something was in the class not just of organizations, but formal organizations, you could write “typeof=’foaf:Organization org:FormalOrganization'”.

Properties and classes serve quite different social roles in RDF. The classes tend towards being dull, boring, because they are the point of connection between different datasets and applications. The detail, personality and real information content in RDF lives in the properties. But both classes and properties fall into specialisation hierarchies that cross independent vocabularies. It is quite a common experience to feel stuck, not sure whether to use a widely known but vague term, or a more precise but ‘niche’, new or specialised vocabulary. As RDF syntaxes improve, this tension can melt away somewhat. In RDFa it is significantly easier to simply publish both, allowing smart clients to understand your full detail, and simple clients to find the patterns they expect without having to do schema-based processing.

Subject classification and Statistics

Subject classification and statistics share some common problems. This post takes a small example discussed at this week’s ODaF event on “Semantic Statistics” in Tilberg, and explores its expression coded in the Universal Decimal Classification (UDC). UDC supports faceted description, providing an abstract grammar allowing sentence-like subject descriptions to be composed from the “raw materials” defined in its vocabulary scheme.

This makes the mapping of UDC (and to some extent also Dewey classifications)  into W3C’s SKOS somewhat lossy, since patterns and conventions for documenting these complex, composed structures are not yet well established. In the NoTube project we are looking into this in a TV context, in large part because the BBC archives make extensive use of UDC via their Lonclass scheme; see my ‘investigating Lonclass‘ and UDC seminar talk for more on those scenarios. Until this week I hadn’t thought enough about the potential for using this to link deep into statistical datasets.

One of the examples discussed on Tuesday was as follows (via Richard Cyganiak):

“There were 66 fatal occupational injuries in the Washington, DC metropolitan area in 2008″

There was much interesting discussion in Tilburg about the proper scope and role of Linked Data techniques for sharing this kind of statistical data. Do we use RDF essentially as metadata, to find ‘black boxes’ full of stats, or do we use RDF to try to capture something of what the statistics are telling us about the world? When do we use RDF as simple factual data directly about the world (eg. school X has N pupils [currently; or at time t]), and when does it become a carrier for raw numeric data whose meaning is not so directly expressed at the factual level?

The state of the art in applying RDF here seems to be SDMX-RDF, see Richard’s slides. The SDMX-RDF work uses SKOS to capture code lists, to describe cross-domain concepts and to indicate subject matter.

Given all this, I thought it would be worth taking this tiny example and looking at how it might look in UDC, both as an example of the ‘compositional semantics’ some of us hope to capture in extended SKOS descriptions, but also to explore scenarios that cross-link numeric data with the bibliographic materials that can be found via library classification techniques such as UDC. So I asked the ever-helpful Aida Slavic (editor in chief of the UDC), who talked me through how this example data item looks from a UDC perspective.

I asked,

So I’ve just got home from a meeting on semweb/stats. These folk encode data values with stuff like “There were 66 fatal occupational injuries in the Washington, DC metropolitan area in 2008″. How much of that could have a UDC coding? I guess I should ask, how would subject index a book whose main topic was “occupational injuries in the Washington DC metro area in 2008″?

Aida’s reply (posted with permission):

You can present all of it & much more using UDC. When you encode a subject like this in UDC you store much more information than your proposed sentence actually contains. So my decision of how to ‘translate this into udc’ would depend on learning more about the actual text and the context of the message it conveys, implied audience/purpose, the field of expertise for which the information in the document may be relevant etc. I would probably wonder whether this is a research report, study, news article, textbook, radio broadcast?

Not knowing more then you said I can play with the following: 331.46(735.215.2/.4)”2008

Accidents at work — Washington metropolitan area — year 2008
or a bit more detailed:  331.46-053.18(735.215.2/.4)”2008
Accidents at work — dead persons – Washington metropolitan area — year 2008
[you can say the number of dead persons but this is not pertinent from point of view of indexing and retrieval]

…or maybe (depending what is in the content and what is the main message of the text) and because you used the expression ‘fatal injuries’ this may imply that this is more health and safety/ prevention area in health hygiene which is in medicine.

The UDC structures composed here are:

TIME “2008”

PLACE (735.215.2/.4)  Counties in the Washington metropolitan area

TOPIC 1
331     Labour. Employment. Work. Labour economics. Organization of  labour
331.4     Working environment. Workplace design. Occupational safety.  Hygiene at work. Accidents at work
331.46  Accidents at work ==> 614.8

TOPIC 2
614   Prophylaxis. Public health measures. Preventive treatment
614.8    Accidents. Risks. Hazards. Accident prevention. Persona protection. Safety
614.8.069    Fatal accidents

NB – classification provides a bit more context and is more precise than words when it comes to presenting content i.e. if the content is focused on health and safety regulation and occupation health then the choice of numbers and their order would be different e.g. 614.8.069:331.46-053.18 [relationship between] health & safety policies in prevention of fatal injuries and accidents at work.

