20 July 2014

When did linking begin?

Pointing somewhere with a dereferenceable address, in the twelfth (or maybe fifth) century.

University of Bologna woodcut

As I have once before, I'm republishing an entry from an O'Reilly blog I had from 2003 to 2005 on topics related to linking. I've been reading up on early concepts of metadata lately—I particularly recommend Ann Blair's Too Much to Know: Managing Scholarly Information before the Modern Age—and have recently found another interesting reference to the "Regulae Iuris" book mentioned below. When I wrote this, I was more interested in hypertext issues, and if I was going to change anything to update this piece, I would change the word "traverse" to "dereference," but all the points are still meaningful.

Works about linking often claim that it's been around for thousands of years, and then they give examples that are no more than a few centuries old. I can only find one reference to something more than a thousand years old that qualifies as a link: Peter Stein's 1966 work "Regulae Iuris: from Juristic Rules to Legal Maxims" describes some late fifth-century lecture notes on a commentary by the legal scholar Ulpian. The notes mention that confirmation of a particular point can be found in the Regulae ("Rules") of the third-century Roman jurist (and student of Ulpian) Modestinus, "seventeen regulae from the end, in the regula beginning 'Dotis'...". The citation's explicit identification of the point in the cited work where the material could be found makes it the earliest link that I know of.

Other than Stein's tantalizing example, all of my research points to the 12th century as the beginning of linking. In a 1938 work on the medieval scholars of Bologna, Italy, who studied what remained of ancient Roman law, Hermann Kantorowicz wrote that in "the eleventh century...titles of law books are cited without indicating the passage, books of the Code are numbered, and the name of the law book is considered a sufficient reference." He uses this to build his argument that that a particular work described in his essay is from the eleventh century and not the twelfth, as other scholars had argued. Apparently, it was common knowledge in Kantoriwicz's field that twelfth century Bolognese scholars would reference a written law using the name of the law book, the rubric heading, and the first few words of the law itself. (Referencing of particular chapters and sections by their first few words was common at the time; the use of chapter, section, and page numbers didn't begin until the following century.)

Italian legal scholars trying to organize and make sense of the massive amounts of accumulated Roman law contributed a great deal to the mechanics of the cross-referencing that provide many of the earliest examples of linking. The medievalist husband and wife team Richard and Mary Rouse also found some in their research into evolving scholarship techniques in the great universities of England and France (that is, Oxford, Cambridge, and the Sorbonne) and they described Gilbert of Poitiers's innovative twelfth-century mechanism for addressing specific parts of his work on the psalms: he added a selection of Greek letters and other symbols down the side of each page to identify concepts such as the Penitential Psalms or the Passion and Resurrection. If you found the symbol for the Passion and Resurrection in the margin of Psalm 2 with a little 8 next to it (actually, a little "viii"—they weren't using Arabic numerals quite yet), it would tell you that the next discussion of this concept appeared in Psalm 8. Once you found the same symbol on one of the eighth psalm's pages, you might find a little "xii" with it to show that the next discussion of the same concept was in Psalm 12. This addressing system made it possible for someone preparing a sermon on the Passion and Resurrection to easily find the relevant material in the Psalms. (In fact, aids to sermon preparation was one of the main forces in the development of new research tools, as clergymen were encouraged to go out and compete with the burgeoning heretic movements for the hearts and minds of the people.)

The use of information addressing systems really got rolling in the thirteenth-century English and French universities, as scholarly monks developed concordances, subject indexes, and page numbers for both Christian religious works and the classic ancient Greek works that they learned about from their contact with the Arabic world. In fact, this is where Arabic numbers start to appear in Europe; page numbering was one of the early drivers for its adoption.

Quoting of one work by another was certainly around long before the twelfth century, but if an author doesn't identify an address for his source, his reference can't be traversed, so it's not really a link. Before the twelfth century, religious works had a long tradition of quoting and discussing other works, but in many traditions (for example, Islam, Theravada Buddhism, and Vedic Hinduism) memorization of complete religious works was so common that telling someone where to look within a work was unnecessary. If one Muslim scholar said to another "In the words of the Prophet..." he didn't need to name the sura of the Qur'an that the quoted words came from; he could assume that his listener already knew. Describing such allusions as "links" adds heft to claims that linking is thousands of years old, but a link that doesn't provide an address for its destination can't be traversed, and a link that can't be traversed isn't much of a link. And, such claims diminish the tremendous achievements of the 12th-century scholars who developed new techniques to navigate the accumulating amounts of recorded information they were studying.


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10 June 2014

Integrating hiphop vocabulary scores with other relevant data—then querying it

With a little JSON + DBpedia integration.

rapper vocabularies chart

About a month ago, media outlets ranging from NPR to Rolling Stone to Britain's Daily Mail reported on how a "designer, coder, and data scientist" named Matt Daniels had analyzed the number of unique words in samples of work by Shakespeare, Herman Melville, and 85 rappers. He then published a chart and article about how their scores related to each other. The highest score went to Aesop Rock, who I thought I'd heard of but hadn't—I was confusing him with A$AP Rocky, who was not included in the survey.

The chart and discussion were interesting, but what I really wanted to see was the complete list of subjects with their scores, and after searching around the web a bit I found that it was under my nose the whole time—the chart is dynamically generated from JSON embedded in his web page. So, I converted that JSON to RDF, used some SPARQL to retrieve additional data about each rapper from DBpedia such as their record labels, the years their careers began, any subject keywords assigned to them, and the abstracts, or summaries of their careers. (You'll find more details on the procedure for doing this below; the resulting integrated data is available for you to query here as a Turtle file.) Combining this additional data with the vocabulary scores let me do some interesting queries and provide an excellent example of how RDF and SPARQL let you perform ad hoc data integration to combine different data sets into aggregates that let you identify new patterns and other information.

For example, of all record labels with more than four rappers associated with them, I found that MCA's roster had the highest average vocabulary score at 5472.5, well above the overall average of 4624. Who are these artists? Another simple query showed their names and scores:

GZA 6426
The Roots 5803
Killah Priest 5737
Blackalicious 5480
Big Daddy Kane 4768
Rakim 4621

(As Daniels pointed out, members of the Wu-Tang Clan tend to have higher scores, so GZA and Killah Priest are a big help to MCA's average score.)

The dcterms:subject values assigned to the rappers in DBpedia provide the most interesting opportunities for exploration. In fact, it turned out that I didn't even need to pull down the record label values, because they each have corresponding dcterms:subject values. For example, each of the artists listed above have a dcterms:subject value of http://dbpedia.org/resource/Category:MCA_Records_artists along with their other dcterms:subject values.

Of the subject categories with more than four rappers, here are several interesting ones with high average scores, ranked by number of members in the category:

countavg score
Members of the Nation of Gods and Earths 13 5117
Underground rappers 8 5849
People from Brooklyn 7 5323
MCA Records artists 7 5401
Rappers from Long Island 6 5160
Alternative hip hop groups 5 5286
Wu-Tang Clan members 5 5611

I hadn't heard of the Nation of Gods and Earths, also known as the Five-Percent Nation; again, we have Wu-Tang skewing the numbers up. After I saw the high averages for "People from Brooklyn" and "Rappers from Long Island" but no mention of Staten Island, I clicked around and found out that only about half of Wu-Tang came from the borough in which they were based, which I never knew before.

Here are some interesting low scoring categories. Again, remember that the overall average score is 4624:

countavg score
Participants in American reality television series8 4108
People convicted of drug offenses 7 3741
American philanthropists 6 4022
American shooting survivors 5 4025
American fashion businesspeople 5 4110

Of course, the data collection itself isn't very scientific; what constitutes an "alternative" rapper? A less successful artist popular with music nerds? "People convicted of drug offenses" seems like a more cut and dried category, but remember that data from a Wikipedia page is not an authoritative source for such facts.

As with the list of MCA artists above, a simple query of the data can tell you who falls in each of these categories, so pull down the data from the link above and have fun querying it. If you're interested in how I did the integration, read on.

Integrating the data

Upon seeing that Daniels includes a score for Ghostface Killah, it's easy to ask DBpedia for all the { <http://dbpedia.org/resource/Ghostface_Killah> ?p ?o } triples. It's not as simple for many other artists, though, for several reasons:

  • Some rappers use stage names that are common phrases and words, so putting that name at the end of "http://dbpedia.org/resource/" won't necessarily get you data about them.

  • Tricky spellings and punctuation are pretty common in hiphop names. For example, Jay Z originally spelled his name with a hyphen but later dropped it, much as LexisNexis did twelve years earlier.

  • Daniels sometimes included qualifications in names ("GZA (only solo albums)"), included or didn't include the word "The" that was in the DBpedia name ("Roots" vs. "The Roots") or just spelled their names wrong, such as omitting the final "t" from "Missy Elliott."

Dropping parenthesized qualifications was easy enough. Even better, DBpedia often has the data necessary to find the page based on a slightly wrong name, and the techniques I described in Normalizing company names with SPARQL and DBpedia worked for most of them. This is not a minor point: even when the names aren't quite right, sending the right SPARQL queries to DBpedia can still retrieve valuable data about them. This has applications in all kinds of domains.

You can find the scripts and queries mentioned below in rapperrdf.zip. The rapperdata.js file is taken directly from the source of Daniels' web page, and loads his data into an array. Another JavaScript file, rappervocab.js, loads rapperdata.js and outputs Turtle RDF of the rapper's scores and the Daniels versions of their names. (If you're using the TopBraid platform and working with JSON, there's an excellent SPARQLMotion module to automate the conversion of any JSON to RDF.) I used Rhino to run the JavaScript, as I described in Javascript from the command line.