So when you read  UDC number 331.46 you do not see only e.g. ‘accidents at work’ but  ==>  ‘accidents at work < occupational health/safety < labour economics, labour organization < economy
and when you see UDC number 614.8  it is not only fatal accidents but rather ==> ‘fatal accidents < accident prevention, safety, hazards < Public health and hygiene. Accident prevention

When you see (735.2….) you do not only see Washington but also United States, North America

So why is this interesting? A couple of reasons…

1. Each of these complex codes combines several different hierarchically organized components; just as they can be used to explore bibliographic materials, similar approaches might be of value for navigating the growing collections of public statistical data. If SKOS is to be extended / improved to better support subject classification structures, we should take care also to consider use cases from the world of statistics and numeric data sharing.

2. Multilingual aspects. There are plans to expose SKOS data for the upper levels of UDC. An HTML interface to this “UDC summary” is already available online, and includes collected translations of textual labels in many languages (see progress report) . For example, we can look up 331.4 and find (in hierarchical context) definitions in English (“Working environment. Workplace design. Occupational safety. Hygiene at work. Accidents at work”), alongside e.g. Spanish (“Entorno del trabajo. Diseño del lugar de trabajo. Seguridad laboral. Higiene laboral. Accidentes de trabajo”), CroatianArmenian, …

Linked Data is about sharing work; if someone else has gone to the trouble of making such translations, it is probably worth exploring ways of re-using them. Numeric data is (in theory) linguistically neutral; this should make linking to translations particularly attractive. Much of the work around RDF and stats is about providing sufficient context to the raw values to help us understand what is really meant by “66” in some particular dataset. By exploiting SDMX-RDF’s use of SKOS, it should be possible to go further and to link out to the wider literature on workplace fatalities. This kind of topical linking should work in both directions: exploring out from numeric data to related research, debate and findings, but also coming in and finding relevant datasets that are cross-referenced from books, articles and working papers. W3C recently launched a Library Linked Data group, I look forward to learning more about how libraries are thinking about connecting numeric and non-numeric information.

RDFa in Drupal 7: last call for feedback before alpha release

Stéphane has just posted a call for feedback on the Drupal 7 RDFa design, before the first official alpha release.

First reaction above all, is that this is great news! Very happy to see this work maturing.

I’ve tried to quickly suggest some tweaks to the vocab, by hacking his diagram in photoshop. All it really shows is that I’ve forgotten how to use photoshop, but I’ll upload it here anyway.

So if you click through to the full image, you can see my rough edits.

I’d suggest:

  1. Use (dcterms) dc:subject as the way of pointing from a document to it’s SKOS subject.
  2. Use (dcterms) dc:creator as the relationship between a document and the person that created it (note that in FOAF, we now declare foaf:maker to map as an equivalentProperty to (dcterms)dc:creator).
  3. Distinguish between the description of the person versus their account in the Drupal system; I would use foaf:Person for the human, and sioc:User (a kind of foaf:OnlineAccount) as the drupal account. The foaf property to link from the former to the latter is foaf:account (new name for foaf:holdsAccount).
  4. Focus on SIOC where it is most at-home: in modelling the structure of the discussion; threading, comments and dialog.
  5. Provide a generated URI for the person. I don’t 100% understand Stephane’s comment, “Hash URIs for identifying things different from the page describing them can be implemented quite easily but this case hasn’t emerged in core” but perhaps this will be difficult? I’d suggest using URIs ending “userpage#!person” so the fragment IDs can’t clash with HTML usage.

If the core release can provide this basic structure, including a hook for describing the human person rather than the site-specific account (ie. sioc:User) then extensions should be able to add their own richness. The current markup doesn’t quite work for that end, as the human user is only described indirectly (unless  I understand current reading of sioc:User).

Anyway, I’m nitpicking! This is really great, and a nice and well-deserved boost for the RDFa community.

WOT in RDFa?

(This post is written in RDFa…)

To the best of my knowledge, Ludovic Hirlimann‘s PGP fingerprint is 6EFBD26FC7A212B2E093 B9E868F358F6C139647C. You might also be interested in his photos on flickr, or his workplace, Mozilla Messaging. The GPG key details were checked over a Skype video call with me, Ludo and Kaare A. Larsen.

This blog post isn’t signed, the URIs it referenced don’t use SSL, and the image could be switched by evildoers at any time! But the question’s worth asking: is this kind of scruffy key info useful, if there’s enough of it? If I wrote it somehow in Thunderbird’s editor instead, would it be easier to sign? Will 99.9% of humans ever know enough of what’s going on to understand what signing a bunch of complex markup means?

For earlier discussion of this kind of thing, see Joseph Reagle’s Key-free Trust piece (“Does Google Show How the Semantic Web Could Replace Public Key Infrastructure?”). It’s more PKI-free trust than PK-free.