Another short script called rapperValuesList.js reads the same data and creates the list of names that I inserted as a VALUES list into the retrieveRapperData.rq SPARQL query that actually retrieves the relevant data from DBpedia. (VALUES is a great SPARQL technique for saying "I need data about this list of specific things," as I've written here before.) This SPARQL query uses the SERVICE keyword to send the request off to DBpedia and does a CONSTRUCT to save the triples. It uses the "Normalizing company names" trick mentioned above to see if the Daniels name with the parenthesized part stripped out is either the "official" rdfs:label value for a resource or otherwise attached to something that gets redirected to that.

Of the 81 artists in Daniels' list, there were 12 whose names couldn't be looked up even with the redirect trick in retrieveRapperData.rq. To account for these, I created extraRapperDanielsNames.ttl with a text editor to link Daniels' names for these 12 extra rappers to their DBpedia resource URIs such as http://dbpedia.org/resource/Common_(entertainer), which I had to look up manually. The retrieveExtraRapperData.rq query then uses that to retrieve the same data about those 12.

The queries only retrieve the start year, record label, abstract, and subjects about the artists because they all had those values. Retrieving data that only some of them have (such as the birth year, which you don't have for bands like The Roots) would mean using the OPTIONAL keyword, and DBpedia said that my query would take too long when I tried that—I'm sure the big VALUES part has a lot to do with that.

The integrateRapperData.rq query reads the extraRapperDanielsNames.ttl data and the data created by rappervocab.js, retrieveRapperData.rq, and retrieveExtraRapperData.rq, and then creates the final product: rapperDataIntegrated.ttl.

Querying the data

Next was the fun part: executing queries to explore that integrated data. The zip file includes queries to find the following information from rapperDataIntegrated.ttl:

averageScore.rqoverall average Daniels score
averageScoreByLabel.rq average score by record label for labels with more than four artists associated with them
subjectReport.rqaverage score by subject associated with the rappers for all subjects (like "Underground rappers" and "American philanthropists")
MCAArtists.rqMCA artists
JamaicanDescent.rqthe name, Daniels score, and abstract of "American rappers of Jamaican descent"

That last one can provide a template for the creation of other queries about who falls into which subject categories.

Linking this data with other data about the artists from some of the blue parts of the Linked Data Cloud such as DBTune or the BBC would provide some even more interesting possibilities. As one taste, this link has a SPARQL query that retrieves all the MusicBrainz data about Missy Elliott.


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9 May 2014

"Experience in SPARQL a plus"

The long tail story of SPARQL success: appearances in job postings.

logos of companies hiring SPARQL talent

When people talk about semantic web or linked data success stories, they usually talk about the big, well-known projects such as those at BestBuy, the BBC, NASA, life sciences companies, the whole vocabulary and taxonomy management industry, and the growing use of DBpedia by a range of companies. I've always found that a company's job postings provide interesting clues about their potential technology directions, and the increasing references to SPARQL in these postings is another positive trend. These fly further under most radars than the projects mentioned earlier, but their volume adds up to a real long tail, in the Chris Anderson sense of the word.

For a while now, I've had a saved search for appearances of "SPARQL" on the job posting site indeed.com so that I could occasionally mention companies looking for SPARQL experience in the Twitter feed for my book Learning SPARQL. I've mostly limited it to high-profile, brand name companies because there are really too many to mention them all; last Saturday's email from indeed.com listed six positions ranging from Xerox (who has two positions open) to Axius Technologies in Hoboken, who I've never heard of.

I thought it would be fun to review the names of the companies I've tweeted about and make a list of the most well-known ones, and as you can see below, it's an impressive list. Sometimes these companies bury the mention of SPARQL deep down in their descriptions of duties for the Java developer or "solution architect" that they seek, but others, like Xerox, say right in the job title that they want a Database-SPARQL Developer.

What does this mean? It means that the use of RDF and SPARQL is really getting traction at the grass roots level, as large and small companies move beyond side projects for investigating the technology to projects that require RDF and SPARQL enough to influence their hiring budgets. That's some nice progress.

AccentureMorgenthaler Life Science
AmazonNBC Entertainment
AstraZenecaNokia
Bank of America Northrop Grumman
BoeingOrbis
Boston Public LibraryPearson
Children's Hospital of Los AngelesPitney Bowes
Columbia University's Lamont-
Doherty Earth Observatory
Reed Elsevier
ComcastSAIC
Craig Venter InstituteSAP
DeloitteSears
ElsevierSiemens
Ely LillySocrata
Goldman SachsSony
GoogleStanford University
Harvard Medical SchoolThomson Reuters
IBM Global Business ServicesTurner Broadcasting
JP Morgan ChaseVodafone
Lockheed MartinXerox
Los Alamos National LaboratoryYahoo
Mayo ClinicYale University library
MicrosoftZoominfo

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